
==== Front
BMC Oral Health
BMC Oral Health
BMC Oral Health
1472-6831
BioMed Central London

39300434
4849
10.1186/s12903-024-04849-8
Research
Deep radiomics-based prognostic prediction of oral cancer using optical coherence tomography
Yuan Wei 1
Rao Jiayi 1
Liu Yanbin 2
Li Sen 3
Qin Lizheng qinlizheng@ccmu.edu.cn

1
Huang Xin huangxin@ccmu.edu.cn

1
1 https://ror.org/013xs5b60 grid.24696.3f 0000 0004 0369 153X Department of Oral and Maxillofacial & Head and Neck Oncology, Beijing Stomatological Hospital, Capital Medical University, Beijing, 100050 China
2 https://ror.org/013xs5b60 grid.24696.3f 0000 0004 0369 153X Department of Dental Implant Center, Beijing Stomatological Hospital, Capital Medical University, Beijing, 100050 China
3 grid.19373.3f 0000 0001 0193 3564 School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055 Guangdong China
19 9 2024
19 9 2024
2024
24 111711 1 2024
2 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

This study aims to evaluate the integration of optical coherence tomography (OCT) and peripheral blood immune indicators for predicting oral cancer prognosis by artificial intelligence.

Methods

In this study, we examined patients undergoing radical oral cancer resection and explored inherent relationships among clinical data, OCT images, and peripheral immune indicators for oral cancer prognosis. We firstly built a peripheral blood immune indicator-guided deep learning feature representation method for OCT images, and further integrated a multi-view prognostic radiomics model incorporating feature selection and logistic modeling. Thus, we can assess the prognostic impact of each indicator on oral cancer by quantifying OCT features.

Results

We collected 289 oral mucosal samples from 68 patients, yielding 1,445 OCT images. Using our deep radiomics-based prognosis model, it achieved excellent discrimination for oral cancer prognosis with the area under the receiver operating characteristic curve (AUC) of 0.886, identifying systemic immune-inflammation index (SII) as the most informative feature for prognosis prediction. Additionally, the deep learning model also performed excellent results with 85.26% accuracy and 0.86 AUC in classifying the SII risk.

Conclusions

Our study effectively merged OCT imaging with peripheral blood immune indicators to create a deep learning-based model for inflammatory risk prediction in oral cancer. Additionally, we constructed a comprehensive multi-view radiomics model that utilizes deep learning features for accurate prognosis prediction. The study highlighted the significance of the SII as a crucial indicator for evaluating patient outcomes, corroborating our clinical statistical analyses. This integration underscores the potential of combining imaging and blood indicators in clinical decision-making.

Trial registration

The clinical trial associated with this study was prospectively registered in the Chinese Clinical Trial Registry with the trial registration number (TRN) ChiCTR2200064861. The registration was completed on 2021.

Keywords

Optical coherence tomography
Oral cancer
Prognostic prediction
Deep learning
Peripheral blood immune indicators
the Clinical Technology Innovation Project of Beijing Hospital Management CenterXMLX202123 the Capital’s Funds for Health Improvement and ResearchCFH 202-2-2141 issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcBackground

Oral cancer is one of the most common malignant tumors worldwide. According to recent statistics, there are over 300,000 new cases of oral cancer reported annually globally [1]. Among these cases, oral squamous cell carcinoma (OSCC) accounts for approximately 90% of all oral cancer cases [2]. Due to the unique anatomy of the oral cavity, oral cancer often severely affects important functions such as chewing, swallowing, speech, and breathing, posing a threat to the quality of life and even survival of patients. Oral cancer has a high malignant potential, prone to recurrence and metastasis, with a relatively lower 5-year survival rate ranging from 50–70% [3]. Therefore, investigating prognostic factors for oral cancer is of significant value in formulating rational individualized treatment plans.

Previous studies have demonstrated that inflammatory responses occur in both the tumor microenvironment and systemic circulation, and are associated with the occurrence, progression, and prognosis of various cancers. Immunomediators play a crucial role in multiple aspects of cancer progression, including tumor initiation, growth, invasion, and metastasis [4]. Studies have confirmed a close correlation between circulating blood cell counts (such as neutrophils, lymphocytes, and platelets) and levels of circulating inflammation-related proteins (such as C-reactive protein and interleukins) with tumor occurrence and progression [5, 6]. Peripheral blood immune indicators include platelets, neutrophils, lymphocytes, and monocytes. Neutrophils promote tumor progression through matrix degradation, immunomodulation, and facilitation of tumor cell proliferation and angiogenesis [7]. Platelets can protect circulating tumor cells from T-cell-mediated cytotoxicity, while tumor cells increase and activate platelet numbers by releasing platelet-generating factors and platelet activators, thereby promoting tumor development [8]. Lymphocytes play a critical role in tumor defense through inducing cell death, inhibiting tumor cell proliferation and migration, among other mechanisms [9].

Although peripheral blood immune indicators may undergo changes during the occurrence and progression of tumors, these markers are not specific to oral cancer. Other diseases or factors can also lead to alterations in inflammatory responses. Therefore, relying solely on these indicators cannot provide precise prognostic assessment for specific tumors [10, 11]. Moreover, the extraction of patient blood samples for routine blood tests is an invasive procedure that increases patient discomfort. Therefore, when using preoperative peripheral blood immune indicators for prognostic assessment of oral cancer, it is necessary to consider various clinical and biological factors comprehensively and incorporate them as part of a predictive model rather than relying solely on these markers.

Optical coherence tomography (OCT) is a non-invasive, high-resolution optical imaging technique that provides microscopic structural information of tissues [12]. By generating three-dimensional (3D) images of tissues, OCT can be used to assess the biological characteristics of tumors, such as tumor thickness, invasion depth, and vascular density [13]. Besides, these imaging parameters provide vital information for prognostic assessment of tumor patients and have been extensively researched and applied in clinical practice [14–16]. The remarkable advantages of OCT imaging have led to the utilization of deep learning techniques in the analysis of OCT data for oral cancer diagnosis [17, 18]. To illustrate, Yuan et al. devised an artificial intelligence (AI) approach centered around a local residual adaptation network to enable oral diagnosis using OCT images [17]. Subsequently, they introduced a more sophisticated deep learning model incorporating attention mechanisms [18]. These methodologies have exhibited superior performance in the diagnosis of OSCC when compared to expert specialists. Such endeavors clearly demonstrate the exceptional capabilities of deep learning in oral cancer diagnosis and underscore the potential value of noninvasive screening through OCT imaging in providing invaluable insights into oral cancers. In light of this, the present study aims to utilize deep learning techniques to comprehensively explore the correlations between prognostic assessment and OCT imaging in the field of oral oncology. Additionally, peripheral blood inflammatory indicator data will be integrated to further enhance the research potential and practical applications of this study.

The effective integration of peripheral blood inflammatory indicator data with OCT is of paramount importance in the establishment of a comprehensive prognostic assessment model for oral cancer from multiple perspectives and levels, aiming to improve the diagnosis and treatment of oral cancer using OCT [19]. Therefore, it is crucial to develop an OCT and inflammatory indicator fusion analysis model for intelligent diagnosis and treatment of oral cancer, which can identify low-risk patients who can benefit from reduced overtreatment. This approach addresses the significant clinical demand for responsive applications in the field.

As analyzed above, this study prospectively exploits the clinical and OCT data of patients with oral cancer who underwent radical resection, aiming to evaluate the predictive value of OCT for the prognosis of oral cancer based on peripheral blood immune indicators. Specifically, this prospective study explores the clinical and OCT data of patients who underwent radical resection for oral cancer. The aim is to evaluate the prognostic value of OCT in oral cancer by examining peripheral blood immune indicators. The study includes 68 patients who underwent 3D OCT imaging and were tested for peripheral blood immune indicators, such as platelet count multiplied by neutrophil count divided by lymphocyte count (systemic immune-inflammation index, SII), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte ratio (NLR) [20, 21]. Follow-up data is collected to assess the patients’ prognosis. Three OCT-based deep learning models, employing a Mixed Convolution 3D (MC3) network with 18 residual layers, are developed to extract deep learning features guided by the inflammatory indicators. The models predict the risk grading of peripheral blood immune indicators. Furthermore, a multi-view radiomics model is designed to explore the relationships between inflammatory indicators and prognosis. This model combines the deep learning features from three inflammatory indicators into the radiomics model, enabling the prediction of prognosis for individual patients. The radiomics model undergoes feature selection analysis to determine the relative importance of each indicator in predicting prognosis. This research introduces a deep learning-based 3D OCT feature learning approach for noninvasive prediction of inflammatory risk and also presents a multi-view radiomics model to assess the contribution of each indicator to the prognosis of oral cancer.

Materials

Study population

This study was designed as a prospective preliminary study to evaluate the feasibility of OCT for prognostic prediction in patients with oral cancer. The study was approved by the Institutional Review Board. Written informed consent was obtained from all participants. Inclusion Criteria: (1) Age above 18 years, regardless of gender; (2) Confirmed diagnosis of oral cancer through pathological biopsy; (3) Patients undergoing surgical treatment during hospitalization; (4) No previous history of chemotherapy, radiotherapy, or other tumor treatments. Exclusion Criteria: (1) Patients with severe underlying diseases unable to tolerate surgery; (2) Patients with mental disorders; (3) Patients with recurrent tumors; (4) Patients who have applied topical medications to oral mucosal lesions prior to surgery; (5) Female patients who are pregnant or breastfeeding.

Research design

The primary lesion of oral cancer, which was surgically excised, was initially obtained by the pathologist for pathological diagnosis. The researchers then collected specimens from the remaining discarded tumor tissues. These specimens included cancer tissue, cancer-adjacent tissue (1 cm adjacent to the cancerous tissue), and normal tissue (beyond 2 cm or at the farthest location from the cancerous tissue). One to two specimens were taken from each location, with a sample size of 0.2 cm × 0.2 cm. The specimens were placed in specimen cups filled with physiological saline for OCT scanning. During the scanning process, the oral mucosal epithelium was positioned towards the objective lens, and OCT imaging was performed at multiple locations on the surface of the oral tissue. The scanning parameters were set as follows: all scans were performed in a 3D mode, scanning direction was from left to right, A-scan was set at 800, B-scan was set at 200, X-range was 1.0 mm, and Y-range was 0.5 mm.

All patients were followed up postoperatively through outpatient visits, hospital admissions, and telephone contacts. The observation endpoints were tumor recurrence and/or metastasis, defined as the period from the date of surgery to the date of tumor recurrence and/or metastasis. Patients without recurrence or metastasis were followed up until their last recorded visit, with the final follow-up date set as August 31, 2023. The follow-up duration was measured in months.

The objective of this section was to evaluate the prognostic utility of preoperative peripheral blood immune indicators in predicting clinical outcomes, utilizing a convenience sampling methodology. Hematological parameters were collected using ethylenediamine tetraacetic acid dipotassium anticoagulant tubes within one week prior to surgery. Fasting morning venous blood (2 mL) was obtained from the elbow and immediately inverted to mix thoroughly for routine blood tests. The following indices were calculated: SII, PLR, NLR.

OCT imaging system

The system used in this study is a high-resolution OCT imaging system consisting of a superluminescent diode (Superlum, cBLMD-T-850-HP, Ireland) with a central wavelength of 850 nm and a 3 dB spectral width exceeding 155 nm. The axial resolution within the tissue is approximately 1.5 μm. As shown in Fig. 1, the spectrum from the light source is split evenly between the sample arm and the reference arm. The sample arm utilizes a 10X objective lens to achieve a penetration depth of approximately 2–3 mm and a lateral resolution of approximately 3 μm. The scattered light from the sample arm interferes with the reference arm’s coherent light and is detected by a spectrometer (Wasatch Photonics, North Carolina, USA). The longitudinal scanning rate of this system is approximately 80,000 A-scans per second.

Fig. 1 Summary of OCT imaging procedures. (C) Diagram of the ultrahigh-resolution OCT imaging system utilized in the study. (AB) Intraoral and ex vivo gross images of oral cancer specimens. OCT imaging locations were indicated on the gross tissue image using blue dots (a-c). (D-F) Representative OCT cross sections of a cancerous region (a), an area of epithelial dysplasia (b), and a benign region (c) corresponding to the locations depicted in (B)

Deep learning feature extraction

As shown in Fig. 2 (Step 1), inflammatory risk prediction and corresponding deep learning feature extraction were performed using a MC3 network [22]. The MC3 network is an innovative architecture that combines spatial and temporal information from 3D data. By incorporating mixed convolutions as its core building block, the MC3 network enables joint extraction of spatial and temporal features, effectively capturing local spatio-temporal patterns. This design facilitates robust feature representations and enhances discrimination power. The network comprises multiple layers, including 3D convolutional layers, pooling layers, and fully connected layers. Figure 3 illustrates the detailed architecture. The use of 3D convolutional layers allows for the efficient capture of spatial dependencies within the input data, while pooling layers reduce dimensionality and extract key information. Fully connected layers enable the network to learn higher-level representations and make predictions based on the extracted features. To tackle the inflammatory risk prediction task, three individual MC3 networks, namely SII, PLR, and NLR, were employed to learn Inflammatory-related feature representations. Each network was trained separately using the Cross-Entropy Loss with their respective inflammatory risk labels. From the fully connected layers in each MC3 network, 512-dimensional feature vectors were extracted. These feature vectors from the three networks were concatenated to form the 1,536-D input for our prognosis radiomics model.

Fig. 2 The inflammatory risk prediction and prognosis model construction based on the obtained OCT images

Fig. 3 The architecture of the MC3-based deep learning network

To ensure the reproducibility of this study, we hereby provide detailed information regarding the parameters and configuration of the MC3 network used in our deep radiomics model for the prognostic prediction of oral cancer. The model was implemented under the Pytorch framework and operated on an Nvidia RTX 4090 GPU. To accommodate the network input requirements, we initially resized the input images of OCT for oral cancer to 224 × 224 pixels. During the model training process, we set the learning rate to 0.00001 and the batch size to 8 to optimize the learning efficiency and memory utilization of the model. Moreover, to enhance the model’s generalization capability, we employed various data augmentation techniques, including image rotation and random horizontal flips. These augmentation techniques assist the model in learning to recognize features of oral cancer from different angles and directions, thereby improving the accuracy of prognosis prediction. We selected for the Adam optimizer to adjust the network weights because the Adam optimizer combines the advantages of momentum and adaptive learning rates, enabling faster convergence during the training of deep learning models and improving performance on the validation set. Throughout the training process, we conducted a total of 100 epochs to ensure the model thoroughly learned the features within the dataset and achieved stable predictive performance.

Overall, the utilization of the MC3 network, with its mixed convolutional architecture, allows for the effective integration of spatial and temporal information, enabling robust feature extraction and enhancing the discriminatory power for inflammatory risk prediction. The extracted features from the three individual MC3 networks are then combined to construct a comprehensive prognosis radiomics model. For the reproducibility of this work, the source code of our deep learning models has been released in GitHub (https://github.com/Wei-yuan0919/DRPP).

Radiomics model reconstruction

As shown in Fig. 2 (Step 2), feature selection and construction of the radiomics model were performed after the concatenation of deep feature representations. The objective of the radiomics model is to accurately predict prognosis results by capturing inherent patterns in the 1,536-D Inflammatory-guided OCT features. Specifically, we standardized the concatenated features to transform them into a range of (0,1). Subsequently, we employed the Select From Model (SFM) feature selection method based on Support Vector Machine (SVM), which identified important features based on their importance weights and selected 84 representative features. Next, we divided the 380 OCT image features into training and testing cohorts in an 8:2 ratio. Following the SFM method, we constructed a logistic regression model with an elastic net penalty and an l1_ratio of 0.1 to predict the prognosis result for each OCT image. During modeling, we recorded the importance weights of the selected features determined by SFM. These weights can be utilized to analyze the contributions of each inflammatory factor on prognosis based on OCT imaging. The workflow of the developed Inflammatory-guided deep radiomics model is illustrated in Fig. 2.

Statistical analysis

The analysis was conducted utilizing IBM SPSS software and GraphPad Prism 8. Continuous variables with normal distribution are presented as mean ± standard deviation, while those with skewed distribution are presented as median (interquartile range). Categorical variables are reported as counts and percentages. To evaluate survival outcomes, univariate survival analysis was performed using the Kaplan-Meier method in conjunction with the Log-rank test. This methodology facilitated the estimation of survival probabilities and the comparison of survival distributions across different patient cohorts. Survival curves were generated using GraphPad Prism 8. For a more comprehensive assessment, multivariable survival analysis was executed using the Cox proportional hazards regression model. This approach allowed for the identification of independent prognostic factors while adjusting for potential confounders, thereby enhancing the precision of survival outcome predictions. The receiver operating characteristic (ROC) curve was employed to determine the area under the curve (AUC), which was used to identify the optimal cutoff value and evaluate the sensitivity and specificity of oral cancer prognosis. P < 0.05 was considered statistically significant.

Results

Clinical details of the study population

This study included a cohort of 83 patients diagnosed with oral cancer who received treatment at the Department of Oral and Maxillofacial & Head and Neck Oncology, Beijing Stomatological Hospital, Capital Medical University, between January 2021 and February 2023. By the cut-off date of August 31, 2023, fifteen patients were excluded due to loss to follow-up or deaths attributed to causes not related to the primary condition under study. Consequently, the final analysis was conducted on a remaining cohort of 68 subjects. Among them, there were 39 males (57.35%) and 29 females (42.65%), with a mean age of 58.5 ± 12.3 years. All 68 patients underwent curative resection surgery, with 5 patients receiving chemotherapy, 11 patients receiving radiotherapy, and 9 patients receiving combined chemotherapy and radiotherapy postoperatively. During the follow-up period, tumor recurrence and/or metastasis occurred in 12 cases, while 56 cases showed no progression. OCT was utilized for imaging oral mucosal specimens. A total of 289 ex vivo specimens from 68 patients were collected, yielding 1,485 OCT/OCM image datasets. However, due to issues with image quality, such as irregularities on the mucosal surfaces and artifacts induced by saliva, the final dataset was refined to include 1,445 OCT/OCM image datasets. On average, five datasets were scanned for each specimen, with each OCT/OCM dataset comprising over 100 images.

Preoperative assessment of peripheral blood immune indicators for patient prognosis

The AUC for SII, PLR, and NLR in assessing the prognosis of oral cancer patients were 78.1% (95% CI: 0.646–0.917), 73.2% (95% CI: 0.551–0.913), and 78.9% (95% CI: 0.644–0.935), respectively (Fig. 4). The optimal cutoff values were determined to be 477.53, 156.83, and 2.31 for SII, PLR, and NLR, respectively. Patients were classified into two groups based on these cutoff values: SII ≤ 477.53 group and SII > 477.53 group, PLR ≤ 156.83 group and PLR > 156.83 group, NLR ≤ 2.31 group and NLR > 2.31 group.

Fig. 4 ROC curve for the determination of the optimal cut-off value of SII, PLR, and NLR

Analysis of prognostic factors in oral cancer

Univariate analysis showed that SII, PLR, and NLR were significant risk factors affecting the prognosis of OSCC (P < 0.01) (Table 1). Multivariate analysis revealed that SII and NLR were independent prognostic factors (P < 0.05) (Table 2). The hazard ratios (HR) were 13.912 (95% CI: 1.015-190.772) for SII and 26.757 (95% CI: 2.330-307.344) for NLR, indicating that higher SII and NLR values are significantly associated with poorer prognosis. Specifically, patients with elevated SII exhibited a 13.912-fold higher risk of disease progression or mortality, whereas those with elevated NLR demonstrated a 26.757-fold higher risk compared to patients with lower values.

Table 1 Univariate analysis of prognosis in oral cancer patients

Factors	Cases (%)	Survival Time (Mean ± SD)	P-value	
Sex			0.884	
Male	39 (57.4)	28.738 ± 1.476	
Female	29 (42.6)	28.872 ± 2.076	
Age (years)			0.875	
≤ 55	26 (38.2)	29.057 ± 1.776		
> 55	42 (61.8)	29.208 ± 1.652		
SII			< 0.001	
≤ 477.53	37 (54.4)	33.714 ± 0.187		
> 477.53	31 (45.6)	22.012 ± 2.554		
PLR			< 0.001	
≤ 156.83	47 (69.1)	32.637 ± 0.772		
> 156.83	21 (30.9)	21.809 ± 2.806		
NLR			< 0.001	
≤ 2.31	44 (64.7)	33.417 ± 0.454		
> 2.31	24 (35.3)	20.843 ± 3.037		
Tumor Site				
Gingiva, Buccal mucosa, Palate	38 (55.9)	28.432 ± 1.625	0.742	
Tongue	30 (44.1)	29.19 ± 1.857		
Pathological Grade			0.743	
High, Intermediate	53 (77.9)	29.48 ± 1.382		
Moderate, Low	15 (22.1)	28.429 ± 2.338		
T Stage			0.389	
T1, T2	33 (48.5)	27.065 ± 1.953		
T3, T4	35 (51.5)	30.571 ± 1.51		
N Stage			0.598	
N0	40(58.8)	29.362 ± 1.457		
N1, N2	28(41.2)	28.799 ± 2.018		
Clinical Stage*			0.209	
I, II	23(33.8)	30.667 ± 1.57		
III, IV	45 (66.2)	28.629 ± 1.62		
*According to the eighth edition of UICC/AJCC staging system

Table 2 Multifactorial analysis of prognosis in oral cancer patients

Factors	Hazard Ratio (95% CI)	P-value	
SII (≤ 477.53/>477.53)	13.912 (1.015-190.772)	0.049	
PLR (≤ 156.83/>156.83)	2.867 (0.476–17.283)	0.250	
NLR (≤ 2.31/>2.31)	26.757 (2.330-307.344)	0.008	

Preoperative SII and NLR assessment of prognosis in oral cancer patients

Kaplan-Meier survival curves were plotted for the variables SII and NLR, respectively, demonstrating that the proportional hazards assumption was met (Fig. 5). Survival analysis revealed that the mean progression-free survival (PFS) period for patients in the preoperative SII ≤ 477.53 group (33.714 ± 0.187 months) was significantly greater than that for patients in the SII > 477.53 group (22.012 ± 2.554 months) (P < 0.001). Similarly, the mean PFS for patients in the preoperative NLR ≤ 2.13 group (33.417 ± 0.454 months) was significantly greater compared to the NLR > 2.13 group (20.843 ± 3.037 months) (P < 0.001).

Fig. 5 Survival curves of preoperative patients based on SII (A) and NLR (B)

Inflammatory risk prediction based on deep learning

Table 3 presents the diagnosis performance of the inflammatory risk prediction model based on deep learning for various inflammatory indicators in oral cavity diagnosis. The results demonstrate the accuracy, precision, recall, F1-score, and AUC values obtained for each classification task. For the SII classification task, an accuracy of 85.26% was achieved, indicating the overall correctness of the predictions made by the model. The precision, recall, and F1-score were found to be 84.82%, 84.34%, and 84.56% respectively, signifying the model’s ability to accurately identify and classify cases related to the SII. Additionally, the AUC was measured at 0.859, indicating a good discriminatory capability of the model (Fig. 6A). In the case of PLR grading, the model exhibited an accuracy of 89.47%. This suggests that the model demonstrated a high level of correctness in predicting cases associated with the PLR. The precision, recall, and F1-score were all consistent at 86.43%, demonstrating the model’s ability to effectively differentiate between positive and negative PLR cases. The AUC value of 0.855 further indicates the model’s strong discriminative performance (Fig. 6B). Similarly, for the NLR classification task, a high accuracy of 85.27% was achieved, implying that the model successfully predicted cases related to the NLR. The precision, recall, and F1-score were observed to be 84.82%, 84.33%, and 84.56% respectively, indicating the model’s robustness in differentiating between positive and negative NLR instances. Notably, the AUC value of 0.89 highlights the model’s exceptional discriminatory ability for NLR-related cases (Fig. 6C).

Table 3 The prediction results of inflammatory risk by deep learning

Task	Accuracy	Precision	Recall	F1-score	AUC	
SII	85.26%	84.82%	84.34%	84.56%	0.859	
PLR	89.47%	86.43%	86.43%	86.43%	0.855	
NLR	85.27%	84.82%	84.33%	84.56%	0.891	

Fig. 6 The ROC performance of deep learning models for SII (A), PLR (B), and NLR (C) predictions, along with the ROC performance of the prognosis model (D). The CI denotes confidence interval

These results reveal the effectiveness of the deep learning-based inflammatory risk prediction model in accurately classifying oral inflammatory indicators. The high accuracy, precision, and recall values achieved across all three diagnosis tasks demonstrate the model’s potential as a reliable tool for inflammatory risk assessment in oral health diagnosis. In summary, the results of this study provide support for the application of deep learning and AI techniques in the diagnosis of oral diseases. These advanced technologies offer valuable insights into inflammatory conditions and assist clinicians in creating personalized treatment strategies for patients.

Prognosis prediction results based on radiomics model

This section presents the results of our prognosis prediction model for oral cancer using radiomics features. We evaluate the performance of the model using ROC analysis and analyze the importance of selected features. Additionally, we highlight the significance of the SII in predicting patient prognosis, demonstrating its strong association with the model’s predictive ability. The clinical implications and value of this finding are also discussed.

The ROC curve for our radiomics model in predicting prognosis of oral cancer is shown in Fig. 6D, with an AUC value of 0.886. This indicates a high discriminatory power of the model in distinguishing between patients with different prognoses. Furthermore, we analyze the feature importance distribution of the selected features in the model, as depicted in Fig. 7. The analysis concludes the Top-30 features in selection step, including three main categories including SII, PLR and NLR. The cumulative scores for each category were SII = 1.115, PLR = 0.680, and NLR = 0.148, which indicates that SII had the highest contribution to the prognosis task. This signifies that the inflammatory indicator, SII, is strongly associated with the predictive performance of our model.

Fig. 7 The feature importance visualization in the prognosis model, along with individual total score of SII, PLR and NLR

The observed correlation between SII and prognosis prediction highlights the clinical relevance of monitoring systemic immune-inflammation responses in oral cancer patients. SII captures the combined effect of neutrophils, lymphocytes, and platelets, reflecting the dynamic interactions between tumor growth and host immune response. Therefore, the significant contribution of SII to the model’s predictive accuracy underlines its potential as a valuable indicator for prognosis assessment in clinical practice.

The utilization of SII as a prognostic indicator could aid clinicians in developing personalized treatment strategies and facilitating timely interventions for patients with poor prognoses. Additionally, its incorporation into existing prognostic models may improve their overall performance and guide clinical decision-making.

Discussion

Assessment of prognosis in oral cancer is a complex and critical aspect of clinical management [23]. The use of imaging evaluation has a significant role in predicting disease progression, lymph node metastasis, and tissue infiltration [24]. Although the TNM staging system based on imaging features aids in determining patient prognosis, its prognostic accuracy is limited [25]. Immunological characteristics also hold importance in prognostic assessment [26]. The presence of immune cell infiltration within the tumor microenvironment is closely associated with prognosis, with high levels of CD8 + T lymphocytes and PD-L1 expression correlating with better outcomes [27]. However, there are technical challenges related to the detection methods and standardization of immunological features, which currently hinder their widespread application in clinical practice.

AI technologies can analyze multidimensional data from patient evaluations, providing more accurate information for prognosis assessment, patient survival rates, and disease progression. Cepeda et al. developed a model based on MRI and intraoperative ultrasound elastography (IOUS-E) data, which uses a machine learning classifier to predict meningioma consistency parameters. This model demonstrated a 95% accuracy and 94% classification accuracy on preoperative diagnostic data from 18 patients [28]. Another study performed targeted metabolomics analysis on plasma samples from participants across multiple centers. The prognostic model derived from machine learning showed superior performance compared to traditional models using clinical parameters, effectively stratifying patients into different risk groups. This stratification aids in guiding precise clinical interventions and achieving accurate prognosis prediction for gastric cancer [29]. Using machine learning to analyze RNA sequencing data and clinical follow-up information from osteosarcoma patients, Liu et al. identified 125 pseudogenes associated with survival. Four pseudogenes were selected through Cox regression analysis and demonstrated the ability to effectively distinguish between high-risk and low-risk patients [30]. Compared to the Cox proportional hazards regression model, machine learning models have proven to be more effective tools for predicting patient survival rates. Borzooei et al. have developed and trained several models, including K-nearest neighbors, SVM, decision trees, random forests, and artificial neural networks, to predict the recurrence risk in thyroid cancer patients. Evaluating 13 clinicopathological features from 383 patients, the models demonstrated a high AUC value of 99.71 (SVM), highlighting the effectiveness of machine learning models in risk stratification, which can aid in developing personalized treatment and follow-up strategies [31]. The advancement of radiomics technology enables classifiers to accurately predict the overall survival of renal cell carcinoma patients, thereby assisting in identifying high-risk patients who may require additional treatments. Such models have demonstrated high AUC, accuracy, sensitivity, and specificity [32]. Moreover, the expression scoring of small nucleolar RNAs (snoRNAs) identified by machine learning serves as an effective prognostic predictor for head and neck cancer, enabling efficient stratification and management of diverse patient groups [33]. These advancements underscore the substantial potential and application value of AI technologies in the prediction of cancer prognosis. However, there remains a notable absence of well-defined guiding principles for decision-making in this domain at present [34]. Various challenges and unresolved issues remain in the comprehensive consideration of oral cancer prognosis [35].

In light of the continuous advancements in AI technology, the objective of this study was to evaluate the utility of noninvasive OCT imaging for prognostic prediction in patients with oral cancer, involving peripheral blood immune indicators to guide the model training. Through a prospective design, we enrolled 68 patients who underwent radical resection for oral cancer. Our study aimed to develop deep learning models for inflammatory risk-related feature representation and a multi-view radiomics model to assess the prognostic result for patients with oral cancer. In this section, we will discuss our findings and their implications in the context of oral cancer prognosis and personalized treatment.

To address the task mentioned above, this study was first to establish an end-to-end AI system for risk grading of peripheral blood immune indicators and prognostic analysis in oral cancer patients, which only utilize OCT images in clinical applications. We employed the MC3 network to construct OCT deep learning models for SII, PLR, and NLR predictions, respectively. Subsequently, we performed joint analysis of these three sets of deep learning features to develop a logistic regression radiomics model for patient prognosis. Additionally, we analyzed the importance of the three inflammation-guided feature sets in contributing to the prognostic task, aiming to interpret the predictive results of the logistic regression model and investigate the relationship between inflammatory influencing factors and patient prognosis. Based on the importance weights assigned to different features within the prognostic model, we further explored the impact of various inflammatory factors on corresponding treatment strategies. These findings have the potential to assist clinicians in making informed treatment decisions for patients with oral cancer. By leveraging peripheral blood immune indicators and OCT imaging, we successfully developed models that exhibit high accuracy and discriminatory power for end-to-end prognosis prediction using noninvasive OCT screening alone, without requiring additional blood tests for inflammatory indicators.

Regarding our deep learning model, it demonstrated exceptional performance in classifying cases related to SII, PLR, and NLR. We observed high accuracy, precision, recall, and AUC values, indicating its robustness in predicting inflammatory risk in patients with oral cancer. Additionally, our radiomics model exhibited promising results in predicting patient prognosis, displaying a high discriminatory power. This suggests its potential to assist in clinical decision-making. The selected features, namely SII, PLR, and NLR, were analyzed for their importance in the predictive model through their weights in the logistic regression classifier in radiomics. This analysis provides valuable information for risk assessment in patients with oral cancer. Notably, SII demonstrated the highest contribution to the prognosis task, implying its potential as a valuable indicator for assessing patient outcomes in clinical practice, aligning with our clinical statistical analysis results.

This model provides valuable references for clinical treatment and personalized medicine. Furthermore, the examination of feature contributions related to the three inflammatory risks demonstrated that features derived from the SII grading task exhibited the greatest significance, underscoring the crucial role of integrating SII in prognosis. In clinic, SII has demonstrated associations with various clinicopathological parameters, including tumor size, lymph node involvement, and distant metastasis [36]. By connecting systemic inflammation and immune response, SII provides clinicians with a reliable and easily accessible prognostic indicator for oral cancer patients [37]. Integrating SII with OCT shows promise in enhancing the diagnostic accuracy and predictive capabilities of OCT in the management of oral cancer. This integration allows for a comprehensive evaluation of both local tumor characteristics and systemic inflammatory status, enabling clinicians to make informed treatment decisions and anticipate patient outcomes. As a result, this prognostic model only requires non-invasive OCT examination for clinical application, eliminating the need for invasive blood tests and reducing the number of examinations. This significantly reduces patient discomfort and saves a substantial amount of economic costs.

However, our study has certain limitations that should be acknowledged. Firstly, the sample size was relatively small, and future studies with larger cohorts are needed to validate our findings. Moreover, the study design was limited to a single center, which may affect the generalizability of our results. Furthermore, additional research is necessary to refine and improve the accuracy and reliability of the deep learning and radiomics models used in this study.

Conclusion

In conclusion, our study highlights the potential of OCT imaging and AI techniques in assessing and predicting inflammation risks and prognosis results in oral cancer. The use of peripheral blood immune indicators, particularly SII, in combination with deep learning models and radiomics analysis, can provide valuable insights into the prognosis of oral cancer patients. Though the analysis, this work opens up new window for the end-to-end prognosis modeling for oral cancer in a noninvasive manner, without the need for invasive blood tests for inflammation indicators. Future studies should address the limitations identified in this study and further refine predictive models to enhance their clinical applicability.

Acknowledgements

Thanks are due to Zhengzhou Ultralucia Technology Co., Ltd, for assistance with the data collection.

Author contributions

Wei Yuan contributed to the study’s conceptualization, conducted investigations, performed formal analysis, and actively participated in writing the original draft. Jiayi Rao contributed to the original draft, conducted investigations, and assisted with the review and editing of the manuscript. Yanbin Liu and Sen Li provided input during the conceptual, review, and editing stages, and were responsible for the methodology, formal analysis, and data curation. Xin Huang and Lizheng Qin played a significant role in the study, contributing to the conceptualization and methodology, as well as formal analysis, investigation, and project administration. All authors critically revised and commented on the manuscript.

Funding

This research received financial support from various funding agencies and organizations. The research leading to these results received funding from the Clinical Technology Innovation Project of Beijing Hospital Management Center under Grant Agreement No. XMLX202123. Additionally, this work was supported by the Capital Health Development Scientific Research Special Project of the Beijing Municipal Health Commission with grant numbers Shoufa 2020-2-2141.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

All procedures performed in studies involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study received approval from the Institutional Review Board (IRB) of the Beijing Stomatological Hospital, Capital Medical University (Approval No. CMUSH-IRB-KJ-PJ-2021-18). Informed consent was obtained from all individual participants included in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Abbreviations

OSCC Oral Squamous Cell Carcinoma

OCT Optical Coherence Tomography

SII Systemic Immune-inflammation Index

PLR Platelet to Lymphocyte Ratio

NLR Neutrophil to Lymphocyte Ratio

MC3 Mixed Convolution 3D

SFM Select From Model

SVM Support Vector Machine

ROC The Receiver Operating Characteristic

AUC The Area Under the Curve

Publisher’s note

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