
==== Front
BMC Cancer
BMC Cancer
BMC Cancer
1471-2407
BioMed Central London

39294623
12934
10.1186/s12885-024-12934-y
Research
Values of apparent diffusion coefficient in pancreatic cancer patients receiving neoadjuvant therapy
Chen Yufei 1
Ma Chao mengqihi@gmail.com

12
Yang Panpan 2
Mao Kuanzheng 23
Gao Yisha 4
Chen Luguang 2
Wang Zhen 2
Bian Yun 2
Shao Chengwei 2
Lu Jianping 2
1 https://ror.org/03rc6as71 grid.24516.34 0000 0001 2370 4535 College of Electronic and Information Engineering, Tongji University, Shanghai, China
2 https://ror.org/02bjs0p66 grid.411525.6 0000 0004 0369 1599 Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Changhai Road 168, Shanghai, 200434 China
3 https://ror.org/00ay9v204 grid.267139.8 0000 0000 9188 055X School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
4 https://ror.org/02bjs0p66 grid.411525.6 0000 0004 0369 1599 Department of Pathology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, China
18 9 2024
18 9 2024
2024
24 116024 1 2024
11 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

To investigate the values of apparent diffusion coefficient (ADC) for the treatment response evaluation in pancreatic cancer (PC) patients receiving neoadjuvant therapy (NAT).

Methods

This study included 103 NAT patients with histologically proven PC. ADC maps were generated using monoexponential diffusion-weighted imaging (b values: 50, 800 s/mm2). Tumors’ minimum, maximum, and mean ADCs were measured and compared pre- and post-NAT. Variations in ADC values measured between pre- and post-NAT completion for NAT methods (chemotherapy, chemoradiotherapy), tumor locations (head/neck, body/tail), tumor regression grade (TRG) levels (0–2, 3), N stages (N0, N1/N2) and tumor resection margin status (R0, R1), were further analyzed.

Results

The minimum, maximum, and mean ADC values all increased dramatically after NAT, rising from 23.4 to 25.4% (all p < 0.001): mean (average: 1.626 × 10− 3 mm2/s vs. 1.315 × 10− 3 mm2/s), minimum (median: 1.274 × 10− 3 mm2/s vs. 1.034 × 10− 3 mm2/s), and maximum (average: 1.981 × 10− 3 mm2/s vs. 1.580 × 10− 3 mm2/s). The ADCs between the subgroups of all the criteria under investigation did not differ significantly for the minimum, maximum, or mean values pre- or post-NAT (P = 0.08 to 1.00). In the patients with borderline resectable PC (n = 47), the rate of tumor size changes after NAT was correlated with the pre-NAT mean ADC values (Spearman’s coefficient: 0.288, P = 0.049).

Conclusions

The ADC values of PC increased significantly following NAT; however, the percentage increases failed to provide any predictive value for the resection margin status or TRG levels.

Keywords

Apparent diffusion coefficient
Pancreatic cancer
Neoadjuvant therapy
Diffusion weighted imaging
Prediction
National Natural Science Foundation of China82073307, 62173252 82073307, 62173252 Shanghai Science and Technology Innovation Action Plan Medical Innovation Research Project20Y11912500 234 Platform Discipline Consolidation Foundation Project of Changhai Hospital2020YPT001 issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
==== Body
pmcBackground

Pancreatic cancer (PC) is a highly lethal ailment that causes over 460,000 fatalities each year [1], with a 5-year survival rate ranging from 2 to 12% [2, 3]. As the annual mortality rate increases, PC-related fatalities may be the second leading cause of death in the United States by 2030 [4]. A significant proportion of patients are identified with distant metastasis or local tumor progression rather than timely surgical resection due to the absence of an efficacious screening method for PC and the asymptomatic or atypical symptoms observed during early phases of PC. Only 10–15% of patients meet the criteria to undergo curative resection [5]. Furthermore, the development of novel treatment strategies for PC is imperative, alongside its early detection.

A recently developed treatment strategy for PC [6–11], neoadjuvant therapy (NAT) decreases tumor stage, postoperative recurrence, and metastasis. This improves the number of patients who are able to undergo surgery for locally advanced and borderline resectable PC, as well as increases the percentage of tumors that are removed via R0 resection. Both CT and MRI yield comparable results for PC staging and diagnosis [12]. In clinical applications, quantitative MRI has emerged as an essential tool, not only aiding radiologists in their clinical diagnoses but also assisting in the elimination of diagnostic subjectivity and furnishing standardized, calibrated parameters to account for system variations [13]. Clinical practice frequently employs apparent diffusion coefficient (ADC) derived from diffusion-weighted imaging (DWI) [14, 15] techniques to quantify the diffusion of water molecules through tissues. Standard criteria for the collection and analysis of DWI have been suggested [16–20] in order to expand the potential of ADC to be utilized in a variety of organs. The characteristics of certain tissues, including cellularity, vascularity, and the Ki-67 index, have been identified to correlate with variations in the ADC values of pancreatic tumors [21–23]. ADC has been used to evaluate the effects of NAT on PC patients [24]. There is ongoing debate regarding the effects of ADC measurements in PC patients undergoing NAT therapy [25–30]. ADC values are substantially impacted by alterations in the tissue components of PC following NAT, including necrosis, edema, inflammation, fibrosis, and the elimination of tumor cells and adjacent tissue. In light of the limited sample size observed in previous investigations [25–30], it is necessary to clarify the values of ADC on PC patients with NAT, so as to augment the accuracy of quantitative DWI for assessing treatment response. The objective of this study was to assess the utility of ADC in predicting prognostic parameters such as R0 resectability or tumor regression grade (TRG) levels among patients with PC, and to evaluate the value of ADC for the treatment response evaluation of PC patients with NAT.

Methods

Patients

The retrospective was granted approval by the Biomedical Research Ethics Committee of our institution. Between April 2019 and December 2021, 1752 patients underwent contrast-enhanced MRI or CT and received pancreatic tumor surgery. A total of 125 patients (72 men, 53 women; mean age, 60.6 ± 8.5 years) who underwent surgical excision of malignancies and were given NAT. Among these patients, 109 patients underwent contrast-enhanced MRI examinations prior to NAT, and 115 individuals underwent contrast-enhanced MRI examinations after the completion of NAT. Finally, this study included 103 individuals who underwent MRIs in both the pre-NAT and post-NAT groups (Fig. 1). Out of the 103 cases, 39 patients underwent twice MRI examinations with the same scanner, one before and one after the NAT was concluded.

Fig. 1 Flow chart of the patient selection process

MRI

All patients underwent MRI examinations utilizing pancreatic protocols on 1.5-T or 3-T MRI systems (Signa HDxt and Discovery MR750, GE Healthcare, Milwaukee, USA; MAGNETOM Skyra and Avanto, Siemens, Erlangen, Germany), which include T1-weighted imaging (T1WI) with two echoes and water-fat Dixon reconstruction, contrast-enhanced T1WI (contrast agent, 0.1–0.15 mmol/kg; rate, 2.0–3.0 ml/s), DWI and magnetic resonance cholangiopancreatography (MRCP). DWI employed a single-shot free-breathing echo-planar-imaging sequence with three orthogonal diffusion gradients. Table 1 provides a summary of the DWI parameters utilized by MRI scanners at present. Tumor diameters were measured using the response evaluation criteria in solid tumors (RECIST) 1.1 criteria in conjunction with MRI.

Table 1 The diffusion weighted imaging parameters for the MRI scanners used in this study

MRI Scanners	Field strength (tesla)	Time of repetition (ms)	Time of echo (ms)	Field of view (mm2)	b values (s/mm2)	Matrix	Slice/gap Thickness (mm)	Number of slices	
GE Signa HDxt	3	3000	59.2	380*304	0, 50, 800	128 × 128	6/1	26	
GE Discovery MR750	3	3329	76.4	360*288	50, 800	128 × 128	6/1	30	
Siemens MAGNETOM Skyra	3	4800	48	380*308	50, 800	128 × 104	6/1.2	26	
Siemens MAGNETOM Avanto	1.5	4000	75	380*285	50, 800	128 × 96	5/1.5	25	
59, 91, 31 and 25 examinations were performed by each MRI scanners, respectively

ADC measurements

The ADC maps were generated by utilizing a monoexponential model DWI (b-values: 50 and 800 s/mm2). By delineating one elliptical or circular region of interest (ROI) on the solid portion of the tumor for each patient (Figs. 2 and 3), with caution to exclude cystic lesions and dilated pancreatic duct by the collaboration of two measurers, who calculated the mean, minimum, and maximum ADC values for every patient both before and after NAT. A radiologist (12 years’ experience in abdominal MRI) and an MRI physicist (13 years’ experience in abdominal MRI) determined the ADCs utilizing Image J (v1.8.0; National Institutes of Health, Bethesda, USA) and RadiAnt DICOM viewer (v2021.1; Medixant, Poznan, Poland).

Fig. 2 Representative MRI images and apparent diffusion coefficient (ADC) measurements of pancreatic cancer with tumor regression grade 0 before and after neoadjuvant therapy (NAT). MRI scans taken pre- and post-NAT are shown in (a-h) and (i-p). Figures (a, i) and (b, j) are diffusion-weighted images with b value of 50 and 800 s/mm2, respectively; (c, k) ADC maps; (d, i) T2-weighted images; (e, m) Pre-contrast water images of the DIXON VIBE; (f, n) Arterial phase T1WI; (g, o) Venous-phase T1WI; (h, p) Delayed-phases T1-weighted images; (q) H&E, magnification×40 and (r) H&E, magnification ×200. On the ADC maps, a rounded or oval-shaped region of interest (ROI) is drawn within the solid tumor areas for ADC measurements. The tumor’s mean, minimum, and maximum ADC values can be determined via an ROI (Pre-NAT: ROI, 280.8 mm2; mean: 1.036 × 10− 3 mm2/s; minimum: 0.761 × 10− 3 mm2/s; maximum: 1.287 × 10− 3 mm2/s; Post-NAT: ROI, 64.5 mm2; mean: 2.009 × 10− 3 mm2/s; minimum: 1.758 × 10− 3 mm2/s; maximum: 2.319 × 10− 3 mm2/s). (Yellow ROI examples in figures c and k)

Fig. 3 Representative MRI images and apparent diffusion coefficient (ADC) measurements of pancreatic cancer with tumor regression grade 3 pre- and post-neoadjuvant therapy (NAT). MRI scans taken pre- and post-NAT are shown in (a-h) and (i-p). Figures (a, i) and (b, j) are diffusion-weighted images with b value of 50 and 800 s/mm2, respectively; (c, k) ADC maps; (d, i) T2-weighted images; (e, m) Pre-contrast water images of the DIXON VIBE; (f, n) Arterial phase T1WI; (g, o) Venous-phase T1WI; (h, p) Delayed-phases T1-weighted images; (q) H&E, magnification×40 and (r) H&E, magnification ×200. On the ADC maps, a rounded or oval-shaped region of interest (ROI) is drawn within the solid tumor areas for ADC measurements. The tumor’s mean, minimum, and maximum ADC values can be determined via an ROI (Pre-NAT: ROI, 581.7 mm2; mean: 1.371 × 10− 3 mm2/s; minimum: 0.894 × 10− 3 mm2/s; maximum: 1.905 × 10− 3 mm2/s; Post-NAT: ROI, 202.7 mm2; mean: 1.740 × 10− 3 mm2/s; minimum: 1.443 × 10− 3 mm2/s; maximum: 2.164 × 10− 3 mm2/s). (Yellow ROI examples in figures c and k)

Pathological analysis

The macroscopic description, specimen specifications, morphological description, margins and neighbors, lymph node metastasis, diagnosis, TRG, and other pertinent information were documented in the structured pathological reports for NAT of PC at our hospital. TRG is classified into four phases (Grades 0 to 3) by the College of American Pathologists (CAP) grading system, which is determined by the proportion of residual tumor cells to stroma. Grade 0 indicates complete response to PC with no surviving tumor cells, Grade 1 indicates the presence of a solitary or small number of focal residual tumor cells; Grade 2 indicates the presence of focal residual tumor cells accompanied by interstitial fibrosis; and Grade 3 indicates that the NAT was ineffective and a considerable number of tumor cells persisted. R0 and R1 were ascertained in the absence of tumor cell infiltration within a 1 mm margin of the resection. Prior to the issuance of final pathology reports, pathological images were analyzed by a minimum of two pathologists. The Ki-67 labeling index represents the percentage of nuclei positively stained among the whole tumor cells.

Statistical analysis

MedCalc (version 13.0.0.0, MedCalc Software, Ostend, Belgium) was utilized for statistical analysis. Normality assumptions of ADCs were assessed using the Kolmogorov-Smirnov test. Normal distribution data are expressed as mean ± standard deviation (range), and non-normally distributed data are expressed as median (range). Wilcoxon tests or T-tests were used to examine potential differences in the measured ADC values (mean, minimum, and maximum) between samples collected before and after NAT. NAT methods (chemotherapy, chemoradiation), tumor locations (head/neck, body/tail), TRG levels (0–2, 3), N stages (N0, N1/N2), and tumor resection margin status (R0, R1) were considered when comparing the mean, minimum, and maximum ADCs between the two groups before and after NAT using Mann-Whitney or independent samples T-tests. The gross, pre-NAT, and post-NAT MRI tumor sizes were compared using the Friedman test with post hoc analysis. The correction between the pre- and post-NAT ADCs, the rate of ADC changes post-NAT, and the rate of tumor size changes post-NAT were examined using Spearman correlation analyses. Furthermore, we also carried out correlation analyses to examine the correlations between ADC values and Ki-67 or the tumor grade. The threshold for statistical significance was established as P < 0.05.

Results

Patient characteristics

Table 2 provides a summary of the clinical characteristics of the 103 patients. The mean age of the 61 males and 42 females was 59.7 ± 8.6 years (range: 33–76). Of the total pancreatic lesions detected, 52.4% (54 of 103) were located in the head/neck area. Histological analyses identified lymph node metastases in 50% (51 of 102) of patients, excluding one individual who did not undergo peripancreatic lymphadenectomy. At the time of tumor resection, 74.8% (77 of 103) of the cases included in this study had R0 margin status. The median tumor size as determined by pre-NAT, preoperative MRI, and pathological measurements was 30.0 mm (range, 8.2 to 89 mm), 20.0 mm (range, 0 to 56.6 mm), and 28.0 mm (range, 5 to 75 mm), respectively. Among the three groups, PC had the smallest preoperative median MRI size. Ki-67 were recorded in 97 patients, with a median of 30% (range, 0 to 90%).

Table 2 Characteristics of the enrolled 103 patients with pancreatic cancer

Mean age ± SD, years (range)	59.7 ± 8.6 (33–76)	
Gender, n (%)		
 Male	61 (59.2%)	
 Female	42 (40.8%)	
Location in pancreas, n (%)		
 Head/neck	54 (52.4%)	
 Body/tail	49 (47.6%)	
Resectability of pancreatic cancer, n (%)		
 Resectable	43 (41.8%)	
 Borderline resectable	47 (45.6%)	
 Locally advanced	13 (12.6%)	
Histopathologic features of mass, n (%)		
 Ductal adenocarcinoma	93 (90.3%)	
 Adenosquamous carcinoma	9 (8.7%)	
 Undifferentiated carcinoma	1 (1.0%)	
Tumor resection margin status, n (%)		
 R0	77 (74.8%)	
 R1	26 (25.2%)	
Year of surgery	2019–2021	
N stage, n (%)		
 NX	1 (1.0%)	
 N0	51 (49.5%)	
 N1	40 (38.8%)	
 N2	11 (10.7%)	
Tumor regression grade, n (%)*		
 0	2 (1.9%)	
 1	9 (8.8%)	
 2	59 (57.3%)	
 3	33 (32.0%)	
SD: standard deviation

*Grading system of the College of American Pathologists

ADC measurements pre- and post-NAT

Subsequent to NAT, the mean ADC of tumors increased by 23.3%, the minimum ADC increased by 25.4%, and the maximum ADC increased by approximately 23.4% (Table 3). Pre- or post-NAT, the difference between the subgroups of all the investigated factors was not significant (P = 0.085 to 0.999) for the ADC measurements of PC on both the two groups in factors such as NAT methods, tumor locations, TRG levels, N stages, and resection margin status. In particular, pre- or post-NAT, the difference among the groups of TRG (G0/G1, G2, G3), was also not significant for the ADCmean, ADCmax and ADCmin with P values ranging from 0.070 to 0.719.

Table 3 Comparisons of ADC measurements of pancreatic cancer (n = 103) before and after neoadjuvant therapy (NAT)

ADC measurements	Pre-NAT	Post-NAT	
ROI (mm2)			
 Median (Interquartile range)	156.3 (115.0-233.3)	117.8 (88.8–150.0)	
 P value	< 0.001	
Minimum (×10− 3 mm2/s)			
 Median (Interquartile range)	1.034 (0.935–1.191)	1.274 (1.106–1.451)	
 P value	< 0.001	
Mean (×10− 3 mm2/s)			
 Average ± SD (Range)	1.315 ± 0.195 (0.903–1.917)	1.626 ± 0.291(1.054-2.400)	
 P value	< 0.001	
Maximum (×10− 3 mm2/s)			
 Average ± SD (Range)	1.580 ± 0.232 (1.092–2.387)	1.981 ± 0.384 (1.217–3.137)	
 P value	< 0.001	
ADC: apparent diffusion coefficient; SD: standard deviation

Further analysis revealed significant fluctuations in the ADC values, encompassing the minimum, mean, and maximum, when longitudinal comparisons of ADC measurements were conducted between the subgroups of all parameters examined before and after NAT (all P < 0.01). There was no correlation observed between the rate of change in tumor size and the pre-NAT ADC values or the rate of change in any of the three categories (mean, minimum, and maximum) following NAT completion (P = 0.237 to 0.967).

Furthermore, the analyses of ADC values before and after NAT treatment for the 39 patients who underwent MRI scans on the identical MRI scanners prior to and following NAT treatment yielded comparable outcomes to those observed in the 103 cases.

After NAT, the Ki-67 of patients with TRG levels of 0–2 (median, 20%; range, 0 to 90%) was significantly lower than that of patients with TRG level TRG 3 (median, 30%; range, 3–90%) with P value of 0.018. There was no correlation between ADC values (mean, minimum, and maximum) and Ki-67 (P = 0.102 to 0.789) or TRG levels (P = 0.090 to 0.973) pre- or post-NAT (Table 4).

Table 4 Correlations between ADC values and the Ki67 index, as well as tumor grade before and after neoadjuvant therapy (NAT)

	Parameters	Ki-67(%)	TRG levels	
Correlation coefficient r (95% CI)	P	Correlation coefficient r (95% CI)	P	
Pre-NAT	ADCmin	-0.027 (-0.226 to 0.173)	0.789	0.003 (-0.197 to 0.190)	0.973	
ADCmean	-0.055 (-0.252 to 0.146)	0.593	-0.029 (-0.221 to 0.165)	0.771	
ADCmax	-0.081 (-0.276 to 0.120)	0.429	-0.021 (-0.214 to 0.173)	0.831	
Post-NAT	ADCmin	-0.167 (-0.355 to 0.033)	0.102	0.133 (-0.063 to 0.318)	0.182	
ADCmean	-0.129 (-0.320 to 0.072)	0.208	0.168 (-0.026 to 0.350)	0.090	
ADCmax	-0.088 (-0.283 to 0.113)	0.390	0.106 (-0.089 to 0.294)	0.286	
CI: Confidence interval

ADC measurements of borderline resectable PC

In this study, 47 patients with PC that was borderline resectable participated. Following NAT, the mean, minimum, and maximum ADC values of tumors increased by roughly 21.8%, 22.7%, and 21.6%, respectively. Before or after NAT, there was no significant difference (P = 0.077 to 0.956) between the subgroups of all tested parameters for the ADC measurements of PC on TRG levels (0–2, 3) and resection margin status (R0, R1). There was a correlation between the rate of tumor size variations following NAT and the mean ADC values prior to NAT (Spearman’s coefficient: 0.288, P = 0.049).

Discussion

To reduce the variability of ADC data, this study applies the identical combination (50 and 800 s/mm2) for ADC calculation across to all MRI systems. A multi-b-value DWI study utilizing six models suggests that DWI can be employed to examine changes in PC both before and after NAT [31]. The performance of the quantitative parameter of ADC changes before and after NAT was superior to that of the parameter’s own repeatability. Following NAT, the minimum, maximum, and mean ADCs for PC patients increased significantly, as shown in the present study; however, there was no significant difference in the percentage increases for R0 resectability or TRG levels. The mean tumor ADC of the 103 patients prior to NAT was 1.31 × 10− 3 mm2/s, which was within the range of ADCs associated with PC [32]. The average ADC values of the tumors from 103 patients increased by about 25.4% to 1.626 × 10− 3 mm2/s subsequent to NAT. The elimination of PC cells subsequent to NAT may facilitate water molecule diffusion and contribute to the increased ADC values.

Prior investigations concerning the impact of ADC on the prediction of NAT’s effectiveness for PC have produced controversial findings. An association between the pathological response of the tumor and the baseline mean ADC value was identified by Cuneo et al. [24] through the analysis of DWI images from seven patients who had resectable PC prior to NAT. In contrast to the non-responding patient group, the responding patient group exhibited a higher pre-NAT ADC baseline value. Chan et al. discovered, using a sample of thirteen cases, that ADC increased in 54% of instances after NAT, and in 95% of those instances, it increased by more than 10% [25]. Using genetically modified rodents as models for PC NAT, Trajkovic-Arsic et al. [33] discovered that ADC can be used to predict the early NAT response of PC. The study also compared the mean ADC before and after NAT in six PC patients and discovered that the cases with the greatest decrease in tumor size had the greatest ADC increases after NAT. The study conducted by Zimmermann et al. [28] examined 25 patients and found that the average ADC values were 1.32 × 10− 3 mm2/s prior to NAT completion and 1.43 × 10− 3 mm2/s subsequent to NAT completion [28]. The distinction between the two categories was not readily apparent. Multiple studies [25, 27, 30, 34] indicate that the average ADC for PC is considerably greater after NAT than it was prior to NAT. The findings corroborate our assertions. Following NAT, there is a possibility of decreased tumor cell density and stromal reorganization, both of which could impact the ability of water molecules to diffuse across the tissue and facilitate the utilization of DWI to identify alterations in ADC values. Our findings suggest that ADC effectively captures the changes that occur in PC following NAT, despite the non-uniformity of the NAT techniques employed in the patients. Further research will be guided by these noteworthy findings.

Studies on a variety of tumors have shown that ADC values can be used to predict pathological features [35–37]. Ki-67 is an indicator of cell proliferation and could be used for the evaluation of tumors. In the liver metastases from both breast and colorectal cancers, a significant correlation was observed between ADC values and Ki-67 indices. Conversely, in the case of pancreatic cancer metastases, no substantial correlations were detected [38]. Ki-67 demonstrates a significant correlation with ADC values in primary central nervous system lymphoma [39]. In meningioma, the authors observed an inverse correlation between Ki-67 and both ADCmean and ADCmax, while no significant correlation was found with ADCmin [40]. Surov et al. noted that ADC values are not predictive of molecular subtypes or lymph node metastases in invasive breast cancer. However, they did find a weak correlation between ADC and the expression levels of Ki-67 [41, 42]. For the pancreatic tumors, a negative correlation between pathological features such as tumor cellularity and Ki-67 and ADC values of pancreatic neuroendocrine tumors [23, 43]. Xie et al. found the ADCs of PC was not significantly correlated with Ki-67 [22]. In the current study, we also found that the ADCs of PC before and after NAT were not correlated with Ki-67 and TRG levels. Consequently, ADC values are not suitable as a proliferation biomarker for PC.

Two critical clinical indicators of NAT are the TRG levels of PC and the state of the resection margin. In this study, we analyzed the variations in ADC among PC patients with NAT, stratified by resection margin status and TRG levels, including the mean, minimum, and maximum ADC values. Before or after NAT, there was no discernible difference in any of the examined parameters between the subgroups. Pre- and post-NAT ADC measurements were unable to predict tumor treatment response, including margin status and TRG levels of PC, according to our findings. kada et al. [26] identified the pre-NAT ADC value as a predictor of R0 curability in patients with borderline resectable PC in a retrospective study. However, the authors of another prospective study found that the post-treatment ADC value may serve as a predictor of R0 resectability in patients with borderline resectable PC [27]. Contradictory findings may be attributable to the varied case sources and tiny sample sizes utilized in the studies. The grading of TRGs is predominantly determined by the existence of interstitial fibrosis induced by treatment and damaged tumor cells. Pancreatic cancer cells are extensively distributed and proliferate haphazardly in this investigation. In general, tumor cells are distributed across an extensive array of proliferative stroma, with a particular concentration in the vicinity of the tumor. Interstitial fibrosis induced by NAT is comparable in appearance to fibroproliferative stroma linked to PC and fibrosis resulting from chronic pancreatitis. Consequently, the grading of TRG may be imprecise and subjective. Conversely, clinical pathology is subject to certain constraints on account of the utilization of minuscule tissue samples. Additional analysis of 47 patients with equivocal resectable PC in this study revealed no differences in pre-treatment or post-treatment ADC measurements or TRG levels (0–2, 3) between the R0 and R1 groups.

The preoperative MRI tumor sizes subsequent to NAT were notably reduced in comparison to the pre-NAT MRI tumor sizes and the pathological size of PC (both P < 0.001), with a median underestimation of tumor size was approximately 10 mm and 8 mm, respectively. In prior publications, we delineated various rationales for the incongruity between the pathology findings and the imaging sizes of the tumors [44–46]. The present study examined the correlation between the pre-NAT mean ADC values and the rate of variations in tumor size for the borderline resectable PC. Hence, the mean ADCs prior to NAT could potentially serve as a metric for assessing the effectiveness of NAT in the treatment of pancreatic cancer [30], although its value requires additional verification.

There were several limitations to this investigation. First, there were variations in the neoadjuvant techniques utilized in this study. For example, albumin-bound paclitaxel, gemcitabine, FOLFIRINOX, and mFOLFIRINOX have all been utilized in chemotherapy. Due to the imperfection of the post-NAT staging system and the evolution of NAT protocols and treatment recommendations in recent years, it is difficult to manage PC patients in clinical practice [47]. Second, although previous research has shown that employing multiple b values can lead to more precise ADC value measurements [48], in our clinical context of pancreatic MRI, ADC maps were constructed using DWI with only two b values in order to balance scanning time. Third, PC tumor cells are often distributed across a diverse array of stroma, particularly in the immediate vicinity of the tumor. It is challenging to describe the contour of the tumor, particularly after NAT, and imaging evaluation typically underestimates the number of tumor cells dispersed throughout the tumor [10]. In this investigation, the ADC values were therefore measured using the solid sample ROI method [49, 50]. Prior research has extensively investigated the impact of ROI approaches, including whole-volume, single-slice, and solid tumor samples, on the measurements of ADC values [49, 50]. However, we refrained from evaluating this particular influence.

Conclusions

Increasing percentage values did not predict the R0 resectability and TRG levels in PC patients, despite the fact that the minimum, maximum, and mean ADC values all increased substantially after NAT for the examinations done in the routine praxis.

Author contributions

C.Y., Y.P., M.K., G.Y., W.Z., L.C., M.C. and B.Y. performed the majority of experiments, made substantial contributions to the data analysis and interpretation, and wrote the manuscript draft; M.C., C.Y., S.C. and L.J. participated in the design of the study and made substantial contribution to data analysis; M.C. made substantial contributions to the study conception and design, critically revised the manuscript draft for important intellectual content, and gave final approval of the version to be published; all the authors read and approved the final manuscript.

Funding

This work was supported by the National Natural Science Foundation of China [82073307, 62173252]; 234 Platform Discipline Consolidation Foundation Project of Changhai Hospital [2020YPT001]; Shanghai Science and Technology Innovation Action Plan Medical Innovation Research Project [20Y11912500].

Data availability

The research dataset for the current study is available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee Changhai Hospital of Shanghai. Signed written informed consent was obtained from all participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Abbreviations

ADC Apparent diffusion coefficients

CAP College of American pathologists

CT Computed tomography

DICOM Digital imaging and communications in medicine

DWI Diffusion-weighted imaging

MRCP Magnetic resonance cholangiopancreatography

MRI Magnetic resonance imaging

NAT Neoadjuvant therapy

PC Pancreatic cancer

RECIST Response evaluation criteria in solid tumors

ROI Regions of interest

T1WI T1-weighted imaging

TRG Tumor regression grade

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Sung H Ferlay J Siegel RL Laversanne M Soerjomataram I Jemal A Global Cancer statistics 2020: GLOBOCAN estimates of incidence and Mortality Worldwide for 36 cancers in 185 countries CA Cancer J Clin 2021 71 3 209 49 10.3322/caac.21660 33538338
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.33538338
2. Siegel RL Miller KD Wagle NS Jemal A Cancer statistics, 2023 CA Cancer J Clin 2023 73 1 17 48 10.3322/caac.21763 36633525
Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17–48.36633525
3. McGuigan A Kelly P Turkington RC Jones C Coleman HG McCain RS Pancreatic cancer: a review of clinical diagnosis, epidemiology, treatment and outcomes World J Gastroenterol 2018 24 43 4846 61 10.3748/wjg.v24.i43.4846 30487695
McGuigan A, Kelly P, Turkington RC, Jones C, Coleman HG, McCain RS. Pancreatic cancer: a review of clinical diagnosis, epidemiology, treatment and outcomes. World J Gastroenterol. 2018;24(43):4846–61.30487695
4. Saad AM Turk T Al-Husseini MJ Abdel-Rahman O Trends in pancreatic adenocarcinoma incidence and mortality in the United States in the last four decades; a SEER-based study BMC Cancer 2018 18 1 688 10.1186/s12885-018-4610-4 29940910
Saad AM, Turk T, Al-Husseini MJ, Abdel-Rahman O. Trends in pancreatic adenocarcinoma incidence and mortality in the United States in the last four decades; a SEER-based study. BMC Cancer. 2018;18(1):688.29940910
5. Park W Chawla A O’Reilly EM Pancreat Cancer: Rev JAMA 2021 326 9 851 62
Park W, Chawla A, O’Reilly EM. Pancreat cancer: a review. JAMA. 2021;326(9):851–62.
6. Tempero MA Malafa MP Al-Hawary M Behrman SW Benson AB Cardin DB Pancreatic adenocarcinoma, Version 2.2021, NCCN clinical practice guidelines in oncology J Natl Compr Canc Netw 2021 19 4 439 57 10.6004/jnccn.2021.0017 33845462
Tempero MA, Malafa MP, Al-Hawary M, Behrman SW, Benson AB, Cardin DB, et al. Pancreatic adenocarcinoma, version 2.2021, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw. 2021;19(4):439–57.33845462
7. Kulkarni NM Soloff EV Tolat PP Sangster GP Fleming JB Brook OR White paper on pancreatic ductal adenocarcinoma from society of abdominal radiology’s disease-focused panel for pancreatic ductal adenocarcinoma: part I, AJCC staging system, NCCN guidelines, and borderline resectable disease Abdom Radiol (NY) 2020 45 3 716 28 10.1007/s00261-019-02289-5 31748823
Kulkarni NM, Soloff EV, Tolat PP, Sangster GP, Fleming JB, Brook OR, et al. White paper on pancreatic ductal adenocarcinoma from society of abdominal radiology’s disease-focused panel for pancreatic ductal adenocarcinoma: part I, AJCC staging system, NCCN guidelines, and borderline resectable disease. Abdom Radiol (NY). 2020;45(3):716–28.31748823
8. Gugenheim J Crovetto A Petrucciani N Neoadjuvant therapy for pancreatic cancer Updates Surg 2022 74 1 35 42 10.1007/s13304-021-01186-1 34628591
Gugenheim J, Crovetto A, Petrucciani N. Neoadjuvant therapy for pancreatic cancer. Updates Surg. 2022;74(1):35–42.34628591
9. Versteijne E van Dam JL Suker M Janssen QP Groothuis K Akkermans-Vogelaar JM Neoadjuvant chemoradiotherapy versus upfront surgery for resectable and borderline resectable pancreatic cancer: long-term results of the Dutch randomized PREOPANC trial J Clin Oncol 2022 40 11 1220 30 10.1200/JCO.21.02233 35084987
Versteijne E, van Dam JL, Suker M, Janssen QP, Groothuis K, Akkermans-Vogelaar JM, et al. Neoadjuvant chemoradiotherapy versus upfront surgery for resectable and borderline resectable pancreatic cancer: long-term results of the Dutch randomized PREOPANC trial. J Clin Oncol. 2022;40(11):1220–30.35084987
10. Soloff EV Al-Hawary MM Desser TS Fishman EK Minter RM Zins M Imaging assessment of pancreatic cancer resectability after neoadjuvant therapy: AJR Expert Panel Narrative Review AJR Am J Roentgenol 2022 218 4 570 81 10.2214/AJR.21.26931 34851713
Soloff EV, Al-Hawary MM, Desser TS, Fishman EK, Minter RM, Zins M. Imaging assessment of pancreatic cancer resectability after neoadjuvant therapy: AJR expert panel narrative review. AJR Am J Roentgenol. 2022;218(4):570–81.34851713
11. Heinrich S Lang H Neoadjuvant therapy of pancreatic cancer: definitions and benefits Int J Mol Sci 2017 18 8 1622 10.3390/ijms18081622 28933761
Heinrich S, Lang H. Neoadjuvant therapy of pancreatic cancer: definitions and benefits. Int J Mol Sci. 2017;18(8):1622.28933761
12. Treadwell JR Zafar HM Mitchell MD Tipton K Teitelbaum U Jue J Imaging tests for the diagnosis and staging of pancreatic adenocarcinoma: a meta-analysis Pancreas 2016 45 6 789 95 10.1097/MPA.0000000000000524 26745859
Treadwell JR, Zafar HM, Mitchell MD, Tipton K, Teitelbaum U, Jue J. Imaging tests for the diagnosis and staging of pancreatic adenocarcinoma: a meta-analysis. Pancreas. 2016;45(6):789–95.26745859
13. Shukla-Dave A Obuchowski NA Chenevert TL Jambawalikar S Schwartz LH Malyarenko D Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials J Magn Reson Imaging 2019 49 7 e101 21 10.1002/jmri.26518 30451345
Shukla-Dave A, Obuchowski NA, Chenevert TL, Jambawalikar S, Schwartz LH, Malyarenko D, et al. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging. 2019;49(7):e101–21.30451345
14. Taouli B Beer AJ Chenevert T Collins D Lehman C Matos C Diffusion-weighted imaging outside the brain: Consensus statement from an ISMRM-sponsored workshop J Magn Reson Imaging 2016 44 3 521 40 10.1002/jmri.25196 26892827
Taouli B, Beer AJ, Chenevert T, Collins D, Lehman C, Matos C, et al. Diffusion-weighted imaging outside the brain: consensus statement from an ISMRM-sponsored workshop. J Magn Reson Imaging. 2016;44(3):521–40.26892827
15. Padhani AR Liu G Koh DM Chenevert TL Thoeny HC Takahara T Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations Neoplasia 2009 11 2 102 25 10.1593/neo.81328 19186405
Padhani AR, Liu G, Koh DM, Chenevert TL, Thoeny HC, Takahara T, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia. 2009;11(2):102–25.19186405
16. Ni P Lin Y Zhong Q Chen Z Sandrasegaran K Lin C Technical advancements and protocol optimization of diffusion-weighted imaging (DWI) in liver Abdom Radiol 2016 41 1 189 202 10.1007/s00261-015-0602-x
Ni P, Lin Y, Zhong Q, Chen Z, Sandrasegaran K, Lin C. Technical advancements and protocol optimization of diffusion-weighted imaging (DWI) in liver. Abdom Radiol. 2016;41(1):189–202.
17. Baltzer P Mann RM Iima M Sigmund EE Clauser P Gilbert FJ EUSOBI International breast diffusion-weighted imaging working group. Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International breast diffusion-weighted imaging working group Eur Radiol 2020 30 3 1436 50 10.1007/s00330-019-06510-3 31786616
Baltzer P, Mann RM, Iima M, Sigmund EE, Clauser P, Gilbert FJ, et al. EUSOBI international breast diffusion-weighted imaging working group. Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International breast diffusion-weighted imaging working group. Eur Radiol. 2020;30(3):1436–50.31786616
18. Ljimani A Caroli A Laustsen C Francis S Mendichovszky IA Bane O Consensus-based technical recommendations for clinical translation of renal diffusion-weighted MRI MAGMA 2020 33 1 177 95 10.1007/s10334-019-00790-y 31676990
Ljimani A, Caroli A, Laustsen C, Francis S, Mendichovszky IA, Bane O, et al. Consensus-based technical recommendations for clinical translation of renal diffusion-weighted MRI. MAGMA. 2020;33(1):177–95.31676990
19. Beets-Tan RGH Lambregts DMJ Maas M Bipat S Barbaro B Curvo-Semedo L Magnetic resonance imaging for clinical management of rectal cancer: updated recommendations from the 2016 European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus meeting Eur Radiol 2018 28 4 1465 75 10.1007/s00330-017-5026-2 29043428
Beets-Tan RGH, Lambregts DMJ, Maas M, Bipat S, Barbaro B, Curvo-Semedo L, et al. Magnetic resonance imaging for clinical management of rectal cancer: updated recommendations from the 2016 European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus meeting. Eur Radiol. 2018;28(4):1465–75.29043428
20. Barnes A Alonzi R Blackledge M Charles-Edwards G Collins DJ Cook G UK quantitative WB-DWI technical workgroup: consensus meeting recommendations on optimisation, quality control, processing and analysis of quantitative whole-body diffusion-weighted imaging for cancer Br J Radiol 2018 91 1081 20170577 10.1259/bjr.20170577 29076749
Barnes A, Alonzi R, Blackledge M, Charles-Edwards G, Collins DJ, Cook G, et al. UK quantitative WB-DWI technical workgroup: consensus meeting recommendations on optimisation, quality control, processing and analysis of quantitative whole-body diffusion-weighted imaging for cancer. Br J Radiol. 2018;91(1081):20170577.29076749
21. Nissan N Modifications of pancreatic diffusion MRI by tissue characteristics: what are we weighting for? NMR Biomed 2017 30 8 e3728 10.1002/nbm.3728
Nissan N. Modifications of pancreatic diffusion MRI by tissue characteristics: what are we weighting for? NMR Biomed. 2017;30(8):e3728.
22. Xie P Liu K Peng W Zhou Z The correlation between diffusion-weighted imaging at 3.0-T Magnetic Resonance Imaging and Histopathology for Pancreatic Ductal Adenocarcinoma J Comput Assist Tomogr 2015 39 5 697 701 10.1097/RCT.0000000000000274 26176427
Xie P, Liu K, Peng W, Zhou Z. The correlation between diffusion-weighted imaging at 3.0-T magnetic resonance imaging and histopathology for pancreatic ductal adenocarcinoma. J Comput Assist Tomogr. 2015;39(5):697–701.26176427
23. Wang Y Chen ZE Yaghmai V Nikolaidis P McCarthy RJ Merrick L Miller FH Diffusion-weighted MR imaging in pancreatic endocrine tumors correlated with histopathologic characteristics J Magn Reson Imaging 2011 33 5 1071 9 10.1002/jmri.22541 21509863
Wang Y, Chen ZE, Yaghmai V, Nikolaidis P, McCarthy RJ, Merrick L, Miller FH. Diffusion-weighted MR imaging in pancreatic endocrine tumors correlated with histopathologic characteristics. J Magn Reson Imaging. 2011;33(5):1071–9.21509863
24. Cuneo KC Chenevert TL Ben-Josef E Feng MU Greenson JK Hussain HK A pilot study of diffusion-weighted MRI in patients undergoing neoadjuvant chemoradiation for pancreatic cancer Transl Oncol 2014 7 5 644 9 10.1016/j.tranon.2014.07.005 25389460
Cuneo KC, Chenevert TL, Ben-Josef E, Feng MU, Greenson JK, Hussain HK, et al. A pilot study of diffusion-weighted MRI in patients undergoing neoadjuvant chemoradiation for pancreatic cancer. Transl Oncol. 2014;7(5):644–9.25389460
25. Chan E Arlinghaus LR Cardin DB Goff L Berlin JD Parikh A Phase I trial of Vorinostat added to chemoradiation with capecitabine in pancreatic cancer Radiother Oncol 2016 119 2 312 8 10.1016/j.radonc.2016.04.013 27106554
Chan E, Arlinghaus LR, Cardin DB, Goff L, Berlin JD, Parikh A, et al. Phase I trial of vorinostat added to chemoradiation with capecitabine in pancreatic cancer. Radiother Oncol. 2016;119(2):312–8.27106554
26. Okada KI Hirono S Kawai M Miyazawa M Shimizu A Kitahata Y Value of apparent diffusion coefficient prior to neoadjuvant therapy is a predictor of histologic response in patients with borderline resectable pancreatic carcinoma J Hepatobiliary Pancreat Sci 2017 24 3 161 8 10.1002/jhbp.430 28064462
Okada KI, Hirono S, Kawai M, Miyazawa M, Shimizu A, Kitahata Y, et al. Value of apparent diffusion coefficient prior to neoadjuvant therapy is a predictor of histologic response in patients with borderline resectable pancreatic carcinoma. J Hepatobiliary Pancreat Sci. 2017;24(3):161–8.28064462
27. Okada KI Kawai M Hirono S Kojima F Tanioka K Terada M Diffusion-weighted MRI predicts the histologic response for neoadjuvant therapy in patients with pancreatic cancer: a prospective study (DIFFERENT trial) Langenbecks Arch Surg 2020 405 1 23 33 10.1007/s00423-020-01857-4 31993737
Okada KI, Kawai M, Hirono S, Kojima F, Tanioka K, Terada M, et al. Diffusion-weighted MRI predicts the histologic response for neoadjuvant therapy in patients with pancreatic cancer: a prospective study (DIFFERENT trial). Langenbecks Arch Surg. 2020;405(1):23–33.31993737
28. Zimmermann C, Distler M, Jentsch C, Blum S, Folprecht G, Zöphel K, et al. Evaluation of response using FDG-PET/CT and diffusion weighted MRI after radiochemotherapy of pancreatic cancer: a non-randomized, monocentric phase II clinical trial-PaCa-DD-041 (Eudra-CT 2009-011968-11). Strahlenther Onkol. 2021;197(1):19–26.
29. Kang JH Lee SS Kim JH Byun JH Kim HJ Yoo C Multiparametric MRI for prediction of treatment response to neoadjuvant FOLFIRINOX therapy in borderline resectable or locally advanced pancreatic cancer Eur Radiol 2021 31 2 864 74 10.1007/s00330-020-07134-8 32813104
Kang JH, Lee SS, Kim JH, Byun JH, Kim HJ, Yoo C, et al. Multiparametric MRI for prediction of treatment response to neoadjuvant FOLFIRINOX therapy in borderline resectable or locally advanced pancreatic cancer. Eur Radiol. 2021;31(2):864–74.32813104
30. Hussien N Hussien RS Saad DHA El Kassas M Elkhatib WF Ezz El Din M The role of MRI pancreatic protocol in assessing response to Neoadjuvant Therapy for patients with Borderline Resectable Pancreatic Cancer Front Oncol 2022 13 796317 10.3389/fonc.2021.796317
Hussien N, Hussien RS, Saad DHA, El Kassas M, Elkhatib WF, Ezz El Din M. The role of MRI pancreatic protocol in assessing response to neoadjuvant therapy for patients with borderline resectable pancreatic cancer. Front Oncol. 2022;13:11:796317.
31. Klaassen R Gurney-Champion OJ Engelbrecht MRW Stoker J Wilmink JW Besselink MG Evaluation of six diffusion-weighted MRI models for assessing effects of Neoadjuvant Chemoradiation in Pancreatic Cancer patients Int J Radiat Oncol Biol Phys 2018 102 4 1052 62 10.1016/j.ijrobp.2018.04.064 29891208
Klaassen R, Gurney-Champion OJ, Engelbrecht MRW, Stoker J, Wilmink JW, Besselink MG, et al. Evaluation of six diffusion-weighted MRI models for assessing effects of neoadjuvant chemoradiation in pancreatic cancer patients. Int J Radiat Oncol Biol Phys. 2018;102(4):1052–62.29891208
32. Barral M Taouli B Guiu B Koh DM Luciani A Manfredi R Diffusion-weighted MR imaging of the pancreas: current status and recommendations Radiology 2015 274 1 45 63 10.1148/radiol.14130778 25531479
Barral M, Taouli B, Guiu B, Koh DM, Luciani A, Manfredi R, et al. Diffusion-weighted MR imaging of the pancreas: current status and recommendations. Radiology. 2015;274(1):45–63.25531479
33. Trajkovic-Arsic M Heid I Steiger K Gupta A Fingerle A Wörner C Apparent diffusion coefficient (ADC) predicts therapy response in pancreatic ductal adenocarcinoma Sci Rep 2017 7 1 17038 10.1038/s41598-017-16826-z 29213099
Trajkovic-Arsic M, Heid I, Steiger K, Gupta A, Fingerle A, Wörner C, et al. Apparent diffusion coefficient (ADC) predicts therapy response in pancreatic ductal adenocarcinoma. Sci Rep. 2017;7(1):17038.29213099
34. Dalah E Erickson B Oshima K Schott D Hall WA Paulson E Correlation of ADC with pathological treatment response for Radiation Therapy of Pancreatic Cancer Transl Oncol 2018 11 2 391 8 10.1016/j.tranon.2018.01.018 29455085
Dalah E, Erickson B, Oshima K, Schott D, Hall WA, Paulson E, et al. Correlation of ADC with pathological treatment response for radiation therapy of pancreatic cancer. Transl Oncol. 2018;11(2):391–8.29455085
35. Liu Y Bai R Sun H Liu H Wang D Diffusion-weighted magnetic resonance imaging of uterine cervical cancer J Comput Assist Tomogr 2009 33 6 858 62 10.1097/RCT.0b013e31819e93af 19940650
Liu Y, Bai R, Sun H, Liu H, Wang D. Diffusion-weighted magnetic resonance imaging of uterine cervical cancer. J Comput Assist Tomogr. 2009;33(6):858–62.19940650
36. Brandão AC Lehman CD Partridge SC Breast magnetic resonance imaging: diffusion-weighted imaging Magn Reson Imaging Clin N Am 2013 21 2 321 36 10.1016/j.mric.2013.01.002 23642556
Brandão AC, Lehman CD, Partridge SC. Breast magnetic resonance imaging: diffusion-weighted imaging. Magn Reson Imaging Clin N Am. 2013;21(2):321–36.23642556
37. Heo SH Jeong YY Shin SS Kim JW Lim HS Lee JH Apparent diffusion coefficient value of diffusion-weighted imaging for hepatocellular carcinoma: correlation with the histologic differentiation and the expression of vascular endothelial growth factor Korean J Radiol 2010 11 3 295 303 10.3348/kjr.2010.11.3.295 20461183
Heo SH, Jeong YY, Shin SS, Kim JW, Lim HS, Lee JH, et al. Apparent diffusion coefficient value of diffusion-weighted imaging for hepatocellular carcinoma: correlation with the histologic differentiation and the expression of vascular endothelial growth factor. Korean J Radiol. 2010;11(3):295–303.20461183
38. Surov A Eger KI Potratz J Gottschling S Wienke A Jechorek D Apparent diffusion coefficient correlates with different histopathological features in several intrahepatic tumors Eur Radiol 2023 33 9 5955 64 10.1007/s00330-023-09788-6 37347430
Surov A, Eger KI, Potratz J, Gottschling S, Wienke A, Jechorek D. Apparent diffusion coefficient correlates with different histopathological features in several intrahepatic tumors. Eur Radiol. 2023;33(9):5955–64.37347430
39. Schob S Meyer J Gawlitza M Frydrychowicz C Müller W Preuss M Diffusion-weighted MRI reflects proliferative activity in primary CNS lymphoma PLoS ONE 2016 11 8 e0161386 10.1371/journal.pone.0161386 27571268
Schob S, Meyer J, Gawlitza M, Frydrychowicz C, Müller W, Preuss M, et al. Diffusion-weighted MRI reflects proliferative activity in primary CNS lymphoma. PLoS ONE. 2016;11(8):e0161386.27571268
40. Surov A Hamerla G Meyer HJ Winter K Schob S Fiedler E Whole lesion histogram analysis of meningiomas derived from ADC values. Correlation with several cellularity parameters, proliferation index KI 67, nucleic content, and membrane permeability Magn Reson Imaging 2018 51 158 62 10.1016/j.mri.2018.05.009 29782920
Surov A, Hamerla G, Meyer HJ, Winter K, Schob S, Fiedler E. Whole lesion histogram analysis of meningiomas derived from ADC values. Correlation with several cellularity parameters, proliferation index KI 67, nucleic content, and membrane permeability. Magn Reson Imaging. 2018;51:158–62.29782920
41. Surov A Chang YW Li L Martincich L Partridge SC Kim JY Wienke A Apparent diffusion coefficient cannot predict molecular subtype and lymph node metastases in invasive breast cancer: a multicenter analysis BMC Cancer 2019 19 1 1043 10.1186/s12885-019-6298-5 31690273
Surov A, Chang YW, Li L, Martincich L, Partridge SC, Kim JY, Wienke A. Apparent diffusion coefficient cannot predict molecular subtype and lymph node metastases in invasive breast cancer: a multicenter analysis. BMC Cancer. 2019;19(1):1043.31690273
42. Surov A Clauser P Chang YW Li L Martincich L Partridge SC Can diffusion-weighted imaging predict tumor grade and expression of Ki-67 in breast cancer? A multicenter analysis Breast Cancer Res 2018 20 1 58 10.1186/s13058-018-0991-1 29921323
Surov A, Clauser P, Chang YW, Li L, Martincich L, Partridge SC, et al. Can diffusion-weighted imaging predict tumor grade and expression of Ki-67 in breast cancer? A multicenter analysis. Breast Cancer Res. 2018;20(1):58.29921323
43. Guo C Chen X Xiao W Wang Q Sun K Wang Z Pancreatic neuroendocrine neoplasms at magnetic resonance imaging: comparison between grade 3 and grade 1/2 tumors Onco Targets Ther 2017 10 1465 74 10.2147/OTT.S127803 28331340
Guo C, Chen X, Xiao W, Wang Q, Sun K, Wang Z. Pancreatic neuroendocrine neoplasms at magnetic resonance imaging: comparison between grade 3 and grade 1/2 tumors. Onco Targets Ther. 2017;10:1465–74.28331340
44. Ma C Yang P Li J Bian Y Wang L Lu J Pancreatic adenocarcinoma: variability in measurements of tumor size among computed tomography, magnetic resonance imaging, and pathologic specimens Abdom Radiol (NY) 2020 45 3 782 8 10.1007/s00261-019-02125-w 31292672
Ma C, Yang P, Li J, Bian Y, Wang L, Lu J. Pancreatic adenocarcinoma: variability in measurements of tumor size among computed tomography, magnetic resonance imaging, and pathologic specimens. Abdom Radiol (NY). 2020;45(3):782–8.31292672
45. Bian Y Jiang H Cao K Mms XF Li J Ma C The relationship between microscopic tumor size and CT tumor size in pancreatic ductal adenocarcinoma Clin Imaging 2021 76 30 7 10.1016/j.clinimag.2020.11.039 33548890
Bian Y, Jiang H, Cao K, Mms XF, Li J, Ma C, et al. The relationship between microscopic tumor size and CT tumor size in pancreatic ductal adenocarcinoma. Clin Imaging. 2021;76:30–7.33548890
46. Yang P Mao K Gao Y Wang Z Wang J Chen Y Tumor size measurements of pancreatic cancer with neoadjuvant therapy based on RECIST guidelines: is MRI as effective as CT? Cancer Imaging 2023 23 1 8 10.1186/s40644-023-00528-z 36653861
Yang P, Mao K, Gao Y, Wang Z, Wang J, Chen Y, et al. Tumor size measurements of pancreatic cancer with neoadjuvant therapy based on RECIST guidelines: is MRI as effective as CT? Cancer Imaging. 2023;23(1):8.36653861
47. Bilreiro C Andrade L Marques RM Matos C Diffusion-weighted imaging for determining response to neoadjuvant therapy in pancreatic cancer: a systematic review and meta-analysis Eur Radiol 2024 34 5 3238 48 10.1007/s00330-023-10381-0 37907761
Bilreiro C, Andrade L, Marques RM, Matos C. Diffusion-weighted imaging for determining response to neoadjuvant therapy in pancreatic cancer: a systematic review and meta-analysis. Eur Radiol. 2024;34(5):3238–48.37907761
48. Tang L Zhou XJ Diffusion MRI of cancer: from low to high b-values J Magn Reson Imaging 2019 49 1 23 40 10.1002/jmri.26293 30311988
Tang L, Zhou XJ. Diffusion MRI of cancer: from low to high b-values. J Magn Reson Imaging. 2019;49(1):23–40.30311988
49. Ma C Liu L Li J Wang L Chen LG Zhang Y Apparent diffusion coefficient (ADC) measurements in pancreatic adenocarcinoma: a preliminary study of the effect of region of interest on ADC values and interobserver variability J Magn Reson Imaging 2016 43 2 407 13 10.1002/jmri.25007 26182908
Ma C, Liu L, Li J, Wang L, Chen LG, Zhang Y, et al. Apparent diffusion coefficient (ADC) measurements in pancreatic adenocarcinoma: a preliminary study of the effect of region of interest on ADC values and interobserver variability. J Magn Reson Imaging. 2016;43(2):407–13.26182908
50. Ma C Guo X Liu L Zhan Q Li J Zhu C Effect of region of interest size on ADC measurements in pancreatic adenocarcinoma Cancer Imaging 2017 17 1 13 10.1186/s40644-017-0116-6 28464866
Ma C, Guo X, Liu L, Zhan Q, Li J, Zhu C, et al. Effect of region of interest size on ADC measurements in pancreatic adenocarcinoma. Cancer Imaging. 2017;17(1):13.28464866
