Park, Ji Won

BACKGROUND: Data registries lack a definitive classification system that distinguishes different locations of colon cancer from one another. OBJECTIVE: To establish an international consensus on the definition of primary colon cancer segment locations. DESIGN: Between December 2022 and June 2023, Delphi survey study was conducted to seek opinions from relevant international experts and eventually develop a consensus definition of each colon cancer segment. SETTING: Three-round online-based Delphi survey study. INTERVENTIONS: Online survey included 17 questions. In the first two rounds, participating experts were asked to rank each statement on a scale of 1–9 (9 is the most relevant). Consensus statements and definitions were revised according to results, obtaining a consensus score of 7–9. During the third round and online meeting, definitions and statements reached a moderate or high consensus(above 4 in more than 70% of participants) were included. MAIN OUTCOME AND MEASURE: Primary goal of our project was focused on precisely localizing the specific segment affected by primary colon cancer, rather than to identify surgical treatment or type of resection needed for particular segment. RESULTS: The first round included 331 experts, 301 (91%) completed the second round, and 295 (98%) completed the final round. Experts strongly supported the use of a “10 cm rule” to describe colon cancer sites at the flexures and anatomical landmarks for other segments. Regarding the definition of rectosigmoid cancer, experts from America and Europe reached a high consensus that the term rectosigmoid as a colon cancer location must be abolished in contrast to experts from Asia. Description of cancers overlapping segments achieved a consensus of 64%. LIMITATIONS: Subjective decisions are based on individual expert clinical experience. CONCLUSIONS: This Delphi survey, the first internationally conducted consensus study, achieved a remarkable level of consensus among a panel of global experts. Ambiguity still exists regarding overlapping lesions. See Video Abstract .

Abstract Introduction As total neoadjuvant therapy for locally advanced rectal cancer (LARC) emerged, the possibility of skipping radiotherapy for poorly responsive patients arose. Machine-learning algorithms have focused on the radiopathologic features of tumor segmentation in order to predict responsiveness to radiotherapy. However, in addition to the tumor itself, there are several factors related to responsiveness, such as vasculature affecting hypoxia or MRI-detected extramural vascular invasion status. We aimed to predict poor responders using pretreatment rectal MRI images without segmentation and to identify which factors mainly contribute to the prediction algorithm. Methods Between Jan 1, 2000, and Dec 30, 2020, 689 consecutive patients were retrospectively included in two hospitals. Poor responders were defined by tumor regression grades (TRG) 2 and 3 that were determined through surgical resection. The ResNet-50 model was trained to predict poor responders from pretreatment rectal MRIs (T2-weighted axial, sagittal, and coronal images). We adopted a tenfold cross-validation for training and testing the model and used Gradient-weighted Class Activation Mapping (Grad-CAM) to highlight the important regions in the MRI scans that help predict poor responders. Results The number in each group of TRG was 108 (15.7%), 250 (36.3%), 265 (38.5%), and 66 (9.6%) for TRG0, TRG1, TRG2, and TRG3, respectively. There were 618 patients in the training cohort and 71 patients in the validation cohort. In the training and validation cohort, the accuracy for the prediction of poor responders was 85.6% (area under the curve (AUC) 0.856 [95% CI 0.761-0.950]) and 70.2% (AUC 0.703 [0.682-0.724]), although without segmentation. Our prediction model achieved a sensitivity of 0.724 (95% CI 0.700-0.748), a specificity of 0.684 (0.658-0.710), a positive predictive value of 0.697 (0.656-0.737), and a negative predictive value of 0.708 (0.666-0.751) in the validation cohort. Grad-CAM showed that the most important part of the accurately predicted images to contribute to the prediction was not the tumor (7/355, 1.9%) but the pelvic vasculature (353/355, 99.4%), including iliac vessels, femoral vessels, and presacral plexus, and followed by the mesorectum (38/355, 10.7%). Conclusion The pelvic vasculature contributes more to predicting poor responders to radiotherapy than the tumor itself. Therefore, when creating a prediction model for responsiveness to radiotherapy in LARC, this should be considered. Citation Format: Rumi Shin, Byunho Jo, Inyeop Jang, Cheong-Il Shin, Jin Sun Choi, Seung-Yong Jeong, Seung Chul Heo, Ji Won Park, Min Jung Kim, Tae Hyun Hwang. Why only focus on the tumor?: The crucial role of the extra-tumor environment to predict poor responders for locally advanced rectal cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5374.