Objectives We aim to illustrate the multimodal imaging spectrum of hepatic involvement in tuberculosis (TB). Whilst disseminated tuberculosis on imaging typically manifests as multiple small nodular lesions scattered in the liver parenchyma, isolated hepatic tuberculosis remains a rare and intriguing entity. Methods Indubitably, imaging is the mainstay for detection of tubercular hepatic lesions which display a broad spectrum of imaging manifestations on different modalities. While sonography and computed tomography (CT) findings have been described in some detail, there is a paucity of literature on magnetic resonance imaging (MRI) features. Due to a significant overlap with other commoner and similar appearing hepatic lesions, hepatic tuberculosis is often either misdiagnosed or labelled as indeterminate lesions. This article is a compendium of cases highlighting the spectrum of imaging patterns that can be encountered in patients with isolated primary hepatic tuberculosis as well as disseminated (secondary) disease. Rare patterns of primary disease such as tubercular cholangitis, hypervascular liver masses, and those with vascular complications are also illustrated and discussed. Conclusions Imaging plays a valuable role in the detection of tubercular hepatic lesions. Also, imaging can be helpful in their characterisation and for assessing associated complications. Teaching points • Hepatic TB has myriad imaging manifestations and is often confounded with neoplastic lesions. • Imaging patterns include miliary TB, macronodular TB, serohepatic TB and tubercular cholangitis. • Concurrent splenic, nodal or pulmonary involvements are helpful pointers towards the diagnosis. • Miliary calcifications along the bile ducts are characteristic of tubercular cholangitis. • Histological/microbiological confirmation is often necessary to confirm the diagnosis.

The larynx, or the voice-box, is a common site of occurrence of Head and Neck cancers. Yet, automated segmentation of the larynx has been receiving very little attention. Segmentation of organs is an essential step in cancer treatment-planning. Computed Tomography scans are routinely used to assess the extent of tumor spread in the Head and Neck as they are fast to acquire and tolerant to some movement.This paper reviews various automated detection and segmentation methods used for the larynx on Computed Tomography images. Image registration and deep learning approaches to segmenting the laryngeal anatomy are compared, highlighting their strengths and shortcomings. A list of available annotated laryngeal computed tomography datasets is compiled for encouraging further research. Commercial software currently available for larynx contouring are briefed in our work.We conclude that the lack of standardisation on larynx boundaries and the complexity of the relatively small structure makes automated segmentation of the larynx on computed tomography images a challenge. Reliable computer aided intervention in the contouring and segmentation process will help clinicians easily verify their findings and look for oversight in diagnosis. This review is useful for research that works with artificial intelligence in Head and Neck cancer, specifically that deals with the segmentation of laryngeal anatomy.

Purpose: To evaluate the diagnostic reliability of Thyroid Imaging Reporting and Data System (TI-RADS) classifications described by American College of Radiology (ACR) and Kwak et al. by calculating the risk of malignancy, to assess the role of TI-RADS in reducing fine-needle aspiration cytology (FNAC) of benign lesions.Material and methods: This was a prospective study during the period from December 2017 to August 2018. Thyroid nodules were classified using ACR TI-RADS and TI-RADS proposed by Kwak et al. The TI-RADS categorisations were compared to the final diagnosis obtained by cytopathological/histopathological analysis. The risk of malignancy for each category was calculated. Sensitivity, specificity, and positive and negative predictive values for individual suspicious ultrasound features were also assessed.Results: We evaluated a total of 127 thyroid nodules. The risk of malignancy was 0% in ACR TR1, 0% in ACR TR2, 6.9% in ACR TR3, 29.2% in ACR TR4, and 80% in ACR TR5 categories. The risk of malignancy for TI-RADS according to Kwak et al. were 0%, 0%, 21.5%, 32.4%, 100% for TI-RADS 2, 3, 4A, 4B, and 4C categories, respectively. Kwak TI-RADS 2 and 3 had higher sensitivity in predicting benignity compared to ACR TR1 and 2 (35.4% vs. 25.9%).Conclusions: We found TI-RADS classification to be a reliable, non-invasive, and practical method for assessing thyroid nodules in routine practice. TI-RADS can safely avert avoidable FNACs in a significant proportion of benign thyroid lesions.

Suspecting a müllerian duct anomaly (MDA), a plain MRI study of the pelvis was performed with the following sequences: T2-weighted (T2W) images in coronal, axial and sagittal planes, T1W axial and T2 FS coronal images.

Background: Brain tumors present a complex challenge in clinical oncology, where precise diagnosis and classification are pivotal for effective treatment planning. Radiomics, a burgeoning field in neuro-oncology, involves extracting and analyzing numerous quantitative features from medical images. This approach captures subtle spatial and textural information imperceptible to the human eye. However, implementation in clinical practice is still distant, and concerns have been raised regarding the methodological quality of radiomic studies. Methodology: A systematic literature search was performed to identify original articles focused on the use of radiomics for brain tumors from 2015 based on the inclusion and exclusion criteria. The radiomic features train machine learning models for glioma classification, and data are split into training and testing subsets to validate the model accuracy, reliability, and generalizability. The present study systematically reviews the status of radiomic studies concerning brain tumors, also using the radiomics quality score (RQS) to assess the quality of the methodology used in each study. Results: A systematic search of PubMed identified 300 articles, with 18 studies meeting the inclusion criteria for qualitative synthesis. These studies collectively demonstrate the potential of radiomics-based machine learning models in accurately distinguishing between glioma subtypes and grades. Various imaging modalities, including MRI, PET/CT, and advanced techniques like ASL and DTI, were utilized to extract radiomic features for analysis. Machine learning algorithms such as deep learning networks, support vector machines, random forests, and logistic regression were applied to develop predictive models. Conclusions: The present study indicates high accuracies in glioma classification, outperforming traditional imaging methods and inexperienced radiologists in some cases. Further validation and standardization efforts are warranted to facilitate the clinical integration of radiomics into routine practice, ultimately enhancing glioma management and patient outcomes. Open science practices: Machine learning using MRI radiomic features provides a simple, noninvasive, and cost-effective method for glioma classification, enhancing transparency, reproducibility, and collaboration within the scientific community.