A review of artificial intelligence in prostate cancer detection on imaging

A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care

A timely computer-aided detection system for acute ischemic and hemorrhagic stroke on CT in an emergency environment

Standalone Presentations: no. LL-IN1105BACKGROUND: When a patient is accepted in the emergency room suspected of stroke, time is of the most importance. The infarct brain area suffers irreparable damage as soon as three hours after the onset of stroke symptoms. Non-contrast CT scan is the standard first line of investigation used to identify hemorrhagic stroke cases. However, CT brain images do not show hyperacute ischemia and small hemorrhage clearly and thus may be missed by emergency physicians. We reported a timely computer-aided detection (CAD) system for small hemorrhages on CT that has been successfully developed as an aid to ER physicians to help improve detection for Acute Intracranial Hemorrhage (AIH). This CAD system has been enhanced for diagnosis of acute ischemic stroke in addition to hemorrhagic stroke, which becomes a more complete and clinically useful tool for assisting emergency physicians and radiologists. In the detection algorithm, brain matter is first segmented, realigned, and left-right brain symmetry is evaluated. As in the AIH system, the system confirms hemorrhagic stroke by detecting blood presence with anatomical and medical knowledge-based criteria. For detecting ischemia, signs such as regional hypodensity, blurring of grey and white matter differentiation, effacement of cerebral sulci, and hyperdensity in middle cerebral artery, are evaluated …published_or_final_versio

Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session.

At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities

Significance of Atypia Found on Needle Biopsy of the Breast: Correlation with Surgical Outcome

Although core needle biopsy has been shown to be effective in diagnosing both benign and malignant mammographically detected lesions in the breast, it has also been shown to underestimate cancer most likely due to sampling error. Since a diagnosis of atypical hyperplasia versus malignancy is based on quantitative factors (which could be affected by an error in sampling), the current recommendation is surgical excision for atypical hyperplasia diagnosed on core biopsy. The purpose of the study was to determine if a subset of patients with atypia diagnosed by core biopsy fit the Breast Imaging Reporting and Data Systems (BI-RADS) Category 3, probably benign, definition of having a less than 2% chance of being carcinoma at subsequent surgical excision when comparing histologic subtype, mammographic findings, core biopsy factors, and clinical factors. For this subset of patients, imaging follow-up, rather than surgical excision could be recommended. Retrospective searches of the breast imaging and pathology databases from 1992 to August 2005 were performed to identify all cases of atypia found on core biopsy. The data collection and database use were HIPAA-compliant and followed the protocols of the institutional review board. The pathology reports were reviewed to determine the histologic type: atypical ductal hyperplasia (ADH), atypical lobular hyperplasia (ALH), mixed, or other atypia. The ADHs were further classified as to focal/mild, not otherwise stated (NOS), or marked based on the pathology reports. Follow-up information was obtained to identify cases in which lesions that were initially diagnosed as atypia at the time of core biopsy were later upgraded to malignancy after subsequent surgical excision or mammographic follow-up. The histologic subtype, mammographic findings, core biopsy factors, and clinical factors were compared to lesions which were not upgraded to carcinoma. The results were analyzed with a Chi-square test, with p\u3c 0.05 indicative of significant difference. There were 327 cases of atypia found in the 3898 (8%) core needle biopsies that were performed during the above stated time period. The histologic subtypes were: ADH (75%), ALH (13%), mixed (4%), other (7%). There was an overall malignancy rate of 13%. Malignancy was found in 14% of ADH lesions, 5% of ALH, 20% of mixed, and 10% of other atypias on excision. The 215 ADH cases were further examined in their histologic subtypes (37% were focal/mild, 42% NOS, and 20% ADH marked). Malignancy was found in 6% focal/mild ADH, 10% NOS ADH, and 40% ADH marked. When comparing all the factors considered, the lowest underestimation rate (3%) was found in patients with focal/mild ADH diagnosed with a vacuum- assisted 11-gauge biopsy needle. Upgrade rates vary significantly depending on classification of ADH and the type of atypia. While severe forms of atypia (NOS ADH, ADH marked, mixed atypias, and other atypias) should continue to receive routine surgical excision, there are selected subsets of patients with whom other management options could possibly be considered. For patients with focal/ mild atypical ductal hyperplasia diagnosed at the time of core biopsy with a vacuum-assisted 11-gauge needle, imaging follow-up (mammography or MRI) could be considered on an individual basis