Purpose: Determining the extent of radical lymphadenectomy at clinical early stage is challenging. We aimed to investigate the appropriate extent of lymphadenectomy in clinical early-stage right colon cancer. Methods: Patients with clinical stage 0 or I right colon cancer who underwent curative surgery from January 2007 to December 2021 were included in this retrospective study. The extent of lymph node (LN) metastases based on the distribution of LN metastases (LND: LND1 pericolic nodes, LND2 intermediate nodes, LND3 apical nodes), along with the depth of submucosal (SM) invasion (classed into SM1–3), were analyzed. Results: Of the 348 patients, distribution across pathologic stages was as follows: 30 patients (8.6%) at stage 0, 207 (59.5%) at stage I, 52 (14.9%) at stage II, and 59 (17.0%) at stage III. In pT1 tumor patients, LN metastases varied by SM invasion depth: 3.6% in SM1 (all LND1), 5.1% in SM2 (all LND1), and 17.5% in SM3 (LND1 10%, LND2 5%, LND3 2.5%). For pT2, pT3, and pT4 stages, LN metastasis rates were 16.2% (LND1 11.3%, LND2 3.8%, LND3 1.3%), 39.7% (LND1 28.9%, LND2 8.4%, LND3 2.4%), and 50% (LND1 25%, LND2 25%), respectively. Tumor invasion depth and lymphovascular invasion were identified as significant risk factors for LN metastasis extending to LND2–3. Conclusion Complete mesocolic excision should be considered for right-sided colon cancer because tumor infiltration deeper than SM2 could metastasize to LND2 or further. If preoperative endoscopy confirms SM1 or SM2 invasion, D2 lymphadenectomy could be a limited surgical option.

Purpose: Determining the extent of radical lymphadenectomy at clinical early stage is challenging. We aimed to investigate the appropriate extent of lymphadenectomy in clinical early-stage right colon cancer. Methods: Patients with clinical stage 0 or I right colon cancer who underwent curative surgery from January 2007 to December 2021 were included in this retrospective study. The extent of lymph node (LN) metastases based on the distribution of LN metastases (LND: LND1 pericolic nodes, LND2 intermediate nodes, LND3 apical nodes), along with the depth of submucosal (SM) invasion (classed into SM1–3), were analyzed. Results: Of the 348 patients, distribution across pathologic stages was as follows: 30 patients (8.6%) at stage 0, 207 (59.5%) at stage I, 52 (14.9%) at stage II, and 59 (17.0%) at stage III. In pT1 tumor patients, LN metastases varied by SM invasion depth: 3.6% in SM1 (all LND1), 5.1% in SM2 (all LND1), and 17.5% in SM3 (LND1 10%, LND2 5%, LND3 2.5%). For pT2, pT3, and pT4 stages, LN metastasis rates were 16.2% (LND1 11.3%, LND2 3.8%, LND3 1.3%), 39.7% (LND1 28.9%, LND2 8.4%, LND3 2.4%), and 50% (LND1 25%, LND2 25%), respectively. Tumor invasion depth and lymphovascular invasion were identified as significant risk factors for LN metastasis extending to LND2–3. Conclusion Complete mesocolic excision should be considered for right-sided colon cancer because tumor infiltration deeper than SM2 could metastasize to LND2 or further. If preoperative endoscopy confirms SM1 or SM2 invasion, D2 lymphadenectomy could be a limited surgical option.

Purpose: Determining the extent of radical lymphadenectomy at clinical early stage is challenging. We aimed to investigate the appropriate extent of lymphadenectomy in clinical early-stage right colon cancer. Methods: Patients with clinical stage 0 or I right colon cancer who underwent curative surgery from January 2007 to December 2021 were included in this retrospective study. The extent of lymph node (LN) metastases based on the distribution of LN metastases (LND: LND1 pericolic nodes, LND2 intermediate nodes, LND3 apical nodes), along with the depth of submucosal (SM) invasion (classed into SM1–3), were analyzed. Results: Of the 348 patients, distribution across pathologic stages was as follows: 30 patients (8.6%) at stage 0, 207 (59.5%) at stage I, 52 (14.9%) at stage II, and 59 (17.0%) at stage III. In pT1 tumor patients, LN metastases varied by SM invasion depth: 3.6% in SM1 (all LND1), 5.1% in SM2 (all LND1), and 17.5% in SM3 (LND1 10%, LND2 5%, LND3 2.5%). For pT2, pT3, and pT4 stages, LN metastasis rates were 16.2% (LND1 11.3%, LND2 3.8%, LND3 1.3%), 39.7% (LND1 28.9%, LND2 8.4%, LND3 2.4%), and 50% (LND1 25%, LND2 25%), respectively. Tumor invasion depth and lymphovascular invasion were identified as significant risk factors for LN metastasis extending to LND2–3. Conclusion Complete mesocolic excision should be considered for right-sided colon cancer because tumor infiltration deeper than SM2 could metastasize to LND2 or further. If preoperative endoscopy confirms SM1 or SM2 invasion, D2 lymphadenectomy could be a limited surgical option.

Detecting sphenoid sinus fluid (SSF) is an additional finding in autopsies for diagnosing drowning. SSF can provide additional forensic evidence through laboratory tests such as diatom and electrolyte analyses. If drowning is suspected, accurately assessing the presence and volume of SSF during an autopsy is crucial. Utilizing postmortem computed tomography (PMCT) images could aid in accurately sampling SSF. Accurately segmenting the region of interest is essential for volume analysis using computed tomography images. However, manual segmentation techniques are labor-intensive and time-consuming, and their success depends on the experience of the observer. Therefore, this study aimed to develop a U-Net–based deep learning model for the automatic segmentation of SSF in drowning cases using PMCT images and to evaluate the performance of the model. We retrospectively reviewed 34 drowning cases in which both PMCT scans and forensic autopsies were performed at our institution. The U-Net architecture of deep learning was used for automatic segmentation. The proposed model achieved the Dice similarity coefficient (DSC) and Intersection over Union (IoU) of a maximum of 95.85% and 92.03%, a minimum of 0% and 0%, and an average of 77.15% and 67.18%, respectively. Although the average DSC and IoU did not show high similarity, this study showed that PMCT images can be used for automatic segmentation of SSF in drowning cases, which could improve the performance with sufficient dataset acquisition and further model training.