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Keywords = CTCs subclasses

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15 pages, 3744 KB  
Article
Characterizing Circulating Tumor Cells and Tumor-Derived Extracellular Vesicles in Metastatic Castration-Naive and Castration-Resistant Prostate Cancer Patients
by Khrystany T. Isebia, Eshwari Dathathri, Noortje Verschoor, Afroditi Nanou, Anouk C. De Jong, Frank A. W. Coumans, Leon W. M. M. Terstappen, Jaco Kraan, John W. M. Martens, Ruchi Bansal and Martijn P. Lolkema
Cancers 2022, 14(18), 4404; https://doi.org/10.3390/cancers14184404 - 10 Sep 2022
Cited by 8 | Viewed by 2651
Abstract
Circulating tumor cell (CTC)- and/or tumor-derived extracellular vesicle (tdEV) loads in the blood of metastatic castration-resistant prostate cancer (CRPC) patients are associated with worse overall survival and can be used as predictive markers of treatment response. In this study, we investigated the quantity/quality [...] Read more.
Circulating tumor cell (CTC)- and/or tumor-derived extracellular vesicle (tdEV) loads in the blood of metastatic castration-resistant prostate cancer (CRPC) patients are associated with worse overall survival and can be used as predictive markers of treatment response. In this study, we investigated the quantity/quality of CTCs and tdEVs in metastatic castration-naive prostate cancer (CNPC) and CRPC patients, and whether androgen deprivation therapy (ADT) affects CTCs and tdEVs. We included 104 CNPC patients before ADT initiation and 66 CRPC patients. Blood samples from 31/104 CNPC patients were obtained 6 months after ADT. CTCs and tdEVs were identified using ACCEPT software. Based on the morphology, CTCs of metastatic CNPC and CRPC patients were subdivided by manual reviewing into six subclasses. The numbers of CTCs and tdEVs were correlated in both CNPC and CRPC patients, and both CTCs (p = 0.013) and tdEVs (p = 0.005) were significantly lower in CNPC compared to CRPC patients. Qualitative differences in CTCs were observed: CTC clusters (p = 0.006) and heterogeneously CK expressing CTCs (p = 0.041) were significantly lower in CNPC patients. CTC/tdEV numbers declined 6 months after ADT. Our study showed that next to CTC-load, qualitative CTC analysis and tdEV-load may be useful in CNPC patients. Full article
(This article belongs to the Special Issue The 5th ACTC: “Liquid Biopsy in Its Best”)
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20 pages, 405 KB  
Article
A Clinical Decision Support Framework for Incremental Polyps Classification in Virtual Colonoscopy
by Mariette Awad, Yuichi Motai, Janne Näppi and Hiroyuki Yoshida
Algorithms 2010, 3(1), 1-20; https://doi.org/10.3390/a3010001 - 4 Jan 2010
Cited by 12 | Viewed by 9546
Abstract
We present in this paper a novel dynamic learning method for classifying polyp candidate detections in Computed Tomographic Colonography (CTC) using an adaptation of the Least Square Support Vector Machine (LS-SVM). The proposed technique, called Weighted Proximal Support Vector Machines (WP-SVM), [...] Read more.
We present in this paper a novel dynamic learning method for classifying polyp candidate detections in Computed Tomographic Colonography (CTC) using an adaptation of the Least Square Support Vector Machine (LS-SVM). The proposed technique, called Weighted Proximal Support Vector Machines (WP-SVM), extends the offline capabilities of the SVM scheme to address practical CTC applications. Incremental data are incorporated in the WP-SVM as a weighted vector space, and the only storage requirements are the hyperplane parameters. WP-SVM performance evaluation based on 169 clinical CTC cases using a 3D computer-aided diagnosis (CAD) scheme for feature reduction comparable favorably with previously published CTC CAD studies that have however involved only binary and offline classification schemes. The experimental results obtained from iteratively applying WP-SVM to improve detection sensitivity demonstrate its viability for incremental learning, thereby motivating further follow on research to address a wider range of true positive subclasses such as pedunculated, sessile, and flat polyps, and over a wider range of false positive subclasses such as folds, stool, and tagged materials. Full article
(This article belongs to the Special Issue Machine Learning for Medical Imaging)
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