This article aims to introduce a novel anatomical scanning method which requires scanning according to varied anatomic positions of the appendix based on the widely used graded compression method. We suggest placing the probe longitudinally in the region of the terminal cecum and moving it laterally to explore the sub-cecal appendix. The probe should be placed transversely on the medial side of the cecum to explore the pre-ileal appendix or post-ileal appendix. Placing the probe perpendicularly along external iliac vessels can help explore the pelvic appendix. The probe should be placed transversely on the paracolic sulci, and moved along the paracolic sulci to observe the extra-peritoneal appendix. Using the cephalic end of the probe as a pivot, push and squeeze the cecum to make it move bilaterally as much as possible, in order to expose the retrocecal appendix behind the air-filled cecum. It is our belief that this anatomical scanning method will greatly improve appendix detection rate and diagnostic accuracy, and provide guidance for surgical localization.

The objective of this study was to predict the preoperative pathological grading and survival period of Pseudomyxoma peritonei (PMP) by establishing models, including a radiomics model with greater omental caking as the imaging observation index, a clinical model including clinical indexes, and a combined model of these two. A total of 88 PMP patients were selected. Clinical data of patients, including age, sex, preoperative serum tumor markers [CEA, CA125, and CA199], survival time, and preoperative computed tomography (CT) images were analyzed. Three models (clinical model, radiomics model and combined model) were used to predict PMP pathological grading. The models’ diagnostic efficiency was compared and analyzed by building the receiver operating characteristic (ROC) curve. Simultaneously, the impact of PMP’s different pathological grades was evaluated. The results showed that the radiomics model based on the CT’s greater omental caking, an area under the ROC curve ([AUC] = 0.878), and the combined model (AUC = 0.899) had diagnostic power for determining PMP pathological grading. The imaging radiomics model based on CT greater omental caking can be used to predict PMP pathological grading, which is important in the treatment selection method and prognosis assessment.

Background: The effect of radiotherapy (RT) may differ according to colorectal cancer (CRC) histological subtypes including adenocarcinoma, mucinous adenocarcinoma (MC), and signet-ring cell carcinoma (SR). This study analyzed the prognosis of three pathological CRC types and focused on RT's prognostic significance on three CRC histological subtypes. Methods: Patients diagnosed with adenocarcinoma (n=54,174), MC (n=3,813), and SR (n=664) in the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database (2010–2017) were evaluated. Cox regression models and competitive risk models were built to assess the effect of RT on the risk of CRC-associated death. Results: Compared with adenocarcinoma patients, SR patients were associated with a 1.28-fold (HR=1.28, 95% CI: 1.16-1.42) risk of death. In the site-specific analyses, adenocarcinoma patients who received RT exhibited an increased risk of death (HR = 1.09, 95% CI: 1.03-1.15). RT did not show any prognostic influence for MC patients (HR = 0.96, 95% CI: 0.77-1.21). In SR patients. RT could reduce the risk of death (HR = 0.61, 95% CI: 0.39-0.95). After taking competing risk events (non-CRC-related death) into consideration. The results remained unchanged. Conclusions: Our study suggests that SR patients exhibited a worse OS (overall survival) than adenocarcinoma patients, and the effect of RT varied according to CRC histological subtypes.

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Purpose: The objective of this study was to predict the preoperative pathological grading and survival period of Pseudomyxoma peritonei (PMP) by establishing models, including a radiomics model with greater mental caking as the imaging observation index, a clinical model including clinical indexes, and a combination model of these two.Methods: A total of 88 PMP patients were selected. Clinical data of patients, including age, sex, preoperative serum tumor markers [CEA, CA125, and CA199], survival time, and preoperative computed tomography (CT) images were analyzed. Three models (clinical model, radiomics model and joint model) were used to predict PMP pathological grading. The models’ diagnostic efficiency was compared and analyzed by building the receiver operating characteristic (ROC) curve. Simultaneously, the impact of PMP’s different pathological grades was evaluated.Results: The results showed that the radiomics model based on the CT’s greater omental caking, an area under the ROC curve ([AUC] = 0.878), and the combined model (AUC = 0.899) had diagnostic power n for determining PMP pathological grade.Conclusion: The imaging radiomics model based on CT greater omental caking can be used to predict PMP pathological grade, which is important in the treatment selection method and prognosis assessment.