Radiomics model of diffusion-weighted whole-body imaging with background signal suppression (DWIBS) for predicting axillary lymph node status in breast cancer.

Background: In breast cancer diagnosis and treatment, non-invasive prediction of axillary lymph node (ALN) metastasis can help avoid complications related to sentinel lymph node biopsy.

Objective: This study aims to develop and evaluate machine learning models using radiomics features extracted from diffusion-weighted whole-body imaging with background signal suppression (DWIBS) examination for predicting the ALN status.

Methods: A total of 100 patients with histologically proven, invasive, clinically N0 breast cancer who underwent DWIBS examination consisting of short tau inversion recovery (STIR) and DWIBS sequences before surgery were enrolled.

Classifying presence or absence of calcifications on mammography using generative contribution mapping.

The purpose of this study was to verify the efficacy of generative contribution mapping (GCM), an explainable deep learning model for images, in classifying the presence or absence of calcifications on mammography. The learning dataset consisted of 303 full-field digital mammography (FFDM) images labeled with microcalcifications obtained from the public INbreast database without extremely dense images. FFDM images were divided into calcification and non-calcification patch images using a sliding window method with 25% overlap.

RadiomicsJ: a library to compute radiomic features.

Despite the widely recognized need for radiomics research, the development and use of full-scale radiomics-based predictive models in clinical practice remains scarce. This is because of the lack of well-established methodologies for radiomic research and the need to develop systems to support radiomic feature calculations and predictive model use. Several excellent programs for calculating radiomic features have been developed.

Deep learning approach of diffusion-weighted imaging as an outcome predictor in laryngeal and hypopharyngeal cancer patients with radiotherapy-related curative treatment: a preliminary study.

Objectives: This preliminary study aimed to develop a deep learning (DL) model using diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps to predict local recurrence and 2-year progression-free survival (PFS) in laryngeal and hypopharyngeal cancer patients treated with various forms of radiotherapy-related curative therapy.

Methods: Seventy patients with laryngeal and hypopharyngeal cancers treated by radiotherapy, chemoradiotherapy, or induction-(chemo)radiotherapy were enrolled and divided into training (N = 49) and test (N = 21) groups based on presentation timeline. All patients underwent MR before and 4 weeks after the start of radiotherapy.

[Object Detection Model Utilizing Deep Learning to Identify Retained Surgical Gauze in the Body on Postoperative Radiography: Phantom Study].

Purpose: Foreign bodies such as a surgical gauze can be retained in the body after surgery and in some cases cannot be detected by postoperative radiography. The aim of this study was to develop an object detection model capable of postsurgical detection of retained gauze in the body. The object detection model used deep learning using abdominal radiographs, and a phantom study was performed to evaluate the ability of the model to automatically detect retained surgical gauze.

Deep Learning for the Preoperative Diagnosis of Metastatic Cervical Lymph Nodes on Contrast-Enhanced Computed ToMography in Patients with Oral Squamous Cell Carcinoma.

We investigated the value of deep learning (DL) in differentiating between benign and metastatic cervical lymph nodes (LNs) using pretreatment contrast-enhanced computed tomography (CT). This retrospective study analyzed 86 metastatic and 234 benign (non-metastatic) cervical LNs at levels I-V in 39 patients with oral squamous cell carcinoma (OSCC) who underwent preoperative CT and neck dissection. LNs were randomly divided into training (70%), validation (10%), and test (20%) sets.

Development and characterization of 14 microsatellite markers for Indigofera pseudotinctoria (Fabaceae).

Premise Of The Study: Microsatellite markers can be used to evaluate population structure and genetic diversity in native populations of Indigofera pseudotinctoria (Fabaceae) and assess genetic disturbance caused by nonnative plants of the same species.

Methods And Results: We developed 14 markers for I. pseudotinctoria using next-generation sequencing and applied them to test two native populations, totaling 77 individuals, and a transplanted population, imported from a foreign country, of 17 individuals.

[Development of a Knowledge-based Application Utilizing Ontologies for the Continuing Site-specific JJ1017 Master Maintenance].

The purpose of this study was to develop the JJ1017 Knowledge-based Application (JKA) to support the continuing maintenance of a site-specific JJ1017 master defined by the JJ1017 guideline as a standard radiologic procedure master for medical information systems that are being adopted by some medical facilities in Japan. The method consisted of the following three steps: (1) construction of the JJ1017 Ontology (JJOnt) as a knowledge base using the Hozo (an environment for building/using ontologies); (2) development of modules (operation, I/O, graph modules) that are required to continue the maintenance of a site-specific JJ1017 master; and (3) unit testing of the JKA that consists of the JJOnt and the modules. As a result, the number of classes included in the JJOnt was 21,697.