Calot's triangle is an anatomical landmark of special value in cholecystectomy. First described by Jean-François Calot as an "isosceles" triangle in his doctoral thesis in 1891, this anatomical space requires careful dissection before the ligation and division of the cystic artery and cystic duct during cholecystectomy. The modern definition of the boundaries of Calot's triangle varies from Calot's original description, although the exact timing of this change is not entirely clear. The structures within Calot's triangle and their anatomical relationships can present the surgeon with difficulties, particularly when anatomical variations are encountered. Sound knowledge of the normal anatomy of the extrahepatic biliary tract and vasculature, as well as understanding of congenital variation, is thus essential in the prevention of iatrogenic injury. The authors describe the normal anatomy of Calot's triangle and common anatomical anomalies. The incidence of structural injury is discussed, and new techniques in surgery for enhancing the visualisation of Calot's triangle are reviewed. © .

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Introduction: The anti-PD-1 immune checkpoint inhibitor nivolumab is currently approved for the treatment of patients with metastatic renal cell carcinoma (mRCC); approximately 25% of patients respond. We hypothesized that we could identify a biomarker of response using radiomics to train a machine learning classifier to predict nivolumab response outcomes. Methods: Patients with mRCC of different histologies treated with nivolumab in a single institution between 2013 and 2017 were retrospectively identified. Patients were labelled as responders (complete response [CR]/particle response [PR]/durable stable disease [SD]) or non-responders based on investigator tumor assessment using RECIST 1.1 criteria. For each patient, lesions were contoured from pre-treatment and first post-treatment computed tomography (CT) scans. This information was used to train a radial basis function support vector machine classifier to learn a prediction rule to distinguish responders from non-responders. The classifier was internally validated by a 10-fold nested cross-validation. Results: Thirty-seven patients were identified; 27 (73%) met the inclusion criteria. One hundred and four lesions were contoured from these 27 patients. The median patient age was 56 years, 78% were male, 89% had clear-cell histology, 89% had prior nephrectomy, and 89% had prior systemic therapy. There were 19 responders vs. eight non-responders. The lesions selected were lymph nodes (60%), lung metastases (23%), and renal/adrenal metastases (17%). For the classifier trained on the baseline CT scans, 69% accuracy was achieved. For the classifier trained on the first post-treatment CT scans, 66% accuracy was achieved. Conclusions: The set of radiomic signatures was found to have limited ability to discriminate nivolumab responders from non-responders. The use of novel texture features (two-point correlation measure, two-point cluster measure, and minimum spanning tree measure) did not improve performance.

Objectives: To see if inserting audited histological outcome data for each Likert score into prostate mpMRI reports was helpful for clinicians counselling patients and influenced prostate biopsy uptake. Methods: A single radiologist reported 791 mpMRI scans for query prostate cancer between 2017 and 2019. A structured template which included histological outcome data from this cohort was devised and included in 207 mpMRI reports between January and June 2021. The outcomes of the new cohort were compared with the historical cohort, and with 160 contemporaneous reports without histological outcome data from the four other radiologists in the department. The opinion of this template was sought from referring clinicians who counselled patients. Results: The proportion of patients biopsied fell from 58.0 to 32.9% overall between the n = 791 cohort and the n = 207 cohort. This was most noticeable in those scoring Likert 3, where the proportion biopsied fell from 78.4 to 42.9%. This reduction was also seen when comparing the biopsy rates of patients scored Likert three by other reporters in a contemporaneous n = 160 cohort without the audit information (65.2%) with the n = 207 cohort (42.9%). 100% of counselling clinicians were in favour and 66.7% said it gave them greater confidence to advise the patient when they did not need a biopsy. Conclusions: Fewer low-risk patients choose unnecessary biopsies when audited histological outcomes for the radiologist’s Likert scores are included in mpMRI reports. Advances in knowledge: Clinicians welcome reporter-specific audit information in mpMRI reports which could result in fewer biopsies.