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Abstract Background The incidence of colorectal cancer is increasing among young adults and more rectal cancers are reported. This study aimed to identify the clinical features specific for early‐onset rectal cancer and provide insights on cancer management. Methods Early‐onset (

Neoadjuvant systemic treatment before surgery is a prevalent regimen in the patients with advanced-stage or high-risk tumor, which has shaped the treatment strategies and cancer survival in the past decades. However, some patients present with poor response to the neoadjuvant treatment. Therefore, it is of great significance to develop tools to help distinguish the patients that could achieve pathological complete response before surgery to avoid inappropriate treatment. Here, this study demonstrated a multi-task deep learning tool called DeepInteg. In the DeepInteg framework, the segmentation module was constructed based on the CE-Net with a context extractor to achieve end-to-end delineation of region of interest (ROI) from radiological images, then the features of segmented Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images of each case were fused and input to the classification module based on a convolution neural network for treatment outcome prediction. The dataset with 1700 MRI and CT slices collected from the prospectively randomized clinical trial (NCT01211210) on systemic treatment for rectal cancer was used to develop and systematically optimize DeepInteg. As a result, DeepInteg achieved automatic segmentation of tumoral ROI with Dices of 0.766 and 0.719 and mIoUs of 0.788 and 0.756 in CT and MRI images, respectively. In addition, DeepInteg achieved AUC of 0.833, accuracy of 0.826 and specificity of 0.856 in the prediction for pathological complete response after treatment, which showed better performance compared with the model based on CT or MRI alone. This study provide a robust framework to develop disease-specific tools for automatic delineation of ROI and clinical outcome prediction. The well-trained DeepInteg could be readily applied in clinic to predict pathological complete response after neoadjuvant therapy in rectal cancer patients.

Abstract Background We aimed to develop a radiomic model based on pre-treatment computed tomography (CT) to predict the pathological complete response (pCR) in patients with rectal cancer after neoadjuvant treatment and tried to integrate our model with magnetic resonance imaging (MRI)-based radiomic signature. Methods This was a secondary analysis of the FOWARC randomized controlled trial. Radiomic features were extracted from pre-treatment portal venous-phase contrast-enhanced CT images of 177 patients with rectal cancer. Patients were randomly allocated to the primary and validation cohort. The least absolute shrinkage and selection operator regression was applied to select predictive features to build a radiomic signature for pCR prediction (rad-score). This CT-based rad-score was integrated with clinicopathological variables using gradient boosting machine (GBM) or MRI-based rad-score to construct comprehensive models for pCR prediction. The performance of CT-based model was evaluated and compared by receiver operator characteristic (ROC) curve analysis. The LR (likelihood ratio) test and AIC (Akaike information criterion) were applied to compare CT-based rad-score, MRI-based rad-score and the combined rad-score. Results We developed a CT-based rad-score for pCR prediction and a gradient boosting machine (GBM) model was built after clinicopathological variables were incorporated, with improved AUCs of 0.997 [95% CI 0.990–1.000] and 0.822 [95% CI 0.649–0.995] in the primary and validation cohort, respectively. Moreover, we constructed a combined model of CT- and MRI-based radiomic signatures that achieve better AIC (75.49 vs. 81.34 vs.82.39) than CT-based rad-score (P = 0.005) and MRI-based rad-score (P = 0.003) alone did. Conclusions The CT-based radiomic models we constructed may provide a useful and reliable tool to predict pCR after neoadjuvant treatment, identify patients that are appropriate for a 'watch and wait' approach, and thus avoid overtreatment. Moreover, the CT-based radiomic signature may add predictive value to the MRI-based models for clinical decision making.

Objective: We conducted this multicenter cohort study to evaluate the current tumor-node-metastasis staging system and treatment modality by analyzing the survival outcomes of patient groups with stage III and IV colon cancer. Patients and Methods: Stage III and IV colon cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database (SEER cohort) and prospectively maintained Sun Yat-sen University (SYSU) cohort were included in this study. Kaplan-Meier method was used to estimate the cumulative rate of overall survival (OS) between patient groups, and the inverse probability weighting method was used to calculated age and sex-adjusted survival curves. The Cox regression model was used to identify the risk factors for OS. Results: A total of 17,911 and 1135 stage III-IV cases were included in the SEER and SYSU cohorts, respectively. Among them, 1448 and 124 resectable stage IV cases underwent curative-intent treatment in the SEER and SYSU cohorts, respectively. The T4N2b group showed a significantly worse survival outcome compared with the M1a subset receiving curative-intent treatment (HR, 1.46; p < 0.001). This finding was validated in the SYSU cohort, in which the T4N2 group had a worse outcome than that of the M1 group receiving curative-intent treatment (HR, 2.44; p < 0.001). These findings were confirmed in the adjusted survival analysis. In the multivariate analysis, the right-side tumor, poor-undifferentiated tumor, and age over 60 years were identified as independent risk factors for T4N2b patients. Based on this multivariate model, the high-risk T4N2b subgroup had a worse survival outcome compared with resectable M1b patients (HR, 1.24; p = 0.03). Conclusion: By comparing stage III with stage IV colon cancer patients, we identified a subgroup of stage III patients at a higher risk of death than more advanced stages, implying that current cancer care modalities are not sufficient for these high-risk substages. [ABSTRACT FROM AUTHOR]