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Multi-photon Imaging of Tumor Cell Invasion in an Orthotopic Mouse Model of Oral Squamous Cell Carcinoma
Published on: July 25, 2011
Improved risk stratification for oral tongue squamous cell carcinoma using deep learning-based MRI classifier and
Runqiu Zhu1, Yan Zhang1, Fei Wang1
1Department of Oral and Maxillofacial Surgery, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
Background:
Current risk stratification for oral tongue squamous cell carcinoma (OTSCC) has limited accuracy in identifying patients who would benefit from postoperative radiotherapy (PORT), resulting in potential over- or under-treatment. We aimed to develop a more precise risk stratification system for OTSCC by integrating deep learning (DL)-based imaging analysis with pathological markers.
Methods:
A DL model was developed utilizing preoperative MRI images from 658 patients, and externally validated in 419 patients from two independent centers. Subsequently, we constructed a pathological deep learning nomogram (PDLN) that integrated the DL-based risk score with pathological markers (pT stage, pN stage, tumor grade, and tumor budding). Model performance was assessed using the C-index and time-dependent ROC curve analysis. Concomitantly, a composite score combining the DL model and pathological markers was established, forming the composite risk integration system for OTSCC (CRIS-OTSCC). Furthermore, we also evaluated the prognostic accuracy of the CRIS-OTSCC as well as its association with PORT.
Results:
The DL model showed a consistently high accuracy in predicting outcomes across all cohorts (C-index > 0.700). In multivariable analysis, after adjusting for clinicopathological variables, the DL-based risk score independently predicted both OS (overall survival) and DFS (disease-free survival) in all cohorts (all P < 0.001). The PDLN further improved the performance, with a C-index of 0.850 (95% CI, 0.815-0.885) for OS and 0.763 (0.718-0.808) for DFS in the training cohort, 0.809 (0.750-0.868) and 0.726 (0.655-0.797) in the internal validation cohort, 0.804 (0.747-0.861) and 0.737 (0.680-0.794) in the external validation cohort-A, and 0.778 (0.707-0.849) and 0.736 (0.663-0.809) in the external validation cohort-B, respectively. Notably, for patients in the CRIS-OTSCC favorable group, PORT did not affect OS (hazard ratio [HR] 0·429 [0·111-1·660]; P = 0.220) and DFS (0·899 [0·449-1·801]; P = 0.764). Conversely, for patients in the CRIS-OTSCC poor group, PORT was associated with improved OS (0·194 [0·115-0·325]; P < 0.001) and DFS (0·332 [0·214-0·513]; P < 0.001).
Conclusion:
The CRIS-OTSCC not only allows accurate prediction of patient outcomes but also identifies high-risk patients who may benefit from PORT, whereas its potential to guide PORT sparing in low-risk patients requires further validation.
