Purpose To investigate the feasibility of multi-sequence MRI-based radiomics for predicting epidermal growth factor receptor(EGFR)mutation status in brain metastases from non-small cell lung cancer(NSCLC).Materials and Methods This retrospective study included 237 patients with NSCLC brain metastases from the Second Xiangya Hospital of Central South University(January 1,2017 to December 31,2023)who underwent EGFR genetic testing.All patients underwent pretreatment brain MRI including contrast-enhanced T1-weighted,T2-weighted FLAIR,and T2-weighted sequences,along with chest CT for primary lung lesions.EGFR mutations were identified in 120 patients.Using December 31,2021 as the cutoff date,patients were divided into training(n=146)and validation(n=91)cohorts.Senior radiologists delineated brain metastases on multi-sequence MRI and primary lesions on CT.A total of 851 radiomic features were extracted using PyRadiomics.Following feature selection,machine learning models were constructed using support vector machine algorithm and compared with least absolute shrinkage and selection operator-derived radiomic signatures.Five models were developed:three single-sequence MRI models,a multi-sequence MRI fusion model,and a CT model,with diagnostic performance evaluated by area under the receiver operating characteristic curve.Results The multi-sequence MRI fusion model demonstrated superior performance across all imaging types.The least absolute shrinkage and selection operator and support vector machine models achieved training set area under the curve of 0.854(95%CI 0.748-0.960)and 0.948(95%CI 0.923-0.973),respectively,and validation set area under the curve of 0.810(95%CI 0.751-0.869)and 0.951(95%CI 0.917-0.985),respectively.The optimal prediction model utilized support vector machine algorithm with multi-sequence MRI features.Conclusion Pretreatment multi-sequence MRI radiomics combined with machine learning accurately predicts EGFR mutation status in NSCLC patients with brain metastases.

Purpose To investigate the feasibility of multi-sequence MRI-based radiomics for predicting epidermal growth factor receptor(EGFR)mutation status in brain metastases from non-small cell lung cancer(NSCLC).Materials and Methods This retrospective study included 237 patients with NSCLC brain metastases from the Second Xiangya Hospital of Central South University(January 1,2017 to December 31,2023)who underwent EGFR genetic testing.All patients underwent pretreatment brain MRI including contrast-enhanced T1-weighted,T2-weighted FLAIR,and T2-weighted sequences,along with chest CT for primary lung lesions.EGFR mutations were identified in 120 patients.Using December 31,2021 as the cutoff date,patients were divided into training(n=146)and validation(n=91)cohorts.Senior radiologists delineated brain metastases on multi-sequence MRI and primary lesions on CT.A total of 851 radiomic features were extracted using PyRadiomics.Following feature selection,machine learning models were constructed using support vector machine algorithm and compared with least absolute shrinkage and selection operator-derived radiomic signatures.Five models were developed:three single-sequence MRI models,a multi-sequence MRI fusion model,and a CT model,with diagnostic performance evaluated by area under the receiver operating characteristic curve.Results The multi-sequence MRI fusion model demonstrated superior performance across all imaging types.The least absolute shrinkage and selection operator and support vector machine models achieved training set area under the curve of 0.854(95%CI 0.748-0.960)and 0.948(95%CI 0.923-0.973),respectively,and validation set area under the curve of 0.810(95%CI 0.751-0.869)and 0.951(95%CI 0.917-0.985),respectively.The optimal prediction model utilized support vector machine algorithm with multi-sequence MRI features.Conclusion Pretreatment multi-sequence MRI radiomics combined with machine learning accurately predicts EGFR mutation status in NSCLC patients with brain metastases.

Objective:To investigate the safety and efficacy of FOLFOX (5-fluorouracil + calcium folinate + oxaliplatin) hepatic arterial infusion chemotherapy (FOLFOX-HAIC) combined with immune and targeted therapy as triple combination therapy for patients with single China Liver Cancer Staging (CNLC) Ⅰb hepatocellular carcinoma.Methods:A total of 20 patients with single CNLC Ⅰb hepatocellular carcinoma who received FOLFOX-HAIC combined with immune and targeted therapy as triple combination therapy in the First Affiliated Hospital of Guangxi Medical University from October 2021 to August 2022 were included. The clinical data of all patients was retrospectively analyzed. There were 18 males and 2 females, with the age of (55.1±9.9) years. Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 and Modified Response Evaluation Criteria in Solid Tumors (mRECIST) were used to evaluate the efficacy of FOLFOX-HAIC combined with immune and targeted therapy, and the clinical safety of triple combination therapy was evaluated by common terminology criteria for adverse events 4.0.Results:According to RECIST 1.1, objective response rate of 20 patients was 70.0% (14/20) and disease control rate was 100.0% (20/20) after 2 cycles of treatment (one cycle of FOLFOX-HAIC plus programmed death-1 antibody). According to mRECIST, objective response rate was 90.0% (18/20) and the disease control rate was 100.0% (20/20) after 2 cycles of treatment. Following the treatment, 12 patients (60.0%) received liver tumor resection, and all of them achieved R 0 resection, 2 patients (10.0%) received radiotherapy, 3 patients (15.0%) stopped drug treatment for surgery, 2 patients (10.0%) refused surgery, and 1 patient (5.0%) died of multiple organ failure caused by immune hepatitis. According to pathological results, 3 patients (25.0%, 3/12) achieved pathological complete response, and 4 patients (33.3%, 4/12) achieved major pathological response. In the safety evaluation, the overall incidence of adverse events was 100.0% (20/20). Seven patients (35.0%) had grade 3 adverse events and 1 patient (5.0%) died of multiple organ failure due to immune hepatitis (grade 5). Grade 1-3 adverse events could be relieved after symptomatic treatment. Conclusion:The triple combination therapy of FOLFOX-HAIC combined with immune and targeted therapy is safe and has high objective response rate and disease control rate, which could be a new strategy for the neoadjuvant treatment of hepatocellular carcinoma.