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Evaluating the pathological and clinical implications of errors made by an artificial intelligence colon biopsy screening tool.
BMJ Open Gastroenterol
January 2025
Histopathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
Objective: Artificial intelligence (AI) tools for histological diagnosis offer great potential to healthcare, yet failure to understand their clinical context is delaying adoption. IGUANA (Interpretable Gland-Graphs using a Neural Aggregator) is an AI algorithm that can effectively classify colonic biopsies into normal versus abnormal categories, designed to automatically report normal cases. We performed a retrospective pathological and clinical review of the errors made by IGUANA.
View Article and Find Full Text PDFLeachables leached from a medical device during its clinical use are important due to the patient health-related effects they may have. Thus, medical devices are profiled for leachables (and/or extractables as probable leachables) by screening extracts or leachates of the medical device for released organic substances via non-targeted analysis (NTA) employing chromatographic methods coupled with mass spectrometric detection. Chromatographic mass spectral response factors for extractables and leachables vary significantly from compound to compound, complicating the application of assessment strategies such as the Analytical Evaluation Threshold (AET), which is the concentration threshold at or above which an extractable or leachable must be reported for quantitative toxicological risk assessment.
View Article and Find Full Text PDFElastography-based AI model can predict axillary status after neoadjuvant chemotherapy in breast cancer with nodal involvement: A prospective, multicenter, diagnostic study.
Int J Surg
October 2024
Department of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
Objective: To develop a model for accurate prediction of axillary lymph node (LN) status after neoadjuvant chemotherapy (NAC) in breast cancer patients with nodal involvement.
Methods: Between October 2018 and February 2024, 671 breast cancer patients with biopsy-proven LN metastasis who received NAC followed by axillary LN dissection were enrolled in this prospective, multicenter study. Preoperative ultrasound (US) images, including B-mode ultrasound (BUS) and shear wave elastography (SWE), were obtained.
A prospectively deployed deep learning-enabled automated quality assurance tool for oncological palliative spine radiation therapy.
Lancet Digit Health
January 2025
Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA. Electronic address:
Background: Palliative spine radiation therapy is prone to treatment at the wrong anatomic level. We developed a fully automated deep learning-based spine-targeting quality assurance system (DL-SpiQA) for detecting treatment at the wrong anatomic level. DL-SpiQA was evaluated based on retrospective testing of spine radiation therapy treatments and prospective clinical deployment.
View Article and Find Full Text PDFClinical characteristics of EGFR-ctDNA shedders in EGFR-mutant NSCLC patients.
Transl Oncol
December 2024
Saint Camillus International University of Medical and Health Sciences, Rome, Italy; Direzione Scientifica Fondazione Policlinico A. Gemelli IRCCS, Rome, Italy.
Background: Circulating tumor DNA (ctDNA) revolutionized the molecular diagnostics of lung cancer by enabling non-invasive, sensitive identification of actionable mutations. However, ctDNA analysis may be challenging due to tumor shedding variability, leading to false negative results. This study aims to understand the determinants for ctDNA shedding based on clinical characteristics of lung cancer patients, for a better interpretation of false negative results to be considered when ordering ctDNA analysis for clinical practice.
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