AIMC Topic: Neoplasm, Residual
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Deep Learning-Based Segmentation of Locally Advanced Breast Cancer on MRI in Relation to Residual Cancer Burden: A Multi-Institutional Cohort Study.
Journal of magnetic resonance imaging : JMRI
Mar 17, 2023
BACKGROUND: While several methods have been proposed for automated assessment of breast-cancer response to neoadjuvant chemotherapy on breast MRI, limited information is available about their performance across multiple institutions.
Automated quantification of measurable residual disease in chronic lymphocytic leukemia using an artificial intelligence-assisted workflow.
Cytometry. Part B, Clinical cytometry
Feb 23, 2023
Detection of measurable residual disease (MRD) in chronic lymphocytic leukemia (CLL) is an important prognostic marker. The most common CLL MRD method in current use is multiparameter flow cytometry, but availability is limited by the need for expert...
Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients.
PloS one
Jan 6, 2023
The goal of this study was to employ novel deep-learning convolutional-neural-network (CNN) to predict pathological complete response (PCR), residual cancer burden (RCB), and progression-free survival (PFS) in breast cancer patients treated with neoa...
Towards annotation-efficient segmentation via image-to-image translation.
Medical image analysis
Sep 21, 2022
An important challenge and limiting factor in deep learning methods for medical imaging segmentation is the lack of available of annotated data to properly train models. For the specific task of tumor segmentation, the process entails clinicians labe...
Therapy resistance mechanisms in hematological malignancies.
International journal of cancer
Aug 23, 2022
Hematologic malignancies are model diseases for understanding neoplastic transformation and serve as prototypes for developing effective therapies. Indeed, the concept of systemic cancer therapy originated in hematologic malignancies and has guided t...
Machine learning methods to predict presence of residual cancer following hysterectomy.
Scientific reports
Feb 17, 2022
Surgical management for gynecologic malignancies often involves hysterectomy, often constituting the most common gynecologic surgery worldwide. Despite maximal surgical and medical care, gynecologic malignancies have a high rate of recurrence followi...
Multi-magnification-based machine learning as an ancillary tool for the pathologic assessment of shaved margins for breast carcinoma lumpectomy specimens.
Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Apr 26, 2021
The surgical margin status of breast lumpectomy specimens for invasive carcinoma and ductal carcinoma in situ (DCIS) guides clinical decisions, as positive margins are associated with higher rates of local recurrence. The "cavity shave" method of mar...
Assessing Rectal Cancer Treatment Response Using Coregistered Endorectal Photoacoustic and US Imaging Paired with Deep Learning.
Radiology
Mar 23, 2021
Background Conventional radiologic modalities perform poorly in the radiated rectum and are often unable to differentiate residual cancer from treatment scarring. Purpose To report the development and initial patient study of an imaging system compri...
Label-Free Leukemia Monitoring by Computer Vision.
Cytometry. Part A : the journal of the International Society for Analytical Cytology
Feb 24, 2020
Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. While there are a number of well-recognized prognostic biomarkers at diagnosis, the most powerful independent prognostic factor is the response of the leukemia to induction chemo...
A novel machine-learning-derived genetic score correlates with measurable residual disease and is highly predictive of outcome in acute myeloid leukemia with mutated NPM1.
Blood cancer journal
Oct 1, 2019
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