Computed tomography (CT) imaging is the most common imaging modality for the diagnosis and staging of gastric cancer. The aim of this study is was to prospectively explore the ability of quantitative spectral CT parameters in the detection of gastric cancer and its histologic types. A total of 87 gastric adenocarcinoma (43 poorly and 44 well-differentiated) patients and 36 patients with benign gastric wall lesions (25 inflammation and 11 normal), who underwent dual-phase enhanced spectral CT examination, were retrospectively enrolled in this study. Iodine concentration (IC) and normalized iodine concentration (nIC) during arterial phase (AP) and portal venous phase (PP) were measured thrice in each patient by two blinded radiologists. Moreover, intraclass correlation coefficient (ICC) was used to assess the interobserver reproducibility. Differences of IC and nIC values between gastric cancer and benign lesion groups were compared using Mann-Whitney U test. Furthermore, the gender, age, location, thickness and histological types of gastric adenocarcinoma were analyzed by Mann-Whitney U test or Kruskal-Wallis H test. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of IC and nIC values, and the optimal cut-off value was calculated with Youden J. An excellent interobserver agreement (ICC > 0.6) was achieved for IC. Notably, the values of ICAP, ICPP, nICAP and nICPP were significantly higher in gastric cancer group (Z = 5.870, 3.894, 2.009 and 10.137, respectively; P < 0.05) than those in benign lesion group. Additionally, the values of ICAP, ICPP, nICAP and nICPP were significantly higher in poorly differentiated gastric adenocarcinoma group (Z = 4.118, 5.637, 6.729 and 2.950, respectively; P < 0.005) than those in well-differentiated gastric adenocarcinoma group. There were no statistically significant differences in the values of ICAP, ICPP, nICAP and nICPP between age, gender, tumor thickness and tumor location. Furthermore, the area under the curve (AUC) values of ICAP, nICAP, ICPP and nICPP were 0.745, 0.584, 0.662, and 0.932, respectively, for gastric cancer detection; while 0.756, 0.919, 0.851 and 0.684, respectively, in discriminating poorly differentiated gastric adenocarcinoma. IC values exhibited great potential in the preoperative and non-invasive diagnosis of gastric cancer and its histological types. In particular, nICPP is more effective for the identification of gastric cancer, whereas nICAP is more effective in discriminating poorly differentiated gastric adenocarcinoma.

Carcinosarcoma of the liver is a very rare tumor composed of a mixture of carcinomatous and sarcomatous elements. Less than 25 adequately documented cases have been reported, with inadequate description of imaging features. In order to improve the awareness of this rare tumor, this study aimed to analyze the clinicopathologic and imaging features of six cases of hepatic carcinosarcoma (HCS) confirmed by surgical pathologic evaluation. We retrospectively studied the clinicopathologic and imaging features of six cases of HCS (matching the World Health Organization definition) and discussed the differential diagnosis on the basis of imaging findings. The patients, including five men and one woman, were 38 to 69 years of age. Five patients underwent CT scans, one underwent MRI scans. While 3 patients were positive for hepatitis-B surface antigen, 2 had cirrhosis. The largest tumor diameter ranged from 5.0 to 21.0 cm. Satellite nodules, venous thrombi, and organ invasion (gastric wall, gallbladder, and right adrenal gland) were identified. Pathologically, the carcinomatous components corresponded to hepatocellular carcinoma in three cases, cholangiocellular carcinoma in one case, and adenocarcinoma in two cases. The sarcomatous components exhibited complex features, with undifferentiated spindle cells in five cases and a leiomyosarcoma in one. All tumors showed heterogeneous density/intensity with extensive cystic change and necrosis; spot calcification was observed in one case. Capsule was not identified. While four tumors showed heterogeneous hypervascular enhancement, two showed hypovascular enhancement. All patients underwent surgical resection. The follow-up period ranged from 2 to 18 months. Four patients died from recurrence and metastasis. The clinical and imaging features of HCS are heterogeneous. Due to the heterogenous nature and very low morbidity of HCS, combination of careful analysis of imaging findings and clinical features might be useful for a more accurate diagnosis of HCS.

Identifying gastric heterotopic pancreas and stromal tumors is difficult. Few studies have reported computed tomography (CT) findings for differentiating lesions less than 3 cm in diameter. In this study, we aimed to identify clinical characteristics and CT findings that can differentiate gastric heterotopic pancreatic lesions from stromal tumors less than 3 cm in diameter. A total of 132 patients with pathologically confirmed gastric heterotopic pancreas (n = 66) and stromal tumors (n = 66) were included. Each group was divided into primary (n = 50) and validation cohort (n = 16). Clinical characteristics and CT findings were retrospectively reviewed. CT findings included location, border, contour, growth pattern, enhancement pattern and grade, the enhancement value of tumor, enhancement ratio of tumor, and enhancement ratio of tumor to pancreas in venous phase. The findings in the two groups were compared using the Pearson χ2 test or Student t-test. Receiver operating characteristic curves were used to determine areas under the curve and optimal cut-offs. Significant differences were observed between heterotopic pancreas and stromal tumors in the distribution of tumor location, border, contour (all P < 0.001), enhancement values (P < 0.001), enhancement ratios of tumors (P < 0.001), and enhancement ratios of tumors to pancreas (P < 0.001). No significant differences existed in growth pattern (P = 0.203). The area under the curve differed significantly between enhancement ratio of tumor to pancreas and enhancement ratio (P = 0.030). There were significant differences in above characteristics between two groups in validation cohort. Heterotopic pancreas has characteristic CT features differentiating it from stromal tumors.

Objective: To develop and validate a deep learning model based on multi-lesion and time series CT images in predicting overall survival (OS) in patients with stage IV gastric cancer (GC) receiving anti-HER2 targeted therapy. Methods: A total of 207 patients were enrolled in this multicenter study, with 137 patients for retrospective training and internal validation, 33 patients for prospective validation, and 37 patients for external validation. All patients received anti-HER2 targeted therapy and underwent pre- and post-treatment CT scans (baseline and at least one follow-up). The proposed deep learning model evaluated the multiple lesions in time series CT images to predict risk probabilities. We further evaluated and validated the risk score of the nomogram combining a two-follow-up lesion-based deep learning model (LDLM-2F), tumor markers, and clinical information for predicting the benefits from treatment (Nomo-LDLM-2F). Results: In the internal validation and prospective cohorts, the one-year AUCs for Nomo-LDLM-2F using the time series medical images and tumor markers were 0.894 (0.728–1.000) and 0.809 (0.561–1.000), respectively. In the external validation cohort, the one-year AUC of Nomo-LDLM-2F without tumor markers was 0.771 (0.510–1.000). Patients with a low Nomo-LDLM-2F score derived survival benefits from anti-HER2 targeted therapy significantly compared to those with a high Nomo-LDLM-2F score (all p < 0.05). Conclusion: The Nomo-LDLM-2F score derived from multi-lesion and time series CT images holds promise for the effective readout of OS probability in patients with HER2-positive stage IV GC receiving anti-HER2 therapy. Critical relevance statement: The deep learning model using baseline and early follow-up CT images aims to predict OS in patients with stage IV gastric cancer receiving anti-HER2 targeted therapy. This model highlights the spatiotemporal heterogeneity of stage IV GC, assisting clinicians in the early evaluation of the efficacy of anti-HER2 therapy. Key points: • Multi-lesion and time series model revealed the spatiotemporal heterogeneity in anti-HER2 therapy. • The Nomo-LDLM-2F score was a valuable prognostic marker for anti-HER2 therapy. • CT-based deep learning model incorporating time-series tumor markers improved performance. Graphical