
==== Front
J Cancer Res Clin Oncol
J Cancer Res Clin Oncol
Journal of Cancer Research and Clinical Oncology
0171-5216
1432-1335
Springer Berlin Heidelberg Berlin/Heidelberg

39249584
5930
10.1007/s00432-024-05930-z
Research
Integrative radiopathomics model for predicting progression-free survival in patients with nonmetastatic nasopharyngeal carcinoma
http://orcid.org/0000-0002-9543-6389
Hou Jing 1
http://orcid.org/0000-0001-9053-8549
Yi Xiaochun 1
http://orcid.org/0000-0001-9625-2752
Li Handong 1
http://orcid.org/0000-0003-2176-4395
Lu Qiang 1
http://orcid.org/0000-0002-1274-1709
Lin Huashan 2
Li Junjun lijunjun@hnca.org.cn

3
http://orcid.org/0000-0001-8179-6300
Zeng Biao zengbiao@hnca.org.cn

4
http://orcid.org/0000-0003-4502-1027
Yu Xiaoping nj9015@163.com

1
1 grid.216417.7 0000 0001 0379 7164 Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, Hunan 410013 P. R. China
2 Department of Pharmaceuticals Diagnosis, GE Healthcare, Beijing, P. R. China
3 grid.216417.7 0000 0001 0379 7164 Department of Pathology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, Hunan 410013 P. R. China
4 grid.216417.7 0000 0001 0379 7164 Department of Radiotherapy, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, Hunan 410013 P. R. China
9 9 2024
9 9 2024
2024
150 9 4154 4 2024
21 8 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/.
Purpose

To construct an integrative radiopathomics model for predicting progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC) patients.

Methods

357 NPC patients who underwent pretreatment MRI and pathological whole-slide imaging (WSI) were included in this study and randomly divided into two groups: a training set (n = 250) and validation set (n = 107). Radiomic features extracted from MRI were selected using the minimum redundancy maximum relevance and least absolute shrinkage and selection operator methods. The pathomics signature based on WSI was constructed using a deep learning architecture, the Swin Transformer. The radiopathomics model was constructed by incorporating three feature sets: the radiomics signature, pathomics signature, and independent clinical factors. The prognostic efficacy of the model was assessed using the concordance index (C-index). Kaplan-Meier curves for the stratified risk groups were tested by the log-rank test.

Results

The radiopathomics model exhibited superior predictive performance with C-indexes of 0.791 (95% confidence interval [CI]: 0.724–0.871) in the training set and 0.785 (95% CI: 0.716–0.875) in the validation set compared to any single-modality model (radiomics: 0.619, 95% CI: 0.553–0.706; pathomics: 0.732, 95% CI: 0.662–0.802; clinical model: 0.655, 95% CI: 0.581–0.728) (all, P < 0.05). The radiopathomics model effectively stratified patients into high- and low-risk groups in both the training and validation sets (P < 0.001).

Conclusion

The developed radiopathomics model demonstrated its reliability in predicting PFS for NPC patients. It effectively stratified individual patients into distinct risk groups, providing valuable insights for prognostic assessment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-024-05930-z.

Keywords

Nasopharyngeal carcinoma
Radiomics
Digital pathology
Prognosis
Risk stratification
Department of Science and Technology of Hunan Province,China2022JJ70105 Science and Technology Bureau, Changsha, Chinakq2202469 issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2024
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pmcIntroduction

Nasopharyngeal carcinoma (NPC) is a prevalent type of head and neck cancer arising in the nasopharynx. Its geographical distribution is remarkably uneven, with a high incidence in Southeast Asia (Bray et al. 2018). Although significant advancements in survival rates attributed to intensity-modulated radiation therapy (IMRT) and concurrent chemotherapy, a substantial proportion of NPC patients (20–30%) still succumb to local-regional relapse and distant metastasis (Pan et al. 2016). Thus, early prognostic prediction of NPC is critical for guiding treatment decisions and enabling personalized precision therapy.

Currently, therapeutic decisions and prognostic assessments of NPC rely mainly on the tumor-node-metastasis (TNM) staging system (Pan et al. 2016). However, it has been observed that patients with the same clinical stages can have highly variable treatment outcomes (Chen et al. 2017), indicating that the TNM staging system may not provide sufficient precision. Additionally, several prognostic factors have been identified to distinguish NPC patients with poor clinical outcomes. These include the plasma load of Epstein-Barr virus DNA (You et al. 2019), the expression of programmed cell death protein 1/programmed cell death ligand 1 (Ono et al. 2018), and the C-reactive protein/albumin ratio (He et al. 2016), which may serve as independently prognostic indicators for NPC. However, their clinical use remains limited. Therefore, there is a need for improved and effective prognostic markers to stratify patients with NPC before treatment and refine the selection of therapeutic regimens.

Radiomics transforms macroscopic medical images into quantitative information by extracting features that are not visible to the naked eye (Hatt et al. 2017; Kuo and Jamshidi 2014). This technique has the potential to provide crucial information regarding tumor phenotypes. Radiomics features hold promise as potential biomarkers for predicting treatment efficacy and evaluating prognosis in patients with NPC (Spadarella et al. 2021). A recent systematic review highlighted several studies aimed at predicting the prognosis of NPC based on magnetic resonance imaging (MRI) radiomics (Lee et al. 2022). However, these studies’ results demonstrated considerable variability in the efficacy of radiomics for predicting progression-free survival (PFS) in NPC. Although radiomics shows great potential for prognostic prediction of NPC, its performance requires further enhancement and robustness.

The emergence of digital pathology facilitated by digital pathological section scanners and whole-slide image (WSI) has revolutionized the pathology field. By digitizing pathological section information, digital pathology can provide valuable insights into the molecular characteristics and genetic patterns that correlate with therapeutic response and tumor prognosis. Recent studies have applied artificial intelligence to mine digital pathology data to aid risk stratification, genotype prediction, and outcome assessment of various tumors (Kather et al. 2019; Pantanowitz et al. 2020; Skrede et al. 2020). To date, a few studies have combined pathological microfeatures with deep learning to predict survival risk, and the results have demonstrated the reliability of pathological models in predicting NPC prognosis (Liu et al. 2020). However, further research is warranted to explore the full potential of digital pathology and artificial intelligence for improving the prognostic prediction of NPC.

Multi-omics research, encompassing genomics, proteomics, pathomics, radiomics, and other fields, has emerged as a prominent area of study. Researchers are actively exploring the relationship between multi-omics data and diseases. Radiopathomics, a significant branch of the multi-omics approach, bridges radiomics and pathomics data, providing a multidimensional perspective for investigating tumor heterogeneity across macroscopic and microscopic scales. Radiopathomics has shown promise in predicting the prognosis of solid tumors (Alvarez-Jimenez et al. 2020; Feng et al. 2022; Saednia et al. 2022; Shao et al. 2020) demonstrating that digital pathology can complement tumor heterogeneity information and improve the prognostic performance of existing radiomics models. However, the application of radiopathomics to NPC remains largely unexplored. The aim of the study was to construct and validate an integrative radiopathomics model to predict the prognosis of patients with NPC.

Materials and methods

Compliance with ethical standards

Local institutional review board approved this retrospective study (approval number: 2014-8) and the requirement for written informed consent was waived.

Patients

We reviewed and screened eligible patients diagnosed at our center between January 2014 and December 2019. Inclusion criteria for the study were as follows: (1) newly histologically confirmed non-metastatic NPC; (2) availability of pretreatment MRI scan; (3) biopsy under nasopharyngoscopy at our hospital with complete hematoxylin-eosin (H&E)-stained slides; and (4) regular post-treatment follow-up and complete clinical data. Exclusion criteria were: (1) incomplete pretreatment head and neck MRI; (2) poor quality on H&E-stained slides, including over-light and over-deep staining; (3) other malignancies and receipt of other anti-cancer therapies; or (4) distant metastasis at pretreatment. The final study cohort consisted of 357 eligible patients (Fig. 1).

Fig. 1 Flow-chart shows patient recruitment process. Abbreviations: NPC = nasopharyngeal carcinoma, MRI = magnetic resonance imaging

Treatment, follow-up, and clinical endpoint

All NPC patients were staged based on the 7th or 8th edition of the American Joint Committee on Cancer TNM classification system. As per the guidelines for NPC management, patients with stages I–II (T1N0, T2N0) disease (n = 5) received radiotherapy alone, whereas those with stage II (T1-2N1, T3N0) disease (n = 18) received concurrent chemoradiotherapy (CCRT). Patients with stages III–IVA disease (n = 334) received CCRT combined with adjuvant chemotherapy (AC) or induction chemotherapy (IC). The IC and AC regimens consisted of cisplatin-based therapies administered every 3 weeks for 2–4 cycles. All patients received radiotherapy at a total dose of 70–76 Gy, delivered in 30–33 fractions. During radiotherapy, the CCRT regimen involved the use of cisplatin for 3–6 cycles.

All patients in the study underwent regular follow-ups according to the National Comprehensive Cancer Network guidelines (Pfister et al. 2020). They had MRI examination at 3-month intervals during the first year, 6-month intervals during the second year, and yearly intervals thereafter. PFS was set as the primary endpoint in the study, which was defined as the time interval from the initiation of therapy to disease progression (including locoregional recurrence or distant metastases), death due to any cause, or the date of the last follow-up visit (censored). Disease progression was confirmed through pathological and/or imaging examinations, such as MRI, computed tomography (CT), and positron emission tomography-CT.

MRI protocols

The MRI procedure was carried out utilizing a 1.5-Tesla MRI scanner with a combined head and neck coil. The MRI protocols were as follows: (1) axial T1-weighted spin-echo images (T1WI): repetition time (TR)/echo time (TE), 580/7.8 ms; slice thickness, 5 mm; slice number, 36; slice space, 1 mm; and number of excitations (NEX), 2; (2) axial T2-weighted spin-echo images with fat suppression (T2WI): TR/TE, 6289 ms/85 ms; slice thickness, 5 mm; slice number 36, slice space, 1 mm; and NEX, 2; and (3) axial contrast-enhanced T1-weighted spin-echo images (CET1-w): TR/TE, 500/8 ms; field of view, 22 × 22 cm; NEX, 2; slice thickness, 4 mm; and interslice gap, 0.8 mm. To acquire axial CET1-w images, gadodiamide was administered intravenously at a dose of 0.1 mmol/kg of body weight and a rate of 3.5 ml/s.

Radiomics signature construction and validation

Given the variation in MRI images of intra- and inter-patients, preprocessing is crucial. Preprocessing of the MRI data was carried out using the AK software, which has obtained registration and approval. The preprocessing steps included resampling, skull stripping, and standardization of intensity. The image resolution was modified to 1 × 1 × 1 mm during the resampling process. The thickness of all MRI images was adjusted to 1 mm using linear interpolation. And then a gray-level unified adjustment was performed to standardize the range to 0-255.

Two radiologists, Radiologist A and Radiologist B, each having 13 and 14 years of experience in head and neck MRI, respectively, performed the segmentation using the ITK-SNAP software. The radiologists were unaware of the prognostic outcomes of the NPC patients and separately outlined the boundaries of the primary NPC lesion, layer by layer. Following this, the regions of interest (ROIs) were automatically transformed into volumes of interest (VOIs) and saved in the NII format.

Inter- and intraclass correlation coefficients (ICCs) were used to evaluate the reproducibility of intra- and inter-observer segmentation. The two above-mentioned radiologists independently selected 50 NPC patients for MRI segmentation at random. One month later, Radiologist A repeated the same process. Good consistency was obtained, with ICCs of > 0.75 (details of the results are provided below). As a result, Radiologist A was tasked with segmenting all the remaining images.

A total of 792 radiomic features were obtained from T2WI and CET1-w images, with 396 features extracted from each MRI sequence using AK software. These features were further categorized into the following six categories, namely histogram, gray-level co-occurrence matrix, gray-level size zone matrix, haralick, form factor, and run-length matrix.

In order to identify the most predictive features linked to PFS, two methods for feature selection, the minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO), were employed. Firstly, the mRMR method was utilized to eliminate redundant and irrelevant features, utilizing a heuristic scoring criterion that assessed their relevance-redundancy index ranks. Subsequently, LASSO classification was carried out to choose an optimized subset of features, while 10-fold cross-validation was used to prevent overfitting. The Rad-score, a radiomics signature, was developed through LASSO-Cox regression analysis, generating a linear combination of the selected features weighted by their respective coefficients for each patient.

Pathomics signature construction and validation

All H&E-stained slices of the enrolled patients with NPC were converted to WSI using a Pannoramic MIDI II digital section scanner at a magnification of ×40, with a resolution of 0.14 μm/pixel, and saved in SVS format.

A novel deep learning architecture called the Swin Transformer was developed as a deep feature extractor. The process involved the following steps: (1) the tissue regions were segmented using morphological methods, including Gaussian blur, the OTSU algorithm, and closed operation; (2) a sliding window (224 × 224) traversed the entire WSI image from top left to bottom right to obtain tissue-involved patches, each containing ≥ 50% of pathological tissue; (3) each patch was fed into the Swin Transformer, with its classification head removed, which was pretrained using the ImageNet dataset to extract 768 features for further survival analysis of PFS; and (4) the mean value of all patches in each patient was calculated. The neural network was implemented using the PyTorch 1.4.1 library in Python 3.7.0 (https://pytorch.org).

Radiopathomics model construction and validation

Univariate and multivariate Cox regression analyses were carried out to identify independent clinical prognostic factors from all clinical parameters. And a clinical model was constructed by independent clinical prognostic factors. Subsequently, a radiopathomics nomogram was constructed to predict PFS by incorporating the radiomics signature, pathomics signature, and independent clinical factors utilizing logistic regression analysis. The prognostic performance of the radiomics, pathomics, clinical, and radiopathomics models was quantitatively evaluated using the concordance index (C-index). Additionally, a calibration curve was produced to evaluate the efficacy of the radiopathomics model. The goodness-of-fit of the radiopathomics model was assessed using the Hosmer-Lemeshow test. Furthermore, risk stratification analysis was performed to investigate the discriminability of the radiomics, pathomics, clinical, and radiopathomics models. The workflow of this study is illustrated in Fig. 2.

Fig. 2 Workflow of this study. Note: Images of pretreatment MRI (T2WI, CET1-w) and WSI were retrospectively collected and segmented for feature extraction. After feature selection and modelling, two sets of signatures (radiomics, pathomics) were generated, and the two signatures combined with some independent clinical factors to construct the radiopathomics model. Abbreviations: T2WI = T2-weighted imaging, CET1-w = contrast-enhanced T1-weighted image, WSI = whole-slide image

Statistical analysis

All statistical analyses were conducted with R software (version 3.3.3, http://www.Rproject.org). C-index was used to evaluate the prognostic performance of the radiomics, pathomics, clinical, and radiopathomics models. The CompareC package was used to compare the C-indices of the different models. In the two-tailed analysis, statistical significance was defined as a P-value < 0.05.

Results

Baseline patient characteristics

Table 1 summarizes the demographic factors and clinical statuses of the patients in both the training and validation cohorts. The median follow-up interval were 60.02 months (interquartile range [IQR]: 45.25–72.33) and 54.23 months (IQR: 43.33–67.60) for the training and validation cohorts, respectively. No significant differences were observed in any clinical factor between the two cohorts.

Table 1 Characteristics of patients in the training and validation sets

	Training
(N = 250)	Validation
(N = 107)	P	
Sex, No. (%)	
 Male	153 (61.2)	76 (71.0)		
 Female	97 (38.8)	31 (29.0)	0.076	
Age	
 Median (IQR)	46.50(41–51)	47(41–53)	0.664	
Smoking, No. (%)	
 YES	94(37.6)	44(41.1)		
 NO	156(62.4)	63(58.9)	0.531	
Drinking, No. (%)	
 YES	71(28.4)	32(29.9)		
 NO	179(71.6)	75(70.1)		
Family history of cancer, No. (%)	
 YES	34(13.6)	11(10.3)		
 NO	216(86.4)	96(89.7)	0.387	
WHO pathological type	
 I	0(0)	0(0)		
 II	1(0.4)	1(0.9)		
 III	249(99.6)	106(99.1)	0.510	
TNM stage	
 I	0(0)	0(0)		
 II	15(6.0)	8(7.5)		
 III	139(55.6)	62(57.9)		
 IV	96(38.4)	37(34.6)	0.733	
T stage	
 1	24(9.6)	10(9.3)		
 2	112(44.8)	47(43.9)		
 3	68(27.2)	31(29.0)		
 4	46(18.4)	19(17.8)	0.989	
N stage	
 0	7(2.8)	4(3.7)		
 1	27(10.8)	9(8.4)		
 2	156(62.4)	71(66.4)		
 3	60(24.0)	23(21.5)	0.795	
Hb	
 Median (IQR)	132(120–145)	131(123–143)	0.719	
ALB	
 Median (IQR)	41.1(38.17–43.6)	41.4(38.5–44.7)	0.784	
EBV-DNA	
 Median (IQR)	408(400-493.75)	400(400–671)	0.637	
CRP	
 Median (IQR)	2.16(1.44–3.28)	2.19(1.73–2.60)	0.886	
LDH	
 Median (IQR)	163(146.95–186.50)	165(148.80-178.50)	0.745	
PFS(months)	
 Median (IQR)	60.02(45.25–72.33)	54.23(43.33–67.60)	0.083	
Abbreviations: WHO = world health organization, Hb = hemoglobin, ALB = albumin, EBV-DNA = Epstein-Barr Virus DNA, CRP = C-reaction protein, LDH = lactate dehydrogenase, PFS = progression-free survival, IQR = interquartile range

Single-modality models

The intra-reader ICC for measurements operated by radiologist A varied from 0.769 to 0.926. Additionally, the inter-reader ICC between the two radiologists ranged from 0.834 to 0.915.

Following the mRMR and LASSO algorithms for feature selection, 7 features derived from T2WI (n = 1) and CET1-w (n = 6) were retained to construct the final radiomics model. The detailed feature selection process is depicted in Fig. S1, and the Radscore formula can be found in the Supplementary Material. The radiomics model exhibited poor prognostic performance in both the training and validation cohorts, with C-indices of 0.619 (95% confidence interval [CI]: 0.553–0.706) and 0.640 (95% CI: 0.571–0.771), respectively (Table 2).

Table 2 Prognostic performance of four models in NPC patients

Models	Training	Validation	
C-index (95%CI)	P	C-index (95%CI)	P	
Radiomics	0.619(0.554–0.707)	<0.001	0.640(0.571–0.771)	0.004	
Pathomcis	0.732(0.662–0.802)	<0.001	0.744(0.655–0.834)	<0.001	
Clinical	0.655(0.581–0.723)	<0.001	0.576(0.484–0.667) 0.073	0.073	
Radiopathomics	0.791(0.724–0.871)	<0.001	0.785(0.716–0.875)	<0.001	
Abbreviation NPC = nasopharyngeal carcinoma

However, the pathomics signature demonstrated a good C-indices of 0.732 (95% CI: 0.662–0.802) and 0.744 (95% CI: 0.655–0.834) in the training and validation cohorts, respectively (Table 2).

In the clinical model, T stage (hazard ratio [HR]: 1.512, P = 0.013), N stage (HR: 1.859, P = 0.013), family history of cancer (HR: 2.173, P = 0.033), and lactate dehydrogenase (LDH) level (HR: 1.007, P < 0.001) were significantly associated with PFS. However, the clinical model yielded poor C-indices of 0.655 (95% CI: 0.581–0.728) and 0.576 (95% CI: 0.484–0.627) in the training and validation cohorts, respectively (Table 2).

Radiopathomics model

The radiopathomics nomogram model, which integrated independent clinical predictors, radiomics, and pathological signatures, is shown in Fig. 3. The radiopathomics model exhibited the highest predictive performance with a C-index of 0.791 (95% CI: 0.724–0.871) among the four models in the training set. Similarly, in the validation set, the combined model also demonstrated the highest predictive performance (C-index 0.785, 95% CI: 0.716–0.875) (Table 2). Significant differences were observed in the C-indices between the radiopathomics model and the radiomics (P < 0.001), pathomics (P = 0.036), and clinical models (P < 0.001).

Fig. 3 Radiopathomics model integrating radiomics signature, pathomics signature, and clinical factors for the prediction of PFS in patients with NPC. Abbreviations: PFS = progression-free survival, NPC = nasopharyngeal carcinoma, LDH = lactate dehydrogenase

The calibration curve of the radiopathomics model displayed excellent calibration performance in both the training (Fig. 4a) and validation sets (Fig. 4b). The Hosmer–Lemeshow test revealed no significant difference between the training and validation sets (P > 0.05). Furthermore, decision curve analysis indicated that the integrative model exhibited the highest performance compared to the other three models (Fig. 4c).

Fig. 4 Calibration curve of radiopathomics model in training set (a) and validation set (b) and decision curve analysis (c) of the radiopathomics, radiomics, pathomics and clinical models for the prediction of PFS in NPC patients. Abbreviations: PFS = progression-free survival, NPC = nasopharyngeal carcinoma

When employing the constructed models to categorize patients into high- and low-risk groups using a threshold value derived from their PFS risk score, we found that the radiomics, pathomics, and radiopathomics models successfully stratified patients into two groups with distinct differences in PFS in both the training and validation sets (all, P < 0.001) (Fig. 5a, b, d). This indicated that the low-risk group exhibited a longer PFS than the high-risk group. However, the clinical model stratified patients into low- and high-risk groups in the training set (P < 0.001) but not in the validation set (P = 0.065) (Fig. 5c).

Fig. 5 Risk stratification analysis of radiomics (a), pathomics (b), clinical (c) and radiopathomics model (d) for predicting PFS in the training set and validation set respectively in NPC patients. Abbreviations: PFS = progression-free survival, NPC = nasopharyngeal carcinoma

Discussion

In clinical practice, obtaining comprehensive prognostic information for treatment optimization based on single radiographic or clinical data can be challenging. Here, we addressed this issue by constructing a radiopathomics model that integrates a quantitative radiomics signature, pathomics signature, and clinical factors to predict NPC prognosis. The radiopathomics model demonstrated superior prediction performance compared with any of the single-modality models.

Pathological microscopic features can provide valuable insights into tissue heterogeneity and have been utilized in constructing prognostic models for various tumors, such as lung cancer, prostate cancer, and NPC (Lambrechts et al. 2018; Pantanowitz et al. 2020; Travis et al. 2013; Wang et al. 2018). In our study, we observed that quantitative pathological information derived from WSI was valuable for enriching lesion descriptors to predict NPC prognosis. Among the three single-modality models, the pathomics model exhibited the best prognostic performance for PFS in both the training and validation sets. This finding is consistent with a previous study that reported a similar predictive performance (C-index: 0.723) for PFS in NPC using a pathological model constructed with handcrafted features extracted from WSI (Liu et al. 2020). In contrast, the present study used an end-to-end approach to develop the pathomics model, which automatically extracted representative information and avoided the potential issues of manual segmentation, e.g., incompleteness and instability. Furthermore, we found that the prognostic performance of the radiopathomics model outperformed the radiomics and pathomics models alone in predicting PFS. This result further confirms the essential role of the pathomics signature in predicting PFS and its ability to enhance the predictive power of the radiomics model. These findings align with a previous report involving 220 patients with NPC(Zhang et al. 2020).

We constructed a prognostic radiomics model using seven MRI-based features to predict PFS in patients with NPC. However, the radiomics model exhibited a poor predictive performance when compared to the findings of a previous meta-analysis study, which reported a mean C-index of 0.76 (Lee et al. 2022). The heterogeneity observed across the studies may be attributed to several factors. First, the number of selected features has been reported as a significant factor influencing C-index performance (Chu et al. 2019; Skrede et al. 2020). Studies with a larger number of features tend to yield higher C-indices; however, this may lead to overfitting and reduced reproducibility of the external datasets. We selected a limited number of features to maintain a balance between predictive performance and generalizability. Furthermore, our study included patients with NPC at various stages (T1–T4), whereas previous studies focused only on advanced stages. This broad inclusion may have influenced the model’s predictive performance. A radiomics model based on a cohort of patients at stage T1–2 or T3–4 had a better prognostic performance than that based on a dataset of patients at all stages (Wu et al. 2021). Nevertheless, the broad scope of the model reflects real-world clinical situations and enhances its ability to be generalized to different patient populations. Interestingly, when we incorporated radiomics features into the pathomics model, the predictive performance improved. This finding suggests that the combination of radiomics and pathomics signatures adds complementary value to prognosis prediction.

Although clinical factors and biochemical indicators are related to survival, their individual prognostic values are limited. In our study, T stage, N stage, family history of cancer, and LDH levels were identified as independent factors. However, the clinical model based solely on these factors displayed a poor predictive performance. However, when these clinical factors were integrated into the radiomics, pathomics, or radiopathomics models, the combined models showed significantly improved predictive abilities compared with the original models. Our results align with those of prior studies (Bologna et al. 2020; Yang et al. 2019; Zhang et al. 2017), emphasizing the complementary nature of all prognostic information, which should be thoroughly explored for a comprehensive understanding of patient outcomes.

Our study has a few limitations. First, all patients were recruited from a single center, necessitating multicenter validation using independent datasets and prospective studies to confirm the model’s robustness and generalizability. Additionally, NPC biopsy specimens often contain numerous benign lymphoid tissues interspersed with atypical squamous cells. Analyzing the entire slice may have influenced the accuracy of the final results. Future investigations should explore manual labeling and delineation of cellular components to better elucidate the value of digital pathology for the prognostic evaluation of NPC. Moreover, the sample size in our study may require further expansion to enhance the statistical power and reliability of our findings. Future work with independent validation sets from multiple institutions would provide a more comprehensive assessment of the model’s stability and generalizability.

Conclusion

In summary, we have successfully constructed and validated an integrative radiopathomics model that combines radiological information from pretreatment MRI and pathological information from WSI for predicting NPC prognosis. The radiopathomics model demonstrated superior discrimination of PFS and effectively stratified individual patients into distinct prognostic groups. Due to its non-invasive and effective nature, the radiopathomics model holds promise for prognosis prediction and risk stratification of NPC, facilitating individualized clinical decision-making.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Abbreviations

CET1-w Contrast-enhanced T1 weighted imaging

C-index Concordance index

EBV DNA Epstein-Barr virus DNA

IMRT Intensity-modulated radiation therapy

IQR Interquartile range

LASSO Least absolute shrinkage and selection operator

MRI Magnetic resonance imaging

mRMR Minimum redundancy maximum relevance

NPC Nasopharyngeal carcinoma

PFS Progression-free survival

TE Echo time

TNM Tumor-node-metastasis

T2WI T2 weighted imaging

TR Repetition time

ROI Region of interest

VOI Volume of interest

WSI Whole-slide image

Author contributions

J.H., X.Y., J.L. and X.Y. contributed to the conception and design of the study. X.Y., H.L., Q.L., J.L. and B.Z. contributed to the acquisition of data. J.H., H.L., J.L. and X.Y. contributed to data analysis and interpretation. J.H., H.L. contributed to statistical analyses. First drafting was done by J.H. and X.Y., B.Z., J.L. and X.Y. revised the manuscript. All authors have read and approved the final manuscript.

Funding

Our study was supported by Hunan Provinical Natural Science Foundation of China (project no. 2022JJ70105) and Changsha Municipal Natural Science Foundation (project no. kq2202469).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethical approval

This study was approved by the medial ethnical committee of Hunan Cancer Hospital (2014-8).

Consent to participate

Since this study was a retrospective study, written informed consent was waived.

Consent to publication

The authors affirm that human research participants provided informed consent for publication of the images in Fig. 2.

Competing interests

The authors declare no competing interests.

Publisher’s note

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

Alvarez-Jimenez C, Sandino AA, Prasanna P, Gupta A, Viswanath SE, Romero E (2020) Identifying Cross-scale associations between Radiomic and Pathomic signatures of Non-small Cell Lung Cancer subtypes: preliminary results. Cancers (Basel) 12. 10.3390/cancers12123663
Bologna M, Corino V, Calareso G, Tenconi C, Alfieri S, Iacovelli NA, Cavallo A, Cavalieri S, Locati L, Bossi P, Romanello DA, Ingargiola R, Rancati T, Pignoli E, Sdao S, Pecorilla M, Facchinetti N, Trama A, Licitra L, Mainardi L, Orlandi E (2020) Baseline MRI-Radiomics can predict overall survival in non-endemic EBV-Related nasopharyngeal carcinoma patients. Cancers (Basel) 12. 10.3390/cancers12102958
Bray F Ferlay J Soerjomataram I Siegel RL Torre LA Jemal A Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries CA Cancer J Clin 2018 68 394 424 10.3322/caac.21492 30207593
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394–424. 10.3322/caac.2149230207593 10.3322/caac.21492
Chen F-P Lin L Qi Z-Y Zhou G-Q Guo R Hu J Lin A-H Ma J Sun Y Pretreatment nomograms for local and Regional Recurrence after Radical Radiation Therapy for Primary Nasopharyngeal Carcinoma J Cancer 2017 8 2595 2603 10.7150/jca.20255 28900497
Chen F-P, Lin L, Qi Z-Y, Zhou G-Q, Guo R, Hu J, Lin A-H, Ma J, Sun Y (2017) Pretreatment nomograms for local and Regional Recurrence after Radical Radiation Therapy for Primary Nasopharyngeal Carcinoma. J Cancer 8:2595–2603. 10.7150/jca.2025528900497 10.7150/jca.20255
Chu LC Park S Kawamoto S Fouladi DF Shayesteh S Zinreich ES Graves JS Horton KM Hruban RH Yuille AL Kinzler KW Vogelstein B Fishman EK Utility of CT Radiomics Features in differentiation of pancreatic ductal adenocarcinoma from normal pancreatic tissue AJR Am J Roentgenol 2019 213 349 357 10.2214/AJR.18.20901 31012758
Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK (2019) Utility of CT Radiomics Features in differentiation of pancreatic ductal adenocarcinoma from normal pancreatic tissue. AJR Am J Roentgenol 213:349–357. 10.2214/AJR.18.2090131012758 10.2214/AJR.18.20901
Feng L Liu Z Li C Li Z Lou X Shao L Wang Y Huang Y Chen H Pang X Liu S He F Zheng J Meng X Xie P Yang G Ding Y Wei M Yun J Hung M-C Zhou W Wahl DR Lan P Tian J Wan X Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study Lancet Digit Health 2022 4 e8 e17 10.1016/S2589-7500(21)00215-6 34952679
Feng L, Liu Z, Li C, Li Z, Lou X, Shao L, Wang Y, Huang Y, Chen H, Pang X, Liu S, He F, Zheng J, Meng X, Xie P, Yang G, Ding Y, Wei M, Yun J, Hung M-C, Zhou W, Wahl DR, Lan P, Tian J, Wan X (2022) Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study. Lancet Digit Health 4:e8–e17. 10.1016/S2589-7500(21)00215-634952679 10.1016/S2589-7500(21)00215-6
Hatt M Tixier F Visvikis D Le Cheze Rest C Radiomics in PET/CT: more than meets the Eye? J Nucl Med 2017 58 365 366 10.2967/jnumed.116.184655 27811126
Hatt M, Tixier F, Visvikis D, Le Cheze Rest C (2017) Radiomics in PET/CT: more than meets the Eye? J Nucl Med 58:365–366. 10.2967/jnumed.116.18465527811126 10.2967/jnumed.116.184655
He S Wang Y Chen H Yang L Liang S Lu L Chen Y C-Reactive Protein/Albumin ratio (CAR) as a prognostic factor in patients with non-metastatic nasopharyngeal carcinoma J Cancer 2016 7 2360 2366 10.7150/jca.16443 27994675
He S, Wang Y, Chen H, Yang L, Liang S, Lu L, Chen Y (2016) C-Reactive Protein/Albumin ratio (CAR) as a prognostic factor in patients with non-metastatic nasopharyngeal carcinoma. J Cancer 7:2360–2366. 10.7150/jca.1644327994675 10.7150/jca.16443
Kather JN Pearson AT Halama N Jäger D Krause J Loosen SH Marx A Boor P Tacke F Neumann UP Grabsch HI Yoshikawa T Brenner H Chang-Claude J Hoffmeister M Trautwein C Luedde T Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer Nat Med 2019 25 1054 1056 10.1038/s41591-019-0462-y 31160815
Kather JN, Pearson AT, Halama N, Jäger D, Krause J, Loosen SH, Marx A, Boor P, Tacke F, Neumann UP, Grabsch HI, Yoshikawa T, Brenner H, Chang-Claude J, Hoffmeister M, Trautwein C, Luedde T (2019) Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med 25:1054–1056. 10.1038/s41591-019-0462-y31160815 10.1038/s41591-019-0462-y
Kuo MD Jamshidi N Behind the numbers: decoding molecular phenotypes with radiogenomics–guiding principles and technical considerations Radiology 2014 270 320 325 10.1148/radiol.13132195 24471381
Kuo MD, Jamshidi N (2014) Behind the numbers: decoding molecular phenotypes with radiogenomics–guiding principles and technical considerations. Radiology 270:320–325. 10.1148/radiol.1313219524471381 10.1148/radiol.13132195
Lambrechts D Wauters E Boeckx B Aibar S Nittner D Burton O Bassez A Decaluwé H Pircher A van den Eynde K Weynand B Verbeken E de Leyn P Liston A Vansteenkiste J Carmeliet P Aerts S Thienpont B Phenotype molding of stromal cells in the lung tumor microenvironment Nat Med 2018 24 1277 1289 10.1038/s41591-018-0096-5 29988129
Lambrechts D, Wauters E, Boeckx B, Aibar S, Nittner D, Burton O, Bassez A, Decaluwé H, Pircher A, van den Eynde K, Weynand B, Verbeken E, de Leyn P, Liston A, Vansteenkiste J, Carmeliet P, Aerts S, Thienpont B (2018) Phenotype molding of stromal cells in the lung tumor microenvironment. Nat Med 24:1277–1289. 10.1038/s41591-018-0096-529988129 10.1038/s41591-018-0096-5
Lee S, Choi Y, Seo M-K, Jang J, Shin N-Y, Ahn K-J, Kim B-S (2022) Magnetic resonance imaging-based Radiomics for the prediction of progression-free survival in patients with Nasopharyngeal Carcinoma: a systematic review and Meta-analysis. Cancers (Basel) 14. 10.3390/cancers14030653
Liu K Xia W Qiang M Chen X Liu J Guo X Lv X Deep learning pathological microscopic features in endemic nasopharyngeal cancer: prognostic value and protentional role for individual induction chemotherapy Cancer Med 2020 9 1298 1306 10.1002/cam4.2802 31860791
Liu K, Xia W, Qiang M, Chen X, Liu J, Guo X, Lv X (2020) Deep learning pathological microscopic features in endemic nasopharyngeal cancer: prognostic value and protentional role for individual induction chemotherapy. Cancer Med 9:1298–1306. 10.1002/cam4.280231860791 10.1002/cam4.2802
Ono T Azuma K Kawahara A Sasada T Matsuo N Kakuma T Kamimura H Maeda R Hattori C On K Nagata K Sato F Chitose S-I Shin B Aso T Akiba J Umeno H Prognostic stratification of patients with nasopharyngeal carcinoma based on tumor immune microenvironment Head Neck 2018 40 2007 2019 10.1002/hed.25189 29756253
Ono T, Azuma K, Kawahara A, Sasada T, Matsuo N, Kakuma T, Kamimura H, Maeda R, Hattori C, On K, Nagata K, Sato F, Chitose S-I, Shin B, Aso T, Akiba J, Umeno H (2018) Prognostic stratification of patients with nasopharyngeal carcinoma based on tumor immune microenvironment. Head Neck 40:2007–2019. 10.1002/hed.2518929756253 10.1002/hed.25189
Pan JJ Ng WT Zong JF Chan LLK O’Sullivan B Lin SJ Sze HCK Chen YB Choi HCW Guo QJ Kan WK Xiao YP Wei X Le QT Glastonbury CM Colevas AD Weber RS Shah JP Lee AWM Proposal for the 8th edition of the AJCC/UICC staging system for nasopharyngeal cancer in the era of intensity-modulated radiotherapy Cancer 2016 122 546 558 10.1002/cncr.29795 26588425
Pan JJ, Ng WT, Zong JF, Chan LLK, O’Sullivan B, Lin SJ, Sze HCK, Chen YB, Choi HCW, Guo QJ, Kan WK, Xiao YP, Wei X, Le QT, Glastonbury CM, Colevas AD, Weber RS, Shah JP, Lee AWM (2016) Proposal for the 8th edition of the AJCC/UICC staging system for nasopharyngeal cancer in the era of intensity-modulated radiotherapy. Cancer 122:546–558. 10.1002/cncr.2979526588425 10.1002/cncr.29795
Pantanowitz L Quiroga-Garza GM Bien L Heled R Laifenfeld D Linhart C Sandbank J Albrecht Shach A Shalev V Vecsler M Michelow P Hazelhurst S Dhir R An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study Lancet Digit Health 2020 2 e407 e416 10.1016/S2589-7500(20)30159-X 33328045
Pantanowitz L, Quiroga-Garza GM, Bien L, Heled R, Laifenfeld D, Linhart C, Sandbank J, Albrecht Shach A, Shalev V, Vecsler M, Michelow P, Hazelhurst S, Dhir R (2020) An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study. Lancet Digit Health 2:e407–e416. 10.1016/S2589-7500(20)30159-X33328045 10.1016/S2589-7500(20)30159-X
Pfister DG Spencer S Adelstein D Adkins D Anzai Y Brizel DM Bruce JY Busse PM Caudell JJ Cmelak AJ Colevas AD Eisele DW Fenton M Foote RL Galloway T Gillison ML Haddad RI Hicks WL Hitchcock YJ Jimeno A Leizman D Maghami E Mell LK Mittal BB Pinto HA Ridge JA Rocco JW Rodriguez CP Shah JP Weber RS Weinstein G Witek M Worden F Yom SS Zhen W Burns JL Darlow SD Head and Neck cancers, Version 2.2020, NCCN Clinical Practice guidelines in Oncology J Natl Compr Canc Netw 2020 18 873 898 10.6004/jnccn.2020.0031 32634781
Pfister DG, Spencer S, Adelstein D, Adkins D, Anzai Y, Brizel DM, Bruce JY, Busse PM, Caudell JJ, Cmelak AJ, Colevas AD, Eisele DW, Fenton M, Foote RL, Galloway T, Gillison ML, Haddad RI, Hicks WL, Hitchcock YJ, Jimeno A, Leizman D, Maghami E, Mell LK, Mittal BB, Pinto HA, Ridge JA, Rocco JW, Rodriguez CP, Shah JP, Weber RS, Weinstein G, Witek M, Worden F, Yom SS, Zhen W, Burns JL, Darlow SD (2020) Head and Neck cancers, Version 2.2020, NCCN Clinical Practice guidelines in Oncology. J Natl Compr Canc Netw 18:873–898. 10.6004/jnccn.2020.003132634781 10.6004/jnccn.2020.0031
Saednia K Lagree A Alera MA Fleshner L Shiner A Law E Law B Dodington DW Lu F-I Tran WT Sadeghi-Naini A Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies Sci Rep 2022 12 9690 10.1038/s41598-022-13917-4 35690630
Saednia K, Lagree A, Alera MA, Fleshner L, Shiner A, Law E, Law B, Dodington DW, Lu F-I, Tran WT, Sadeghi-Naini A (2022) Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies. Sci Rep 12:9690. 10.1038/s41598-022-13917-435690630 10.1038/s41598-022-13917-4
Shao L Liu Z Feng L Lou X Li Z Zhang X-Y Wan X Zhou X Sun K Zhang D-F Wu L Yang G Sun Y-S Xu R Fan X Tian J Multiparametric MRI and whole slide image-based pretreatment prediction of pathological response to Neoadjuvant Chemoradiotherapy in rectal Cancer: a Multicenter Radiopathomic Study Ann Surg Oncol 2020 27 4296 4306 10.1245/s10434-020-08659-4 32729045
Shao L, Liu Z, Feng L, Lou X, Li Z, Zhang X-Y, Wan X, Zhou X, Sun K, Zhang D-F, Wu L, Yang G, Sun Y-S, Xu R, Fan X, Tian J (2020) Multiparametric MRI and whole slide image-based pretreatment prediction of pathological response to Neoadjuvant Chemoradiotherapy in rectal Cancer: a Multicenter Radiopathomic Study. Ann Surg Oncol 27:4296–4306. 10.1245/s10434-020-08659-432729045 10.1245/s10434-020-08659-4
Skrede O-J de Raedt S Kleppe A Hveem TS Liestøl K Maddison J Askautrud HA Pradhan M Nesheim JA Albregtsen F Farstad IN Domingo E Church DN Nesbakken A Shepherd NA Tomlinson I Kerr R Novelli M Kerr DJ Danielsen HE Deep learning for prediction of colorectal cancer outcome: a discovery and validation study Lancet 2020 395 350 360 10.1016/S0140-6736(19)32998-8 32007170
Skrede O-J, de Raedt S, Kleppe A, Hveem TS, Liestøl K, Maddison J, Askautrud HA, Pradhan M, Nesheim JA, Albregtsen F, Farstad IN, Domingo E, Church DN, Nesbakken A, Shepherd NA, Tomlinson I, Kerr R, Novelli M, Kerr DJ, Danielsen HE (2020) Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet 395:350–360. 10.1016/S0140-6736(19)32998-832007170 10.1016/S0140-6736(19)32998-8
Spadarella G Calareso G Garanzini E Ugga L Cuocolo A Cuocolo R MRI based radiomics in nasopharyngeal cancer: systematic review and perspectives using radiomic quality score (RQS) assessment Eur J Radiol 2021 140 109744 10.1016/j.ejrad.2021.109744 33962253
Spadarella G, Calareso G, Garanzini E, Ugga L, Cuocolo A, Cuocolo R (2021) MRI based radiomics in nasopharyngeal cancer: systematic review and perspectives using radiomic quality score (RQS) assessment. Eur J Radiol 140:109744. 10.1016/j.ejrad.2021.10974433962253 10.1016/j.ejrad.2021.109744
Travis WD Brambilla E Riely GJ New pathologic classification of lung cancer: relevance for clinical practice and clinical trials J Clin Oncol 2013 31 992 1001 10.1200/JCO.2012.46.9270 23401443
Travis WD, Brambilla E, Riely GJ (2013) New pathologic classification of lung cancer: relevance for clinical practice and clinical trials. J Clin Oncol 31:992–1001. 10.1200/JCO.2012.46.927023401443 10.1200/JCO.2012.46.9270
Wang Y-Q Chen Y-P Zhang Y Jiang W Liu N Yun J-P Sun Y He Q-M Tang X-R Wen X Yang X-J Zhang P-P Zhang J Lei Y Li Y-Q Ma J Prognostic significance of tumor-infiltrating lymphocytes in nondisseminated nasopharyngeal carcinoma: a large-scale cohort study Int J Cancer 2018 142 2558 2566 10.1002/ijc.31279 29377121
Wang Y-Q, Chen Y-P, Zhang Y, Jiang W, Liu N, Yun J-P, Sun Y, He Q-M, Tang X-R, Wen X, Yang X-J, Zhang P-P, Zhang J, Lei Y, Li Y-Q, Ma J (2018) Prognostic significance of tumor-infiltrating lymphocytes in nondisseminated nasopharyngeal carcinoma: a large-scale cohort study. Int J Cancer 142:2558–2566. 10.1002/ijc.3127929377121 10.1002/ijc.31279
Wu S Li H Dong A Tian L Ruan G Liu L Shao Y Differences in Radiomics Signatures between Patients with early and advanced T-Stage Nasopharyngeal Carcinoma Facilitate Prognostication J Magn Reson Imaging 2021 54 854 865 10.1002/jmri.27633 33830573
Wu S, Li H, Dong A, Tian L, Ruan G, Liu L, Shao Y (2021) Differences in Radiomics Signatures between Patients with early and advanced T-Stage Nasopharyngeal Carcinoma Facilitate Prognostication. J Magn Reson Imaging 54:854–865. 10.1002/jmri.2763333830573 10.1002/jmri.27633
Yang K Tian J Zhang B Li M Xie W Zou Y Tan Q Liu L Zhu J Shou A Li G A multidimensional nomogram combining overall stage, dose volume histogram parameters and radiomics to predict progression-free survival in patients with locoregionally advanced nasopharyngeal carcinoma Oral Oncol 2019 98 85 91 10.1016/j.oraloncology.2019.09.022 31569054
Yang K, Tian J, Zhang B, Li M, Xie W, Zou Y, Tan Q, Liu L, Zhu J, Shou A, Li G (2019) A multidimensional nomogram combining overall stage, dose volume histogram parameters and radiomics to predict progression-free survival in patients with locoregionally advanced nasopharyngeal carcinoma. Oral Oncol 98:85–91. 10.1016/j.oraloncology.2019.09.02231569054 10.1016/j.oraloncology.2019.09.022
You R Liu Y-P Lin M Huang P-Y Tang L-Q Zhang Y-N Pan Y Liu W-L Guo W-B Zou X Zhao K-M Kang T Liu L-Z Lin A-H Hong M-H Mai H-Q Zeng M-S Chen M-Y Relationship of circulating tumor cells and Epstein-Barr virus DNA to progression-free survival and overall survival in metastatic nasopharyngeal carcinoma patients Int J Cancer 2019 145 2873 2883 10.1002/ijc.32380 31044420
You R, Liu Y-P, Lin M, Huang P-Y, Tang L-Q, Zhang Y-N, Pan Y, Liu W-L, Guo W-B, Zou X, Zhao K-M, Kang T, Liu L-Z, Lin A-H, Hong M-H, Mai H-Q, Zeng M-S, Chen M-Y (2019) Relationship of circulating tumor cells and Epstein-Barr virus DNA to progression-free survival and overall survival in metastatic nasopharyngeal carcinoma patients. Int J Cancer 145:2873–2883. 10.1002/ijc.3238031044420 10.1002/ijc.32380
Zhang B Tian J Di Dong, Gu D Dong Y Zhang L Lian Z Liu J Luo X Pei S Mo X Huang W Ouyang F Guo B Liang L Chen W Liang C Zhang S Radiomics Features of Multiparametric MRI as Novel prognostic factors in Advanced Nasopharyngeal Carcinoma Clin Cancer Res 2017 23 4259 4269 10.1158/1078-0432.CCR-16-2910 28280088
Zhang B, Tian J, Di Dong, Gu D, Dong Y, Zhang L, Lian Z, Liu J, Luo X, Pei S, Mo X, Huang W, Ouyang F, Guo B, Liang L, Chen W, Liang C, Zhang S (2017) Radiomics Features of Multiparametric MRI as Novel prognostic factors in Advanced Nasopharyngeal Carcinoma. Clin Cancer Res 23:4259–4269. 10.1158/1078-0432.CCR-16-291028280088 10.1158/1078-0432.CCR-16-2910
Zhang F Zhong L-Z Zhao X Di Dong, Yao J-J Wang S-Y Liu Y Zhu D Wang Y Wang G-J Wang Y-M Li D Wei J Tian J Shan H A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study Ther Adv Med Oncol 2020 12 1758835920971416 10.1177/1758835920971416 33403013
Zhang F, Zhong L-Z, Zhao X, Di Dong, Yao J-J, Wang S-Y, Liu Y, Zhu D, Wang Y, Wang G-J, Wang Y-M, Li D, Wei J, Tian J, Shan H (2020) A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study. Ther Adv Med Oncol 12:1758835920971416. 10.1177/175883592097141633403013 10.1177/1758835920971416
