Nakajo M., Hirahara D., Jinguji M., Idichi T., Hirahara M., Tani A., Takumi K., Kamimura K., Ohtsuka T., Yoshiura T. . Machine learning-based prognostic modeling in gallbladder cancer using cl2011 · 記述言語： 日本語 出版者・発行元： Japanese Journal of Radiology Objectives: This study evaluates the effectiveness of machine learning (ML) models that incorporate clinical and 2-deoxy-2-[18F]fluoro-D-glucose ([18F]-FDG)-positron emission tomography (PET)-radiomic features for predicting outcomes in gallbladder cancer patients. Materials and methods: The study analyzed 52 gallbladder cancer patients who underwent pre-treatment [18F]-FDG-PET/CT scans between January 2011 and December 2021. Twenty-seven patients were assigned to the training cohort between January 2011 and January 2018, and the data randomly split into training (70%) and validation (30%) sets. The independent test cohort consisted of 25 patients between February 2018 and December 2021. Eight clinical features (T stage, N stage, M stage, Union for International Cancer Control [UICC] stage, histology, tumor size, carcinoembryonic antigen level, and carbohydrate antigen 19-9 level) and 49 radiomic features were used to forecast progression-free survival (PFS). Three feature selection methods were applied including the univariate statistical feature selection test method, least absolute shrinkage and selection operator Cox regression method and recursive feature elimination method, and two ML algorithms (Cox proportional hazard and random survival forest [RSF]) were employed. Predictive performance was assessed using the concordance index (C-index). Results: Two clinical variables (UICC stage, N stage) and three radiomic features (total lesion glycolysis, grey-level size-zone matrix_grey level non-uniformity and grey-level run-length matrix_run-length non-uniformity) were identified by the statistical feature selection method as significant for PFS prediction. The RSF model incorporating these features demonstrated strong predictive performance, with C-indices above 0.80 in both training and testing sets (training 0.81, testing 0.89). This model almost closely matched the actual and predicted progression timelines with a low mean absolute error of 1.435, a median absolute error of 0.082, and a root mean square error of 2.359. Conclusion: This study highlights the potential of using ML approaches with clinical and pre-treatment [18F]-FDG-PET radiomic data for predicting the prognosis of gallbladder cancer. DOI： 10.1007/s11604-024-01722-0 Scopus PubMed
Hirahara M., Nakajo M., Kitazano I., Jinguji M., Tani A., Takumi K., Kamimura K., Tanimoto A., Yoshiura T. . Usefulness of the Primary Tumor Standardized Uptake Value of Iodine-123 Metaiodoben1952 · 記述言語： 日本語 出版者・発行元： Molecular Imaging and Biology Purpose: To examine the usefulness of semi-quantitative analysis using the standardized uptake value (SUV) of iodine-123 metaiodobenzylguanidine ([123I]-MIBG) for predicting metastatic potential in patients with pheochromocytoma (PHEO) and paraganglioma (PGL). Procedures: This study included 18 PHEO and 2 PGL patients. [123I]-MIBG visibility and SUV-related parameters (SUVmax, SUVmean, tumor volume of [123I]-MIBG uptake [TV_MIBG], and total lesion [123I]-MIBG uptake) were compared with the pathological grading obtained using the Pheochromocytoma of the Adrenal Gland Scaled Score (PASS) and the Grading System for Adrenal Pheochromocytoma and Paraganglioma (GAPP), which are used to predict metastatic potential. The PASS scores were categorized as < 4 and ≥ 4. Based on the GAPP scores, PHEOs/PGLs were categorized as follows: well, moderately, and poorly differentiated tumors. The Mann–Whitney U test or Spearman’s rank correlation was used to assess differences or associations between two quantitative variables. Results: All PHEOs/PGLs were visualized on [123I]-MIBG scintigraphy. There were 16 PASS < 4 and 4 PASS ≥ 4 tumors. Moreover, 11 and 9 tumors were well and moderately differentiated, respectively. The uptake scores and SUV-related parameters significantly differed between tumors with a PASS score of < 4 and those with a PASS score of ≥ 4 (each, p > 0.05). Moderately differentiated tumors had significantly higher uptake scores and SUV-related parameters except TV_MIBG than well-differentiated tumors (each, p < 0.05). The GAPP score was positively correlated with the uptake scores and SUV-related parameters (each, p < 0.05) except TV_MIBG. Conclusions: The primary tumor [123I]-MIBG uptake assessed using SUV-related parameters can be an imaging tool for predicting metastatic potential in patients with PHEO/PGL. DOI： 10.1007/s11307-024-01952-8 Scopus PubMed
