Publications by authors named "K Takumi"
Objectives: This study evaluates the effectiveness of machine learning (ML) models that incorporate clinical and 2-deoxy-2-[F]fluoro-D-glucose ([F]-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 [F]-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.
Article Synopsis
- The study aimed to explore the use of time-dependent diffusion MRI (dMRI) to differentiate between functioning and nonfunctioning pituitary adenomas (PAs).
- It involved 54 participants, measuring the apparent diffusion coefficients (ADCs) of various types of PAs and finding that the cADC values were significantly higher in functioning PAs compared to nonfunctioning ones.
- The results suggest that cADC is an effective diagnostic tool, particularly for distinguishing growth hormone-producing PAs from nonfunctioning PAs.
Purpose: To examine the usefulness of semi-quantitative analysis using the standardized uptake value (SUV) of iodine-123 metaiodobenzylguanidine ([I]-MIBG) for predicting metastatic potential in patients with pheochromocytoma (PHEO) and paraganglioma (PGL).
Procedures: This study included 18 PHEO and 2 PGL patients. [I]-MIBG visibility and SUV-related parameters (SUVmax, SUVmean, tumor volume of [I]-MIBG uptake [TV_MIBG], and total lesion [I]-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.
Objectives: To develop and identify machine learning (ML) models using pretreatment 2-deoxy-2-[F]fluoro-D-glucose ([F]-FDG)-positron emission tomography (PET)-based radiomic features to differentiate benign from malignant parotid gland diseases (PGDs).
Materials And Methods: This retrospective study included 62 patients with 63 PGDs who underwent pretreatment [F]-FDG-PET/computed tomography (CT). The lesions were assigned to the training (n = 44) and testing (n = 19) cohorts.
Background: Pancreatic ductal occlusion can accompany pancreatic head cancer, leading to pancreatic exocrine insufficiency (PEI) and adverse effects on nutritional status and postoperative outcomes. We investigated its impact on nutritional status, body composition, and postoperative outcomes in patients with pancreatic head cancer undergoing neoadjuvant therapy (NAT).
Methods: We analyzed 136 patients with pancreatic head cancer who underwent NAT prior to intended pancreaticoduodenectomy (PD) between 2015 and 2022.