Xijiao Liu

: Objectives: This study aimed to design and evaluate a competency-based education (CBE)-oriented modular training model for radiology residents, emphasizing interdisciplinary integration and disease-focused instruction to enhance clinical competence. Materials and methods: This prospective controlled study included 75 radiology residents enrolled in standardized training from September 2020 to July 2024. A CBE-oriented modular training model was developed, structured around approximately 27 radiologic subspecialties and incorporating interdisciplinary instruction, clinical team rotations, and image post-processing skill training. The participants were assigned to either the experimental group (CBE training) or the control group (traditional training). Teaching effectiveness was evaluated by comparing theoretical and practical exam scores via statistical analysis. Results: Overall, there were no significant differences in theoretical (83.26 ± 6.99 vs 81.76 ± 7.54; p > 0.05) or practical scores (86.79 ± 8.90 vs 84.91 ± 10.71; p > 0.05) between the two groups. However, subgroup analysis revealed that, among residents in the non-graduate subgroup, the experimental group achieved significantly higher theoretical ( p = 0.0179) and practical scores ( p = 0.028) than the control group. No significant differences were observed in the enrolled-graduate subgroup ( p > 0.05). Conclusions: The competency-based modular training model significantly improved clinical competence among residents in the non-graduate subgroup but showed less additional benefit in the enrolled-graduate subgroup. This study offers insights for advancing radiology residency training reform. Critical relevance statement: The distinct effectiveness of this approach was particularly beneficial for trainees with diverse educational backgrounds. This training model enhances residents’ clinical competence and research capabilities and provides insights for global radiology education reform, aligning training paradigms with evolving clinical and technological advancements. Key Points: A CBE-oriented modular training model for radiology residents was developed, integrating interdisciplinary learning, team-based clinical training, and image post-processing instruction. Although no overall difference in examination scores was observed between the two groups, residents in the non-graduate subgroup achieved significantly better theoretical and practical scores under the CBE model.

Background and Aims: Post‐TIPS overt hepatic encephalopathy (OHE) and variceal rebleeding (VRB) remain serious complications with limited predictive tools. This study investigates whether the inherent preoperative three‐dimensional portal venous geometry (3D‐PVG) serves as an independent prognostic biomarker for 1year OHE and VRB in patients undergoing transjugular intrahepatic portosystemic shunt (TIPS). Methods: In this multicenter retrospective study, 579 cirrhotic patients undergoing successful TIPS were enrolled. We extracted 3D‐PVG features from routine computed tomography angiography (CTA) images before TIPS, quantifying bifurcation angles (‘Near’) and vessel segment tortuosity (‘Far’). Cox regression identified predictors for 1‐year OHE and VRB. Model performance was evaluated using Harrell's C‐index, comparing clinical models against integrated clinical‐geometric models (3D‐CGM). Results: For 1‐year OHE, increased age (HR 1.458, p < 0.001), higher BUN (HR 1.171, p = 0.019), and extremely flat bifurcations (near_bin_87: 172°–174°; HR 1.104, p = 0.039) were independent risk factors. For 1‐year VRB, first bleeding (HR 2.347, p = 0.027), elevated Cr (HR 1.244, p = 0.012), relatively flat bifurcations (near_bin_74: 146°–148°; HR 1.432, p = 0.001), and very straight vessel segments (far_bin_2: 2°–4°; HR 1.467, p = 0.013) were risk factors, while use of covered stents (HR 0.197, p = 0.013) and gently curved vessel segments (far_bin_27: 52°–54°; HR 0.262, p = 0.018) were protective factors. Integrating 3D‐PVG features significantly improved predictive performance: the 3D‐CGM achieved C‐indices of 0.801 for OHE and 0.792 for VRB, surpassing clinical models (0.715 and 0.686, respectively). Conclusion: The inherent portal venous geometry may serve as an independent imaging biomarker for post‐TIPS outcomes. The 3D‐CGM markedly enhances risk prediction, providing a non‐invasive approach for prognosis assessment in portal hypertension.

Background: Due to the significant risk of recurrence, postoperative surveillance of gastrointestinal stromal tumors (GISTs) is crucial. Contrast-enhanced computed tomography (CECT) is the standard imaging modality; however, repeated long-term examinations result in cumulative contrast and radiation exposure. Non-contrast computed tomography (NCCT) offers a safer alternative; however, high-quality multi-center evidence comparing its diagnostic performance with that of CECT remains limited. This study aimed to compare the diagnostic performance of abdominopelvic NCCT and CECT for detecting postoperative recurrence or metastasis in patients with GISTs. Methods: This retrospective multi-reader, multi-case (MRMC) study was conducted using data from patients with GISTs who underwent radical resection between September 2011 and January 2023. Five radiologists (with 5–20 years of experience) blindly and independently reviewed NCCT and CECT datasets. Diagnostic accuracy was analyzed using R and Python, with subgroup analyses based on National Institutes of Health (NIH) risk stratification and lesion location. A P value Results: Both NCCT and CECT demonstrated good diagnostic accuracy across both institutions [West China Hospital, Sichuan University (Institution I): area under the curve (AUC) 0.863 vs. 0.882, P=0.025; People’s Hospital of Sichuan Province (Institution II): AUC 0.935 vs. 0.960, P=0.121], with both modalities achieving similarly high specificity (0.969–0.993). In the mid- to high-risk subgroup, performance remained comparable (Institution I: AUC 0.861 vs. 0.876, P=0.066; Institution II: AUC 0.936 vs. 0.966, P=0.138). CECT had significantly higher sensitivity for liver metastases at Institution II (0.833 vs. 0.667, P=0.034) and peritoneal deposits at Institution I (0.669 vs. 0.623, P=0.028), while showing no significant differences in other comparisons (all P>0.05). Senior radiologists achieved similar performance with both modalities (AUC 0.916 vs. 0.932, P=0.197). Conclusions: This multi-center MRMC study demonstrates that NCCT is a valuable alternative to CECT for postoperative GIST surveillance, particularly in patients requiring long-term follow-up or those with contraindications to contrast. NCCT has high specificity comparable to that of CECT, while mitigating the risks of cumulative contrast and radiation exposure; however, expert interpretation remains important for detecting small lesions due to its lower sensitivity.

Background This study analyzed preoperative CT features and postoperative pathological factors to predict recurrence after curative resection in primary gastrointestinal stromal tumor (GIST) and evaluate the prognostic validity of adjuvant therapy. Methods We retrospectively examined 718 patients with pathologically confirmed primary GISTs (mean age 57 ± 12.1 years; range, 14–86 years; 368 males, 350 females), who underwent R0 resection across two hospitals (internal set: n = 624; external set: n = 94). The internal set was randomly divided into training (n = 437) and validation cohorts (n = 187). Predictive efficacy of our nomogram was compared with the modified National Institutes of Health (MNIH) criterion using Kaplan–Meier analysis and multivariable Cox regression for recurrence-free survival (RFS). Results For recurrence prediction, AUC values for the CT model, combined nomogram (integrating CT features and pathological features), and MNIH criterion were 0.867/0.786/0.917 (training/validation/external test) and 0.890/0.810/0.953 versus 0.791/0.739/0.894, respectively. The combined nomogram demonstrated superior efficacy to the MNIH criterion across most probability thresholds (P < 0.001). The prognostic nomogram stratified patients into ‘high-recurrence’ and ‘low-recurrence’ groups with significantly divergent RFS. In the training cohort, the 1-, 3-, 5-, and 7-year RFS rates of these two groups were 95.9%, 85%, 73.5%, and 62.2% versus 100%, 99%, 95.5%, and 95.5%, respectively (P < 0.001). In the validation and external test cohorts, the nomograms were still significantly efficient (P < 0.001). Conclusions The combined nomogram provides improved noninvasive prediction of GIST recurrence and prognosis compared to the MNIH criterion. Trial registration Approved by the Institutional Review Board of West China Hospital, Sichuan University (No. 2020-249).

The immunotherapy landscape for gastrointestinal (GI) tumors is rapidly evolving. There is an urgent need for reliable biomarkers capable of predicting treatment outcomes to optimize therapeutic strategies and enhance patient prognosis. This review presents a comprehensive overview of biomarkers associated with the immunotherapy response of GI tumors, covering advances in molecular genetics, histopathological markers, and imaging. Key molecular biomarkers, such as microsatellite instability, tumor mutational burden, and programmed death-ligand 1 expression, remain critical for identifying patients likely to benefit from immune checkpoint inhibitors. The significance of tumor-infiltrating lymphocytes, notably the CD8+ T cell to regulatory T cell ratio, as a predictor of immunotherapy response is explored. In addition, advanced imaging techniques, including computed tomography (CT), magnetic resonance imaging, and positron emission tomography-CT, facilitate the noninvasive evaluation of tumor biology and therapeutic response. By bridging molecular and imaging data, this integrated strategy enhances precision in patient selection, treatment monitoring, and adaptive therapy design. Future studies should aim to validate these biomarkers in larger, multicenter cohorts and focus on clinical translation to advance precision medicine in GI oncology.

Introduction Neoadjuvant chemotherapy has been demonstrated to be effective and recommended as the standard treatment option in patients with locally advanced gastric or gastro-oesophageal junction (G/GOJ) cancer. In this study, we will explore the efficacy and safety of chemotherapy combined with cadonilimab, a programmed death-1/cytotoxic T lymphocyte-associated antigen-4 bispecific antibody, in neoadjuvant therapy for locally advanced G/GOJ adenocarcinoma. Methods and analysis This is a single-centre, single-arm, open-label, phase II trial that will enrol 37 patients in total. Eligible patients will be registered and receive three cycles of oxaliplatin and S-1 (SOX) regimen in combination with cadonilimab. Radical D2 (D2 lymphadenectomy) gastrectomy will be performed within 4 weeks after the last administration of chemotherapy plus cadonilimab. The primary endpoint is the pathological complete response rate. Secondary endpoints are R0 resection rate, major pathological response, 2-year disease-free survival rate, 2-year overall survival rate and safety. The first participant was recruited on 1 September 2023 and the enrolment will be completed in July 2025. Ethics and dissemination Written informed consent will be required from and provided by all the patients enrolled. The study protocol (V.3.0, 28 April 2023) has been approved by the independent ethics committee of West China Hospital, Sichuan University (approval number: 2023526) and conducted under the Declaration of Helsinki. The results of the study may provide more evidence for neoadjuvant immunotherapy combined with chemotherapy in locally advanced G/GOJ adenocarcinoma. Trial registration number ClinicalTrials.gov, NCT05948449.

Introduction: Patients with pancreatic ductal adenocarcinoma (PDAC) remain a poor prognosis despite the development of chemotherapy. Although programmed cell death 1 (PD-1) blockade has shown great efficacy in various solid tumours, its application in treating PDAC is limited. Recent studies have indicated that chemotherapy or stereotactic body radiotherapy (SBRT) may improve the antitumour effect of PD-1 blockade in patients with PDAC. The objective of this study is to evaluate the efficacy and safety of combined therapy comprising PD-1 blockade, gemcitabine plus nab-paclitaxel chemotherapy and SBRT for patients with metastatic PDAC. Methods and analysis: This is a multicentre, single-arm, prospective phase II clinical trial. Forty-three patients diagnosed with metastatic PDAC will be enrolled. The eligible patients will be intravenously administered 1000 mg/m ² gemcitabine and 125 mg/m ² nab-paclitaxel on days 1 and 8 of the 21-day cycle. Serplulimab (200 mg) will be administered intravenously on day 1 of the 21-day cycle. Furthermore, during the second cycle, the patients will undergo SBRT with doses of 33 Gy in five fractions for primary lesions or doses of 24 Gy in three fractions for metastases. The primary endpoint is the 6-month progression-free survival (PFS) rate. The secondary endpoints overall survival, PFS, overall response rate, disease control rate, time to progression, duration of response, duration of disease control and safety. Moreover, this trial seeks to investigate biomarkers such as circulating tumour DNA and circulating hybrid cells in patients diagnosed with metastatic PDAC. Ethics and dissemination: The study was approved by the Ethics Committee on Biomedical Research, West China Hospital of Sichuan University. The study results will be presented at international conferences and published in a peer-reviewed journal. Trial registration number: ChiCTR2300073237.

BACKGROUND Preoperative knowledge of mutational status of gastrointestinal stromal tumors (GISTs) is essential to guide the individualized precision therapy. AIM To develop a combined model that integrates clinical and contrast-enhanced computed tomography (CE-CT) features to predict gastric GISTs with specific genetic mutations, namely KIT exon 11 mutations or KIT exon 11 codons 557-558 deletions. METHODS A total of 231 GIST patients with definitive genetic phenotypes were divided into a training dataset and a validation dataset in a 7:3 ratio. The models were constructed using selected clinical features, conventional CT features, and radiomics features extracted from abdominal CE-CT images. Three models were developed: ModelCT sign, modelCT sign + rad, and model CTsign + rad + clinic. The diagnostic performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis and the Delong test. RESULTS The ROC analyses revealed that in the training cohort, the area under the curve (AUC) values for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic for predicting KIT exon 11 mutation were 0.743, 0.818, and 0.915, respectively. In the validation cohort, the AUC values for the same models were 0.670, 0.781, and 0.811, respectively. For predicting KIT exon 11 codons 557-558 deletions, the AUC values in the training cohort were 0.667, 0.842, and 0.720 for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic, respectively. In the validation cohort, the AUC values for the same models were 0.610, 0.782, and 0.795, respectively. Based on the decision curve analysis, it was determined that the modelCT sign + rad + clinic had clinical significance and utility. CONCLUSION Our findings demonstrate that the combined modelCT sign + rad + clinic effectively distinguishes GISTs with KIT exon 11 mutation and KIT exon 11 codons 557-558 deletions. This combined model has the potential to be valuable in assessing the genotype of GISTs.

Backgroud This study aimed to explore the non-invasive differentiation of gene mutational subtypes of KIT exon 11 from non-KIT exon 11 in small intestinal gastrointestinal stromal tumors (siGISTs) using radiogenomics based on the contrast-enhanced CT (CECT) images. Materials and Methods Patients with primary siGISTs diagnosed by surgery and pathology at our hospital from May 2010 to December 2022 were retrospectively evaluated. The patients were randomly divided into a training set and a validation set with a ratio of 7:3. The synthetic minority oversampling technique algorithm was used by screening radiomics features and constructing models. Then, CECT, radiomics, and combined models are established. The performance of the three models was evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Results This study included 91 siGISTs, including 59 (64.8%) with KIT exon 11 mutations and 32 (35.2%) with non-KIT exon 11 mutations. The diagnostic performance of the CECT model and radiomics model were sufficient, with the AUC values of its ROC curve being 0.746 and 0.869 in the training set, and 0.676 and 0.787 in the validation set, respectively. Additionally, the combined model composed of CECT and radiomics features has good performance, with AUC values of 0.882 (95% confidence interval [CI]: 0.828–0.937) and 0.618 (95% CI: 0.373–0.862), respectively. Conclusions The combined radiogenomics model based on CECT has the value of non-invasive differentiating KIT exon 11 from non-KIT exon 11 mutations in patients with siGISTs.