Bilateral Adrenal Metastasis of Prostatic Adenocarcinoma on 68 Ga-PSMA PET/CT Imaging.

An 84-year-old man with prostate adenocarcinoma underwent 68 Ga-PSMA PET/CT due to PSA recurrence. Foci of 68 Ga-PSMA uptake were observed in bilateral adrenal glands. Adrenal MRI showed metastasis only in the left adrenal gland.

Can an apparent diffusion coefficient of uterine fibroid before uterine artery embolization predict potential fibroid response?

Objectives: ACOG guidance confirms the use of uterine artery embolisation (UAE) as an alternative to hysterectomy or myomectomy. The main objective of this article is to evaluate the ability of preoperative magnetic resonance ımaging (MRI) to study the relationship between uterine fibroid reduction and diffusion coefficient (ADC) value after UAE. This is a relevant topic with the growing interest in using ADC as a noninvasive imaging biomarker for monitoring tissue changes and predicting uterine fibroid response to UAE over the past years.

Radiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI.

Purpose: Variable response to neoadjuvant chemoradiotherapy (nCRT) is observed among individuals with locally advanced rectal cancer (LARC), having a significant impact on patient management. In this work, we aimed to investigate the potential value of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics in predicting therapeutic response to nCRT in patients with LARC.

Materials And Methods: Seventy-six patients with LARC were included in this retrospective study.

Tubular gastric adenocarcinoma: machine learning-based CT texture analysis for predicting lymphovascular and perineural invasion.

Purpose: Lymphovascular invasion (LVI) and perineural invasion (PNI) are associated with poor prognosis in gastric cancers. In this work, we aimed to investigate the potential role of computed tomography (CT) texture analysis in predicting LVI and PNI in patients with tubular gastric adenocarcinoma (GAC) using a machine learning (ML) approach.

Methods: Sixty-eight patients who underwent total gastrectomy with curative (R0) resection and D2-lymphadenectomy were included in this retrospective study.

Machine Learning in Radiomic Renal Mass Characterization: Fundamentals, Applications, Challenges, and Future Directions.

The purpose of this study is to provide an overview of the traditional machine learning (ML)-based and deep learning-based radiomic approaches, with focus placed on renal mass characterization. ML currently has a very low barrier to entry into general medical practice because of the availability of many open-source, free, and easy-to-use toolboxes. Therefore, it should not be surprising to see its related applications in renal mass characterization.

Computed tomography texture analysis in patients with gastric cancer: a quantitative imaging biomarker for preoperative evaluation before neoadjuvant chemotherapy treatment.

Purpose: The aim of the study is to explore the role of computed tomography texture analysis (CT-TA) for predicting clinical T and N stages and tumor grade before neoadjuvant chemotherapy treatment in gastric cancer (GC) patients during the preoperative period.

Materials And Methods: CT images of 114 patients with GC were included in this retrospective study. Following pre-processing steps, textural features were extracted using MaZda software in the portal venous phase.

Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis.

Rationale And Objectives: This study aimed to investigate whether benign and malignant renal solid masses could be distinguished through machine learning (ML)-based computed tomography (CT) texture analysis.

Materials And Methods: Seventy-nine patients with 84 solid renal masses (21 benign; 63 malignant) from a single center were included in this retrospective study. Malignant masses included common renal cell carcinoma (RCC) subtypes: clear cell RCC, papillary cell RCC, and chromophobe RCC.

Textural differences between renal cell carcinoma subtypes: Machine learning-based quantitative computed tomography texture analysis with independent external validation.

Objective: To develop externally validated, reproducible, and generalizable models for distinguishing three major subtypes of renal cell carcinomas (RCCs) using machine learning-based quantitative computed tomography (CT) texture analysis (qCT-TA).

Materials And Methods: Sixty-eight RCCs were included in this retrospective study for model development and internal validation. Another 26 RCCs were included from public databases (The Cancer Genome Atlas-TCGA) for independent external validation.

Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade.

Objective: To evaluate the performance of quantitative computed tomography (CT) texture analysis using different machine learning (ML) classifiers for discriminating low and high nuclear grade clear cell renal cell carcinomas (cc-RCCs).

Materials And Methods: This retrospective study included 53 patients with pathologically proven 54 cc-RCCs (31 low-grade [grade 1 or 2]; 23 high-grade [grade 3 or 4]). In one patient, two synchronous cc-RCCs were included in the analysis.

Retrospective study of 24 cases of acute appendiceal diverticulitis: CT findings and pathological correlations.

Purpose: Appendiceal diverticulitis is relatively rare and is difficult to distinguish clinically and radiologically from acute appendicitis. The aim of this study was to describe the computed tomography (CT) findings of acute appendiceal diverticulitis.

Materials And Methods: Among the 1329 patients who underwent appendectomy at our institution between January 2010 and July 2015, 28 were diagnosed pathologically with appendiceal diverticulitis, including 24 patients who were evaluated by preoperative CT.

Preoperative localization of distal colorectal tumours.

Background: The detection of true localization of the tumour are crucial to driving the proper treatment algorithm in distally-located colorectal cancers (CRCs). The performance of four methods; colonoscopy, computed tomography (CT), magnetic resonance imaging (MRI), and fluoro-deoxy-glucose-positron emission tomography scan (FDG/PET-CT), were evaluated to identify the localizations of distal colorectal malignancies according to the rectum, sigmoid colon and recto- sigmoid junction (RSJ).

Materials And Methods: Medical records of patients who underwent colorectal surgery for tumours located on the sigmoid colon, RSJ, or rectum were reviewed retrospectively.

Does the abdominal ultrasonography reliable in the diagnosis of postoperative pancreatic fistula after pancreaticoduodenectomy in the first postoperative week?

Purpose: We evaluated the efficacy of ultrasonography (US) in the early postoperative period after pancreaticoduodenectomy (PD) to diagnose postoperative-pancreatic-fistula (POPF). Early diagnosis is important to prevent POPF-dependent mortality after PD. The value of radiological modalities for early diagnosing POPF is not clear.