Deep Learning for Predicting Metastasis on Melanoma WSIs

Northern Europe has the second highest mortality rate of melanoma globally. In 2020, the mortality rate of melanoma rose to 1.9 per 100 000 habitants. Melanoma prognosis is based on a pathologist's subjective visual analysis of the patient's tumor. This methodology is heavily time-consuming, and the prognosis variability among experts is notable, drastically jeopardizing its reproducibility. Thus, the need for faster and more reproducible methods arises. Machine learning has paved its way into digital pathology, but so far, most contributions are on localization, segmentation, and diagnostics, with little emphasis on prognostics. This paper presents a convolutional neural network (CNN) method based on VGG16 to predict melanoma prognosis as the presence of metastasis within five years. Patches are extracted from regions of interest from Whole Slide Images (WSIs) at different magnification levels used in model training and validation. Results infer that utilizing WSI patches at 20x magnification level has the best performance, with an F1 score of 0.7667 and an AUC of 0.81

Quantifying the effect of color processing on blood and damaged tissue detection in Whole Slide Images

Histological tissue examination has been a longstanding practice for cancer diagnosis where pathologists identify the presence of tumors on glass slides. Slides acquired from laboratory routine may contain unintentional artifacts due to complications in surgical resection. Blood and damaged tissue artifacts are two common problems associated with transurethral resection of the bladder tumor. Differences in histotechnical procedures among laboratories may also result in color variations and minor inconsistencies in outcome. A digitized version of a glass slide known as a whole slide image (WSI) holds enormous potential for automated diagnostics. The presence of irrelevant areas in a WSI undermines diagnostic value for pathologists as well as computational pathology (CPATH) systems. Therefore, automatic detection and exclusion of diagnostically irrelevant areas may lead to more reliable predictions. In this paper, we are detecting blood and damaged tissue against diagnostically relevant tissue. We gauge the effectiveness of transfer learning against training from scratch. Best models give 0.99 and 0.89 F1 scores for blood and damaged tissue detection. Since blood and damaged tissue have subtle color differences, we assess the impact of color processing methods on the binary classification performance of five well-known architectures. Finally, we remove the color to understand its importance against morphology on classification performance.acceptedVersio

Invasive cancerous area detection in non-muscle invasive bladder cancer whole slide images

Bladder cancer patients’ stratification into risk groups relies on grade, stage and clinical factors. For non-muscle invasive bladder cancer, T1 tumours that invade the subepithelial tissue are high-risk lesions with a high probability to progress into an aggressive muscle-invasive disease. Detecting invasive cancerous areas is the main factor for dictating the treatment strategy for the patient. However, defining invasion is often subject to intra/interobserver variability among pathologists, thus leading to over or undertreatment. Computer-aided diagnosis systems can help pathologists reduce overheads and erratic reproducibility. We propose a multi-scale model that detects invasive cancerous areas patterns across the whole slide image. The model extracts tiles of different tissue types at multiple magnification levels and processes them to predict invasive patterns based on local and regional information for accurate T1 staging. Our proposed method yields an F1 score of 71.9, in controlled settings 74.9, and without infiltration 90.0.acceptedVersio

Re-examination of the Controversial Coexistence of Traumatic Brain Injury and Posttraumatic Stress Disorder: Misdiagnosis and Self-Report Measures

The coexistence of traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) remains a controversial issue in the literature. To address this controversy, we focused primarily on the civilian-related literature of TBI and PTSD. Some investigators have argued that individuals who had been rendered unconscious or suffered amnesia due to a TBI are unable to develop PTSD because they would be unable to consciously experience the symptoms of fear, helplessness, and horror associated with the development of PTSD. Other investigators have reported that individuals who sustain TBI, regardless of its severity, can develop PTSD even in the context of prolonged unconsciousness. A careful review of the methodologies employed in these studies reveals that investigators who relied on clinical interviews of TBI patients to diagnose PTSD found little or no evidence of PTSD. In contrast, investigators who relied on PTSD questionnaires to diagnose PTSD found considerable evidence of PTSD. Further analysis revealed that many of the TBI patients who were initially diagnosed with PTSD according to self-report questionnaires did not meet the diagnostic criteria for PTSD upon completion of a clinical interview. In particular, patients with severe TBI were often misdiagnosed with PTSD. A number of investigators found that many of the severe TBI patients failed to follow the questionnaire instructions and erroneously endorsed PTSD symptoms because of their cognitive difficulties. Because PTSD questionnaires are not designed to discriminate between PTSD and TBI symptoms or determine whether a patient's responses are accurate or exaggerated, studies that rely on self-report questionnaires to evaluate PTSD in TBI patients are at risk of misdiagnosing PTSD. Further research should evaluate the degree to which misdiagnosis of PTSD occurs in individuals who have sustained mild TBI