Medical machine learning operations: a framework to facilitate clinical AI development and deployment in radiology.

The integration of machine-learning technologies into radiology practice has the potential to significantly enhance diagnostic workflows and patient care. However, the successful deployment and maintenance of medical machine-learning (MedML) systems in radiology requires robust operational frameworks. Medical machine-learning operations (MedMLOps) offer a structured approach ensuring persistent MedML reliability, safety, and clinical relevance.

Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector.

Despite being one of the most prevalent cancers, prostate cancer (PCa) shows a significantly high survival rate, provided there is timely detection and treatment. Currently, several screening and diagnostic tests are required to be carried out in order to detect PCa. These tests are often invasive, requiring either a biopsy (Gleason score and ISUP) or blood tests (PSA).

Automatic sequence identification in multicentric prostate multiparametric MRI datasets for clinical machine-learning.

Objectives: To present an accurate machine-learning (ML) method and knowledge-based heuristics for automatic sequence-type identification in multi-centric multiparametric MRI (mpMRI) datasets for prostate cancer (PCa) ML.

Methods: Retrospective prostate mpMRI studies were classified into 5 series types-T2-weighted (T2W), diffusion-weighted images (DWI), apparent diffusion coefficients (ADC), dynamic contrast-enhanced (DCE) and other series types (others). Metadata was processed for all series and two models were trained (XGBoost after custom categorical tokenization and CatBoost with raw categorical data) using 5-fold cross-validation (CV) with different data fractions for learning curve analyses.

Diffusion tensor imaging and diffusion kurtosis imaging of the pancreas - feasibility, robustness and protocol comparison in a healthy population.

Purpose: This study aims to determine the feasibility, image quality, intra-subject repeatability and inter-reader variability of Diffusion tensor (DTI) and Diffusion kurtosis imaging (DKI) for pancreatic imaging using different protocols and report normative values in healthy individuals.

Methods: Single-institution prospective study performed on healthy volunteers in a clinical 3T scanner, using two different protocols (6/16 diffusion directions). Acquisitions were repeated twice to assess intra-subject repeatability.

Impact of Scanner Manufacturer, Endorectal Coil Use, and Clinical Variables on Deep Learning-assisted Prostate Cancer Classification Using Multiparametric MRI.

Purpose To assess the effect of scanner manufacturer and scanning protocol on the performance of deep learning models to classify aggressiveness of prostate cancer (PCa) at biparametric MRI (bpMRI). Materials and Methods In this retrospective study, 5478 cases from ProstateNet, a PCa bpMRI dataset with examinations from 13 centers, were used to develop five deep learning (DL) models to predict PCa aggressiveness with minimal lesion information and test how using data from different subgroups-scanner manufacturers and endorectal coil (ERC) use (Siemens, Philips, GE with and without ERC, and the full dataset)-affects model performance. Performance was assessed using the area under the receiver operating characteristic curve (AUC).

Pancreatic Intraepithelial Neoplasia Revealed by Diffusion-Tensor MRI.

Objectives: Detecting premalignant lesions for pancreatic ductal adenocarcinoma, mainly pancreatic intraepithelial neoplasia (PanIN), is critical for early diagnosis and for understanding PanIN biology. Based on PanIN's histology, we hypothesized that diffusion tensor imaging (DTI) and T2* could detect PanIN.

Materials And Methods: DTI was explored for the detection and characterization of PanIN in genetically engineered mice (KC, KPC).

Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction.

Purpose: Emerging evidence suggests that the use of artificial intelligence can assist in the timely detection and optimization of therapeutic approach in patients with prostate cancer. The conventional perspective on radiomics encompassing segmentation and the extraction of radiomic features considers it as an independent and sequential process. However, it is not necessary to adhere to this viewpoint.

Imaging of pancreatic ductal adenocarcinoma - An update for all stages of patient management.

Background: Pancreatic ductal adenocarcinoma (PDAC) is a common and lethal cancer. From diagnosis to disease staging, response to neoadjuvant therapy assessment and patient surveillance after resection, imaging plays a central role, guiding the multidisciplinary team in decision-planning.

Review Aims And Findings: This review discusses the most up-to-date imaging recommendations, typical and atypical findings, and issues related to each step of patient management.

Development of End-to-End AI-Based MRI Image Analysis System for Predicting IDH Mutation Status of Patients with Gliomas: Multicentric Validation.

Radiogenomics has shown potential to predict genomic phenotypes from medical images. The development of models using standard-of-care pre-operative MRI images, as opposed to advanced MRI images, enables a broader reach of such models. In this work, a radiogenomics model for IDH mutation status prediction from standard-of-care MRIs in patients with glioma was developed and validated using multicentric data.

Diffusion-weighted imaging for determining response to neoadjuvant therapy in pancreatic cancer: a systematic review and meta-analysis.

Objectives: To determine the role of diffusion-weighted imaging (DWI) for predicting response to neoadjuvant therapy (NAT) in pancreatic cancer.

Materials And Methods: MEDLINE, EMBASE, and Cochrane Library databases were searched for studies evaluating the performance of apparent diffusion coefficient (ADC) to assess response to NAT. Data extracted included ADC pre- and post-NAT, for predicting response as defined by imaging, histopathology, or clinical reference standards.

Gastric schwannoma: Two case reports and review of the literature.

Gastric schwannomas are rare, slow-growing tumors whose clinical presentation is nonspecific. These are mostly benign, with a low probability of malignant transformation and an excellent prognosis. We present 2 cases of gastric schwannomas with distinct clinical features and imaging patterns, whose therapeutic approach differed.

Value of handcrafted and deep radiomic features towards training robust machine learning classifiers for prediction of prostate cancer disease aggressiveness.

There is a growing piece of evidence that artificial intelligence may be helpful in the entire prostate cancer disease continuum. However, building machine learning algorithms robust to inter- and intra-radiologist segmentation variability is still a challenge. With this goal in mind, several model training approaches were compared: removing unstable features according to the intraclass correlation coefficient (ICC); training independently with features extracted from each radiologist's mask; training with the feature average between both radiologists; extracting radiomic features from the intersection or union of masks; and creating a heterogeneous dataset by randomly selecting one of the radiologists' masks for each patient.

Retzius-sparing robot-assisted radical prostatectomy in a medium size oncological center holds adequate oncological and functional outcomes.

Retzius-sparing robot-assisted radical prostatectomy (RS-RARP) has emerged as a surgical option for patients with prostatic cancer in high-volume centers. The objective is to assess oncological and functional outcomes when implementing RS-RARP in a medium-volume center without previous experience of robotic surgery. This is a prospective observational single-center study.

Artificial intelligence and machine learning in cancer imaging.

An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field.

Challenging case of Whipple's disease: The contribution of radiology.

Whipple's disease is a rare chronic infectious disease, caused by . The disease can be challenging to diagnose due to the variable clinical manifestations and the nonspecific laboratory and imaging findings. We report the case of a 75-year-old man, complaining of weight loss and arthralgias with an insidious onset.

Prediction of Prostate Cancer Disease Aggressiveness Using Bi-Parametric Mri Radiomics.

Prostate cancer is one of the most prevalent cancers in the male population. Its diagnosis and classification rely on unspecific measures such as PSA levels and DRE, followed by biopsy, where an aggressiveness level is assigned in the form of Gleason Score. Efforts have been made in the past to use radiomics coupled with machine learning to predict prostate cancer aggressiveness from clinical images, showing promising results.

Re-staging and follow-up of rectal cancer patients with MR imaging when "Watch-and-Wait" is an option: a practical guide.

In the past nearly 20 years, organ-sparing when no apparent viable tumour is present after neoadjuvant therapy has taken an increasingly relevant role in the therapeutic management of locally-advanced rectal cancer patients. The decision to include a patient or not in a "Watch-and-Wait" program relies mainly on endoscopic assessment by skilled surgeons, and MR imaging by experienced radiologists. Strict surveillance using the same modalities is required, given the chance of a local regrowth is of approximately 25-30%, almost always surgically salvageable if caught early.

Two Rare Entities in One Patient: Mucinous Tubular and Spindle Cell Carcinoma of the Kidney and Peritoneal Adenomyomas.

Mucinous tubular and spindle cell carcinoma of the kidney is a rare subtype of renal cell carcinoma, that is believed to portend a favorable prognosis. Adenomyomas are benign tumors that typically arise from the myometrium. Extrauterine adenomyomas are extremely rare, with only a few cases reported in the literature.

Effective bowel motion reduction in mouse abdominal MRI using hyoscine butylbromide.

Purpose: Bowel motion is a significant source of artifacts in mouse abdominal MRI. Fasting and administration of hyoscine butylbromide (BUSC) have been proposed for bowel motion reduction but with inconsistent results and limited efficacy assessments. Here, we evaluate these regimes for mouse abdominal MRI at high field.

Early conformational changes at tumour bed and long term response after neoadjuvant therapy in locally-advanced rectal cancer.

Objectives: To evaluate how changes in tumour scar depth angle and thickness in the post-neoadjuvant period relate to long-term response in locally-advanced rectal cancer patients.

Methods: Informed consent was obtained from all patients and institutional review board approved this retrospective study. Sixty-nine consecutive locally-advanced rectal cancer patients who underwent neoadjuvant therapy and were selected for "Watch-and-Wait" were enrolled.