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BACKGROUND: Neoadjuvant immunochemotherapy (nICT) has emerged as a popular treatment approach for advanced gastric cancer (AGC) in clinical practice worldwide. However, the response of AGC patients to nICT displays significant heterogeneity, and no existing radiomic model utilizes baseline computed tomography to predict treatment outcomes. AIM: To establish a radiomic model to predict the response of AGC patients to nICT. METHODS: Patients with AGC who received nICT (n = 60) were randomly assigned to a training cohort (n = 42) or a test cohort (n = 18). Various machine learning models were developed using selected radiomic features and clinical risk factors to predict the response of AGC patients to nICT. An individual radiomic nomogram was established based on the chosen radiomic signature and clinical signature. The performance of all the models was assessed through receiver operating characteristic curve analysis, decision curve analysis (DCA) and the Hosmer-Lemeshow goodness-of-fit test. RESULTS: The radiomic nomogram could accurately predict the response of AGC patients to nICT. In the test cohort, the area under curve was 0.893, with a 95% confidence interval of 0.803-0.991. DCA indicated that the clinical application of the radiomic nomogram yielded greater net benefit than alternative models. CONCLUSION: A nomogram combining a radiomic signature and a clinical signature was designed to predict the efficacy of nICT in patients with AGC. This tool can assist clinicians in treatment-related decision-making.
RESUMO
BACKGROUND: Primary ovarian mucinous carcinoma is a rare histologic subtype of epithelial ovarian carcinoma and exhibits considerable morphologic overlap with secondary tumour. It is hard to differentiate primary from metastatic ovarian mucinous carcinoma by morphological and immunohistochemical features. Because of the histologic similarity between primary ovarian mucinous carcinoma and metastatic gastrointestinal carcinoma, it has been hypothesized that ovarian mucinous carcinomas might respond better to non-gynecologic regimens. However, the standard treatment of advanced ovarian mucinous carcinoma has not reached a consensus. CASE SUMMARY: A 56-year-old postmenopausal woman presented with repeated pain attacks in the right lower quadrant abdomen, accompanied by diarrhoea, anorexia, and weight loss for about 3 mo. The patient initially misdiagnosed as having gastrointestinal carcinoma because of similar pathological features. Based on the physical examination, tumour markers, imaging tests, and genetic tests, the patient was clinically diagnosed with ovary mucinous adenocarcinoma. Whether gastrointestinal-type chemotherapy or gynecologic chemotherapy was a favourable choice for patients with advanced ovarian mucinous cancer had not been determined. The patient received a chemotherapy regimen based on the histologic characteristics rather than the tumour origin. The patient received nine cycles of FOLFOX and bevacizumab. This was followed by seven cycles of bevacizumab maintenance therapy for 9 mo. Satisfactory therapeutic efficacy was achieved. CONCLUSION: The genetic analysis might be used in the differential diagnosis of primary ovarian mucinous carcinoma and non-gynecologic mucinous carcinoma. Moreover, primary ovarian mucinous carcinoma patients could benefit from gastrointestinal-type chemotherapy.