Unsupervised domain adaptation in brain lesion segmentation with adversarial networks

Significant advances have been made towards building accu- rate automatic segmentation systems for a variety of biomedical applica- tions using machine learning. However, the performance of these systems often degrades when they are applied on new data that differ from the training data, for example, due to variations in imaging protocols. Man- ually annotating new data for each test domain is not a feasible solution. In this work we investigate unsupervised domain adaptation using ad- versarial neural networks to train a segmentation method which is more invariant to differences in the input data, and which does not require any annotations on the test domain. Specifically, we learn domain-invariant features by learning to counter an adversarial network, which attempts to classify the domain of the input data by observing the activations of the segmentation network. Furthermore, we propose a multi-connected domain discriminator for improved adversarial training. Our system is evaluated using two MR databases of subjects with traumatic brain in- juries, acquired using different scanners and imaging protocols. Using our unsupervised approach, we obtain segmentation accuracies which are close to the upper bound of supervised domain adaptation

The process of recovery of people with mental illness: The perspectives of patients, family members and care providers: Part 1

<p>Abstract</p> <p>Background</p> <p>It is a qualitative design study that examines points of divergence and convergence in the perspectives on recovery of 36 participants or 12 triads. Each triad comprising a patient, a family member/friend, a care provider and documents the procedural, analytic of triangulating perspectives as a means of understanding the recovery process which is illustrated by four case studies. Variations are considered as they relate to individual characteristics, type of participant (patient, family, member/friend and care provider), and mental illness. This paper which is part of a larger study and is based on a qualitative research design documents the process of recovery of people with mental illness: Developing a Model of Recovery in Mental Health: A middle range theory.</p> <p><b>Methods</b></p> <p>Data were collected in field notes through semi-structured interviews based on three interview guides (one for patients, one for family members/friends, and one for caregivers). Cross analysis and triangulation methods were used to analyse the areas of convergence and divergence on the recovery process of all triads.</p> <p>Results</p> <p>In general, with the 36 participants united in 12 triads, two themes emerge from the cross-analysis process or triangulation of data sources (12 triads analysis in 12 cases studies). Two themes emerge from the analysis process of the content of 36 interviews with participants: (1) <it>Revealing dynamic context</it>, situating patients in their dynamic context; and (2) <it>Relationship issues in a recovery process</it>, furthering our understanding of such issues. We provide four case studies examples (among 12 cases studies) to illustrate the variations in the way recovery is perceived, interpreted and expressed in relation to the different contexts of interaction.</p> <p>Conclusion</p> <p>The perspectives of the three participants (patients, family members/friends and care providers) suggest that recovery depends on constructing meaning around mental illness experiences and that the process is based on each person's dynamic context (e.g., social network, relationship), life experiences and other social determinants (e.g., symptoms, environment). The findings of this study add to existing knowledge about the determinants of the recovery of persons suffering with a mental illness and significant other utilizing public mental health services in Montreal, Canada.</p

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials.

Funder: Breast Cancer Research Foundation (BCRF); doi: https://doi.org/10.13039/100001006Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting