scholarly journals Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on routine CT imaging

2020 ◽  
Author(s):  
kan He ◽  
Xiaoming Liu ◽  
Mingyang Li ◽  
Xueyan Li ◽  
Hualin Yang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Kras Mutation ◽  
Validation Cohort ◽  
Ct Images ◽  
Ct Imaging ◽  
Kras Mutations ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Pre Treatment

Abstract Background: The detection of KRAS gene mutations in colorectal cancer (CRC) is key to the optimal design of individualized therapeutic strategies. The noninvasive prediction of the KRAS status in CRC is challenging. Deep learning (DL) in medical imaging has shown its high performance in diagnosis, classification, and prediction in recent years. In this paper, we investigated predictive performance by using a DL method with a residual neural network ( ResNet ) to estimate the KRAS mutation status in CRC patients based on routine pre-treatment contrast-enhanced CT imaging. Methods: We have collected a dataset consisting of 157 patients with pathology-confirmed CRC who were randomly divided into a training cohort (n = 117) and a validation cohort (n = 40). We developed an ResNet model that used portal venous phase CT images to estimate KRAS mutations in the axial, coronal, and sagittal directions of the training cohort and validated the model in the validation cohort. Several groups of expended ROI patches were generated for the ResNet model, to explore whether tissues around the tumor can contribute to cancer assessment. We also explored a radiomics model with the random forest classifier (RFC) to predict KRAS mutations and compared it with the DL model. Results: The ResNet model in the axial direction achieved the higher area under the curve (AUC) value (0.90) in the validation cohort and peaked at 0.93 with an input of “ROI and 20-pixel” surrounding area. In the training cohort, the AUC was 0.945 (sensitivity: 0.75; specificity: 0.94), and in the validation cohort, the AUC was0.818 (sensitivity: 0.70; specificity: 0.85). In comparison, the ResNet model showed better predictive ability . Conclusions: Our experiments reveal that the computerized assessment of the pre-treatment CT images of CRC patients using a DL model has the potential to precisely predict KRAS mutations. This new model has the potential to assist in noninvasive KRAS mutation estimation. Keywords: Colorectal Neoplasm, Mutation, Deep Learning

scholarly journals Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on CT imaging

2020 ◽  
Author(s):  
kan He ◽  
Xiaoming Liu ◽  
Mingyang Li ◽  
Xueyan Li ◽  
Hualin Yang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Kras Mutation ◽  
Ct Images ◽  
Ct Imaging ◽  
Kras Mutations ◽  
Training Cohort ◽  
Portal Venous Phase ◽  
Contrast Enhanced Ct ◽  
Pre Treatment

Abstract ABSTRACT Background: The detection of Kirsten rat sarcoma viral oncogene homolog ( KRAS )gene mutations in colorectal cancer (CRC) is key to the optimal design of individualized therapeutic strategies. The noninvasive prediction of the KRAS status in CRC is challenging. Deep learning (DL) in medical imaging has shown its high performance in diagnosis, classification, and prediction in recent years. In this paper, we investigated predictive performance by using a DL method with a residual neural network ( ResNet ) to estimate the KRAS mutation status in CRC patients based on pre-treatment contrast-enhanced CT imaging. Methods: We have collected a dataset consisting of 157 patients with pathology-confirmed CRC who were divided into a training cohort (n = 117) and a testing cohort (n = 40). We developed an ResNet model that used portal venous phase CT images to estimate KRAS mutations in the axial, coronal, and sagittal directions of the training cohort and evaluated the model in the testing cohort. Several groups of expended region of interest （ROI）patches were generated for the ResNet model，to explore whether tissues around the tumor can contribute to cancer assessment. We also explored a radiomics model with the random forest classifier (RFC) to predict KRAS mutations and compared it with the DL model. Results: The ResNet model in the axial direction achieved the higher area under the curve (AUC) value (0.90) in the testing cohort and peaked at 0.93 with an input of “ROI and 20-pixel” surrounding area. AUC of radiomics model in testing cohorts were 0.818. In comparison, the ResNet model showed better predictive ability . Conclusions: Our experiments reveal that the computerized assessment of the pre-treatment CT images of CRC patients using a DL model has the potential to precisely predict KRAS mutations. This new model has the potential to assist in noninvasive KRAS mutation estimation. Keywords: Colorectal Neoplasm, Mutation, Deep Learning

scholarly journals Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on CT imaging

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Kan He ◽  
Xiaoming Liu ◽  
Mingyang Li ◽  
Xueyan Li ◽  
Hualin Yang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Kras Mutation ◽  
Ct Imaging ◽  
Learning Method

scholarly journals Deep learning quantified mucus-tumor ratio predicting survival of patients with colorectal cancer using whole-slide images

2021 ◽  
Author(s):  
Ke Zhao ◽  
Lin Wu ◽  
Yanqi Huang ◽  
Su Yao ◽  
Zeyan Xu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Prognostic Value ◽  
Validation Cohort ◽  
Survival Rates ◽  
Training Cohort ◽  
Mucinous Component ◽  
Hematoxylin And Eosin ◽  
High Consistency ◽  
Whole Slide Images

Abstract Background In colorectal cancer (CRC), mucinous adenocarcinoma differs from other adenocarcinomas in gene-phenotype, morphology, and prognosis. However, mucinous components are present in a large amount of adenocarcinoma, and the prognostic value of mucus proportion has not been investigated. Artificial intelligence provides a way to quantify mucus proportion on whole-slide images (WSIs) accurately. We aimed to quantify mucus proportion by deep learning and further investigate its prognostic value in two CRC patients cohorts. Methods Deep learning was used to segment WSIs stained with hematoxylin and eosin. Mucus-tumor ratio (MTR) was defined as the proportion of mucinous component in the tumor area. A training cohort (N = 419) and a validation cohort (N = 315) were used to evaluate the prognostic value of MTR. Survival analysis was performed by the Cox proportional hazard model. Result Patients were stratified to mucus-low and mucus-high groups by 24.1% as the threshold. In the training cohort, patients with mucus-high had unfavorable outcomes (hazard ratio for high vs. low 1.88, 95% confidence interval 1.18-2.99, P = 0.008), with 5-year overall survival rates of 54.8% and 73.7% in mucus-high and mucus-low groups, respectively. The results were confirmed in the validation cohort (2.09, 1.21-3.60, 0.008; 79.8% vs. 62.8%). The prognostic value of MTR was maintained in multivariate analysis for both cohorts. Conclusion The deep learning quantified MTR was an independent prognostic factor in CRC. With the advantages of advanced efficiency and high consistency, our method is suitable for clinical application and promotes precision medicine development.

Early evaluation using a radiomic signature of unresectable hepatic metastases to predict outcome in patients with colorectal cancer treated with FOLFIRI and bevacizumab

Gut ◽  
2019 ◽  
Vol 69 (3) ◽  
pp. 531-539 ◽  
Author(s):  
Anthony Dohan ◽  
Benoit Gallix ◽  
Boris Guiu ◽  
Karine Le Malicot ◽  
Caroline Reinhold ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Texture Analysis ◽  
Validation Cohort ◽  
External Validation ◽  
Hepatic Metastases ◽  
Survival Prediction ◽  
Training Cohort ◽  
Link Type ◽  
Contrast Enhanced Ct ◽  
Morphological Criteria

PurposeThe objective of this study was to build and validate a radiomic signature to predict early a poor outcome using baseline and 2-month evaluation CT and to compare it to the RECIST1·1 and morphological criteria defined by changes in homogeneity and borders.MethodsThis study is an ancillary study from the PRODIGE-9 multicentre prospective study for which 491 patients with metastatic colorectal cancer (mCRC) treated by 5-fluorouracil, leucovorin and irinotecan (FOLFIRI) and bevacizumab had been analysed. In 230 patients, computed texture analysis was performed on the dominant liver lesion (DLL) at baseline and 2 months after chemotherapy. RECIST1·1 evaluation was performed at 6 months. A radiomic signature (Survival PrEdiction in patients treated by FOLFIRI and bevacizumab for mCRC using contrast-enhanced CT TextuRe Analysis (SPECTRA) Score) combining the significant predictive features was built using multivariable Cox analysis in 120 patients, then locked, and validated in 110 patients. Overall survival (OS) was estimated with the Kaplan-Meier method and compared between groups with the logrank test. An external validation was performed in another cohort of 40 patients from the PRODIGE 20 Trial.ResultsIn the training cohort, the significant predictive features for OS were: decrease in sum of the target liver lesions (STL), (adjusted hasard-ratio(aHR)=13·7, p=1·93×10–7), decrease in kurtosis (ssf=4) (aHR=1·08, p=0·001) and high baseline density of DLL, (aHR=0·98, p<0·001). Patients with a SPECTRA Score >0·02 had a lower OS in the training cohort (p<0·0001), in the validation cohort (p<0·0008) and in the external validation cohort (p=0·0027). SPECTRA Score at 2 months had the same prognostic value as RECIST at 6 months, while non-response according to RECIST1·1 at 2 months was not associated with a lower OS in the validation cohort (p=0·238). Morphological response was not associated with OS (p=0·41).ConclusionA radiomic signature (combining decrease in STL, density and computed texture analysis of the DLL) at baseline and 2-month CT was able to predict OS, and identify good responders better than RECIST1.1 criteria in patients with mCRC treated by FOLFIRI and bevacizumab as a first-line treatment. This tool should now be validated by further prospective studies.Trial registrationClinicaltrial.gov identifier of the PRODIGE 9 study: NCT00952029.Clinicaltrial.gov identifier of the PRODIGE 20 study: NCT01900717.

scholarly journals Imaging Biomarkers of Contrast-enhanced Computed Tomography Predict Survival in Oesophageal Cancer After Definitive Concurrent Chemoradiotherapy

2020 ◽  
Author(s):  
Chengbing Zeng ◽  
Tiantian Zhai ◽  
Jianzhou Chen ◽  
Longjia Guo ◽  
Baotian Huang ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Imaging Biomarkers ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Treatment Contrast ◽  
Pre Treatment ◽  
Enhanced Computed Tomography ◽  
Contrast Enhanced Computed Tomography

Abstract Background: This study aimed to evaluate the predictive potential of contrast-enhanced computed tomography (CT)-based imaging biomarkers (IBMs) for the treatment outcomes of oesophageal squamous cell carcinoma (OSCC) patients after definitive concurrent chemoradiotherapy (CCRT).Methods: A total of 151 ESCC patients who underwent definitive CCRT were included in this retrospective study. All patients were separated randomly to a training cohort (n=97) and the validation cohort (n=54). Pre-treatment contrast-enhanced CT scans were obtained for all patients and used for the extraction of IBMs. An IBM score was constructed by using the least absolute shrinkage and selection operator with logistic regression analysis in training cohort and tested in the validation cohort. IBMsnomograms were built based on IBM score. The concordance index (C-index) was used to assess the performance of the nomograms. Finally, decision curve analysis was performed to estimate the clinical usefulness of the nomograms.Results: A total of 96 IBMs were extracted from each contrast-enhanced CT scan. The IBM score were consisted of 13 CT-based IBMs and were significantly correlated with 3-year overall survival (OS) and 3-year progression-free survival (PFS). Multivariate analysis revealed that IBM score was the independent prognostic factor. In the training cohort, the IBM score yielded an area under the curves (AUCs) of 0.802 (95% CI: 0.713–0.891, p<0.001) and 0.742 (95% CI: 0.620–0.889, p<0.001) in terms of 3-year OS and 3-year PFS, respectively. In validation cohort, the AUCs were 0.761(95% CI: 0. 639–0.900, p<0.001) and 0.761(95% CI: 0.629–0.893, p=0.001) for 3-year OS and 3-year PFS,respectively. Kaplan-Meier survival analysis showed significantly different between risk subgroups in training and validation cohort. The nomograms were built based on the IBM score showed good discrimination. In the training cohort, with the C-indices of IBMsnomograms were 0.732 (95%CI, 0.661–0.803) and 0.670(95%CI, 0.595–0.745) for OS and PFS, respectively. In the validation cohort C-indices were 0.677(95%CI, 0.583–0.771) and 0.678(95%CI, 0.591–0.765) for OS and PFS, respectively. The decision curve showed the clinical usefulness of nomograms.Conclusions: TheIBM score based on pre-treatment contrast-enhanced CT could predict the 3-year OS and 3-year PFS for OSCC patients after definitive CCRT. Further multicenter studies with larger sample sizes are warranted.

scholarly journals Imaging biomarkers of contrast-enhanced computed tomography predict survival in oesophageal cancer after definitive concurrent chemoradiotherapy

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Chengbing Zeng ◽  
Tiantian Zhai ◽  
Jianzhou Chen ◽  
Longjia Guo ◽  
Baotian Huang ◽  
...  
Keyword(s):  
Cox Regression ◽  
Validation Cohort ◽  
Imaging Biomarkers ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Treatment Contrast ◽  
Pre Treatment ◽  
Enhanced Computed Tomography

Abstract Background This study aimed to evaluate the predictive potential of contrast-enhanced computed tomography (CT)-based imaging biomarkers (IBMs) for the treatment outcomes of patients with oesophageal squamous cell carcinoma (OSCC) after definitive concurrent chemoradiotherapy (CCRT). Methods Altogether, 154 patients with OSCC who underwent definitive CCRT were included in this retrospective study. All patients were randomised to the training cohort (n = 99) or the validation cohort (n = 55). Pre-treatment contrast-enhanced CT scans were obtained for all patients and used for the extraction of IBMs. An IBM score, was constructed by using the least absolute shrinkage and selection operator with Cox regression analysis, which was equal to the log-partial hazard of the Cox model in the training cohort and tested in the validation cohort. IBM nomograms were built based on IBM scores for individualised survival estimation. Finally, a decision curve analysis was performed to estimate the clinical usefulness of the nomograms. Results Altogether, 96 IBMs were extracted from each contrast-enhanced CT scan. IBM scores were constructed from 11 CT-based IBMs for overall survival (OS) and 8 IBMs for progression-free survival (PFS), using the LASSO-Cox regression method in the training cohort. Multivariate analysis revealed that IBM score was an independent prognostic factor correlated with OS and PFS. In the training cohort, the C-indices of IBM scores were 0.734 (95% CI 0.664–0.804) and 0.658 (95% CI 0.587–0.729) for OS and PFS, respectively. In the validation cohort, C-indices were 0.672 (95% CI 0.578–0.766) and 0.666 (95% CI 0.574–0.758) for OS and PFS, respectively. Kaplan–Meier survival analysis showed a significant difference between risk subgroups in the training and validation cohorts. Decision curve analysis confirmed the clinical usefulness of the IBM score. Conclusions The IBM score based on pre-treatment contrast-enhanced CT could predict the OS and PFS for patients with OSCC after definitive CCRT. Further multicentre studies with larger sample sizes are warranted.

scholarly journals Imaging biomarkers of contrast-enhanced computed tomography predict survival in oesophageal cancer after definitive concurrent chemoradiotherapy

2020 ◽  
Author(s):  
Chengbing Zeng ◽  
Tiantian Zhai ◽  
Jianzhou Chen ◽  
Longjia Guo ◽  
Baotian Huang ◽  
...  
Keyword(s):  
Cox Regression ◽  
Validation Cohort ◽  
Imaging Biomarkers ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Treatment Contrast ◽  
Pre Treatment ◽  
Enhanced Computed Tomography

Abstract Background: This study aimed to evaluate the predictive potential of contrast-enhanced computed tomography (CT)-based imaging biomarkers (IBMs) for the treatment outcomes of patients with oesophageal squamous cell carcinoma (OSCC) after definitive concurrent chemoradiotherapy (CCRT).Methods: Altogether, 154 patients with OSCC who underwent definitive CCRT were included in this retrospective study. All patients were randomised to the training cohort (n=99) or the validation cohort (n=55). Pre-treatment contrast-enhanced CT scans were obtained for all patients and used for the extraction of IBMs. An IBM score, was constructed by using the least absolute shrinkage and selection operator with Cox regression analysis, which was equal to the log-partial hazard of the Cox model in the training cohort and tested in the validation cohort. IBM nomograms were built based on IBM scores for individualised survival estimation. Finally, a decision curve analysis was performed to estimate the clinical usefulness of the nomograms.Results: Altogether, 96 IBMs were extracted from each contrast-enhanced CT scan. IBM scores were constructed from 11 CT-based IBMs for overall survival (OS) and 8 IBMs for progression-free survival (PFS), using the LASSO-Cox regression method in the training cohort. Multivariate analysis revealed that IBM score was an independent prognostic factor correlated with OS and PFS. In the training cohort, the C-indices of IBM scores were 0.734 (95%CI, 0.664–0.804) and 0.658 (95%CI, 0.587–0.729) for OS and PFS, respectively. In the validation cohort, C-indices were 0.672 (95%CI, 0.578–0.766) and 0.666 (95%CI, 0.574–0.758) for OS and PFS, respectively. Kaplan-Meier survival analysis showed a significant difference between risk subgroups in the training and validation cohorts. Decision curve analysis confirmed the clinical usefulness of the IBM score.Conclusions: The IBM score based on pre-treatment contrast-enhanced CT could predict the OS and PFS for patients with OSCC after definitive CCRT. Further multicentre studies with larger sample sizes are warranted.

Heterogeneity of acquired KRAS and EGFR mutations in colorectal cancer patients treated with anti-EGFR monoclonal antibodies.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 3512-3512 ◽  
Author(s):  
M. Pia Morelli ◽  
Michael J. Overman ◽  
Arvind Dasari ◽  
Syed Mohammad Ali Kazmi ◽  
Eduardo Vilar Sanchez ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Monoclonal Antibodies ◽  
Kras Mutation ◽  
Egfr Mutation ◽  
Acquired Resistance ◽  
Braf Inhibitor ◽  
Tumor Burden ◽  
Egfr Mutations ◽  
Kras Mutations ◽  
Pre Treatment

3512 Background: Although KRAS and EGFR extracellular domain acquired mutations were detected in two small cohorts and correlated with acquired resistance to anti-EGFR monoclonal antibodies (MAb), the frequency, co-occurrence, and distribution of these acquired mutations is unknown. In this study we evaluated the presence of acquired KRAS and EGFR mutations in cfDNA from CRC patients (pts) treated with anti-EGFR monoclonal antibody. Methods: Plasma was collected from EGFR-MAb refractory mCRC pts as part of the ATTACC (Assessment of Targeted Therapies Against Colorectal Cancer) program. Eligible pts had documentation of pre-treatment KRAS wild type tumor. The cfDNA was extracted from the plasma and analyzed by BEAMing technology for acquired KRAS and EGFR mutation. Results: The plasma from 55 patients was analyzed for EGFR and KRAS mutation. The S492R EGFR mutation was detected in 4 pts (7%) treated with cetuximab. Acquired KRAS mutations were detected in 26 of the 55 KRAS wt samples analyzed (47%). Although codon 61 and 146 mutations are rare in untreated CRCs (2% and 1% of the MDACC population, respectively), these atypical KRAS mutations predominated in acquired resistance (Q61H=33% and A146T=10%). Mutations in more than one KRAS codon are exceedingly rare in the primary tumor. In our study we detected more than one KRAS or EGFR mutation in 30% of the population (p<0.001), suggesting the development of multiple independent clones in individual patients. Compared to 8 patients with known KRAS mutations, the average number of mutant reads in the 26 patients with acquired mutation was substantially lower (p<0.01) despite similar tumor burden. Of note, acquired concomitant KRAS mutations were also found in a BRAF V600 mutant patient previously treated with anti-EGFR MAb and a BRAF inhibitor. Conclusions: KRAS and EGFR acquired mutation are present at low concentrations in cfDNA from mCRC pts refractory to anti-EGFR MAb and they are not mutually exclusive, suggesting heterogeneity of the resistant clones. Anti-EGFR MAb refractory patients showed a higher incidence of atypical KRAS mutation and higher incidence of multiple codon KRAS mutations compared with overall CRC patient.

scholarly journals Imaging biomarkers of contrast-enhanced computed tomography predict survival in oesophageal cancer after definitive concurrent chemoradiotherapy

2020 ◽  
Author(s):  
Chengbing Zeng ◽  
Tiantian Zhai ◽  
Jianzhou Chen ◽  
Longjia Guo ◽  
Baotian Huang ◽  
...  
Keyword(s):  
Cox Regression ◽  
Validation Cohort ◽  
Imaging Biomarkers ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Treatment Contrast ◽  
Pre Treatment ◽  
Enhanced Computed Tomography

Abstract Background: This study aimed to evaluate the predictive potential of contrast-enhanced computed tomography (CT)-based imaging biomarkers (IBMs) for the treatment outcomes of patients with oesophageal squamous cell carcinoma (OSCC) after definitive concurrent chemoradiotherapy (CCRT).Methods: Altogether, 154 patients with OSCC who underwent definitive CCRT were included in this retrospective study. All patients were randomised to the training cohort (n=99) or the validation cohort (n=55). Pre-treatment contrast-enhanced CT scans were obtained for all patients and used for the extraction of IBMs. An IBM score was constructed by using the least absolute shrinkage and selection operator with Cox regression analysis in the training cohort and tested in the validation cohort. IBM nomograms were built based on IBM scores for individualised survival estimation. Finally, a decision curve analysis was performed to estimate the clinical usefulness of the nomograms.Results: Altogether, 96 IBMs were extracted from each contrast-enhanced CT scan. IBM scores were constructed from 11 CT-based IBMs for overall survival (OS) and 8 IBMs for progression-free survival (PFS), using the LASSO-Cox regression method in the training cohort. Multivariate analysis revealed that IBM score was an independent prognostic factor correlated with OS and PFS. In the training cohort, the C-indices of IBM scores were 0.734 (95%CI, 0.664–0.804) and 0.658 (95%CI, 0.587–0.729) for OS and PFS, respectively. In the validation cohort, C-indices were 0.672 (95%CI, 0.578–0.766) and 0.666 (95%CI, 0.574–0.758) for OS and PFS, respectively. Kaplan-Meier survival analysis showed a significant difference between risk subgroups in the training and validation cohorts. Decision curve analysis confirmed the clinical usefulness of the IBM score.Conclusions: The IBM score based on pre-treatment contrast-enhanced CT could predict the OS and PFS for patients with OSCC after definitive CCRT. Further multicentre studies with larger sample sizes are warranted.

scholarly journals Imaging biomarkers of contrast-enhanced computed tomography predict survival in oesophageal cancer after definitive concurrent chemoradiotherapy

2020 ◽  
Author(s):  
Chengbing Zeng ◽  
Tiantian Zhai ◽  
Jianzhou Chen ◽  
Longjia Guo ◽  
Baotian Huang ◽  
...  
Keyword(s):  
Cox Regression ◽  
Validation Cohort ◽  
Imaging Biomarkers ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Treatment Contrast ◽  
Pre Treatment ◽  
Enhanced Computed Tomography

Abstract Background: This study aimed to evaluate the predictive potential of contrast-enhanced computed tomography (CT)-based imaging biomarkers (IBMs) for the treatment outcomes of oesophageal squamous cell carcinoma (OSCC) patients after definitive concurrent chemoradiotherapy (CCRT). Methods: Altogether, 154 patients with OSCC who underwent definitive CCRT were included in this retrospective study. All patients were separated randomly to a training cohort (n=99) and the validation cohort (n=55). Pre-treatment contrast-enhanced CT scans were obtained for all patients and used for the extraction of IBMs. An IBM score was constructed by using the least absolute shrinkage and selection operator with Cox regression analysis in the training cohort and tested in the validation cohort. IBM nomograms were built based on IBM scores for individualized survival estimation. Finally, a decision curve analysis was performed to estimate the clinical usefulness of the nomograms. Results: Altogether, 96 IBMs were extracted from each contrast-enhanced CT scan. The IBM score constructed by 11 CT-based IBMs, using LASSO-Cox regression method in training cohort. The multivariate analysis revealed that IBM score was the independent prognostic factor correlated with overall survival (OS) and progression-free survival (PFS). In the training cohort, the C-indices of IBM scores were 0.734 (95%CI, 0.664–0.804) and 0.678 (95%CI, 0.607–0.745) for OS and PFS, respectively. In the validation cohort, C-indices were 0.672 (95%CI, 0.578–0.766) and 0.662 (95%CI, 0.573–0.751) for OS and PFS, respectively. Kaplan-Meier survival analysis showed significantly different between risk subgroups in training and validation cohort. The decision curve showed the clinical usefulness of IBM score. Conclusions: The IBM score based on pre-treatment contrast-enhanced CT could predict the OS and PFS for patients with OSCC after definitive CCRT. Further multicentre studies with larger sample sizes are warranted.

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