Monitoring therapeutic response to neoadjuvant chemotherapy(NAC) is likely to improve NAC effectiveness in breast cancer(BC). Imaging performance seems to vary per tumour subtype(by ER and HER2 status), therefore we performed a systematic review on subtype specific imaging performance in monitoring NAC in BC. Studies examining imaging performance in predicting pathologic complete response(pCR) during NAC in BC subtypes were selected. Per study, negative- and positive predictive value, sensitivity(se) and specificity(sp), AUC and accuracy were derived. Fifteen/106 articles were included. Inter-study variability was revealed in: monitoring interval, response and pCR definitions. In ER-positive/HER2-negative BC, 1F FDG-PET/CT showed se/sp of 38%-89%/74%-100%, MRI showed se/sp of 35%-37%/87%-89%. In triple negative BC, 1F FDG-PET/CT showed se/sp of 0%-79%/95%-100%. 1F FDG-PET/CT showed in ER-positive/HER2-positive BC se/sp of 59%/80% and in ER-negative/HER2-positive 27%/88%. Evidence on imaging performance in monitoring NAC according BC subtypes is lacking. Consensus should be reached in: definitions of pCR, response and monitoring interval before starting well-designed studies.
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http://dx.doi.org/10.1016/j.critrevonc.2017.02.014 | DOI Listing |
Invest Radiol
October 2024
From the Department of Radiology and Nuclear Medicine, UKSH Lübeck, Lübeck, Germany (J.S., M.M., L.B., Y.E., J.B., M.M.S.); Institute of Medical Informatics, University of Lübeck, Lübeck, Germany (L.H., M.P.H.); Philips Research Hamburg, Hamburg, Germany (A.S., H.S.); and Institute of Interventional Radiology, UKSH Lübeck, Lübeck, Germany (M.M.S.).
Purpose: Accurate detection of central venous catheter (CVC) misplacement is crucial for patient safety and effective treatment. Existing artificial intelligence (AI) often grapple with the limitations of label inaccuracies and output interpretations that lack clinician-friendly comprehensibility. This study aims to introduce an approach that employs segmentation of support material and anatomy to enhance the precision and comprehensibility of CVC misplacement detection.
View Article and Find Full Text PDFInt 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.
J Comput Assist Tomogr
December 2024
Department of radiology, Shaoxing People's Hospital (Zhejiang University Shaoxing Hospital), Shaoxing, Zhejiang, China.
Objective: Inflammatory characteristics in pericoronary adipose tissue (PCAT) may enhance the diagnostic capability of radiomics techniques for identifying vulnerable plaques. This study aimed to evaluate the incremental value of PCAT radiomics scores in identifying vulnerable plaques defined by intravascular ultrasound imaging (IVUS).
Methods: In this retrospective study, a PCAT radiomics model was established and validated using IVUS as the reference standard.
J Chem Inf Model
December 2024
School of Physics, Shandong University, Jinan 250100, China.
In recent years, the deep learning (DL) technique has rapidly developed and shown great success in scoring the protein-ligand binding affinities. The protein-ligand conformation optimization based on DL-derived scoring functions holds broad application prospects, for instance, drug design and enzyme engineering. In this study, we evaluated the robustness of a DL-based ligand conformation optimization protocol (DeepRMSD+Vina) for optimizing structures with input perturbations by examining the predicted ligand binding poses and scoring.
View Article and Find Full Text PDFHum Reprod
December 2024
Department of Medical BioSciences, Radboudumc, Nijmegen, The Netherlands.
Study Question: How can we best achieve tissue segmentation and cell counting of multichannel-stained endometriosis sections to understand tissue composition?
Summary Answer: A combination of a machine learning-based tissue analysis software for tissue segmentation and a deep learning-based algorithm for segmentation-independent cell identification shows strong performance on the automated histological analysis of endometriosis sections.
What Is Known Already: Endometriosis is characterized by the complex interplay of various cell types and exhibits great variation between patients and endometriosis subtypes.
Study Design, Size, Duration: Endometriosis tissue samples of eight patients of different subtypes were obtained during surgery.
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