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 (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) 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 in TILs 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 TNBC

Movie Script Similarity Using Multilayer Network Portrait Divergence

International audienceThis paper addresses the question of movie similarity through multilayer graph similarity measures. Recent work has shown how to construct multilayer networks using movie scripts, and how they capture different aspects of the stories. Based on this modeling, we propose to rely on the multilayer structure and compute different similarities, so we may compare movies, not from their visual content, summary, or actors, but actually from their own storyboard. We propose to do so using "portrait divergence", which has been recently introduced to compute graph distances from summarizing graph characteristics. We illustrate our approach on the series of six Star Wars movies

One year of laboratory-based COVID-19 surveillance system in Belgium: main indicators and performance of the laboratories (March 2020-21).

BACKGROUND: With the spread of coronavirus disease 2019 (COVID-19), an existing national laboratory-based surveillance system was adapted to daily monitor the epidemiological situation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the Belgium by following the number of confirmed SARS-CoV-2 infections, the number of performed tests and the positivity ratio. We present these main indicators of the surveillance over a one-year period as well as the impact of the performance of the laboratories, regarding speed of processing the samples and reporting results, for&nbsp;surveillance. METHODS: We describe the evolution of test capacity, testing strategy and the data collection methods during the first year of the epidemic in&nbsp;Belgium. RESULTS: Between the 1 of March 2020 and the 28 of February 2021, 9,487,470 tests and 773,078 COVID-19 laboratory confirmed cases were reported. Two epidemic waves occurred, with a peak in April and October 2020. The capacity and performance of the laboratories improved continuously during 2020 resulting in a high level performance. Since the end of November 2020 90 to 95% of the test results are reported at the latest the day after sampling was&nbsp;performed. CONCLUSIONS: Thanks to the effort of all laboratories a performant exhaustive national laboratory-based surveillance system to monitor the epidemiological situation of SARS-CoV-2 was set up in Belgium in 2020. On top of expanding the number of laboratories performing diagnostics and significantly increasing the test capacity in Belgium, turnaround times between sampling and testing as well as reporting were optimized over the first year of this&nbsp;pandemic.</p

Histological heterogeneity of CD31-stained blood vessels in glioblastoma multiforme (a-d) and renal cell carcinoma (e-h).

<p>(a-b) Q_A = 15 vessels per mm², A_A = 1.56%, (c-d) Q_A = 77 vessels per mm², A_A = 3.70%, (e-f) Q_A = 183 vessels per mm², A_A = 13.10%, (g-h) Q_A = 81 vessels per mm², A_A = 6.17%. Low (a, b, e, f) heterogeneous samples showed a uniform distribution of vessel profiles as compared to high (c, d, g, h) heterogeneous samples. In glioblastoma multiforme, hotspots and garlands (arrows) were more easily recognized in heterogeneous than in homogeneous samples. Scale bar represents 500 μm (a, c, e, g) or 100 μm (b, d, f, h)</p

Intra- (left) and inter-observer (right) variability for the old counting rules.

<p>This was calculated by the intraclass correlation coefficients (ICC) for the four parameters (V, N, Q_A, A_A). In the first row this is displayed for the number of vessel profiles in a region of interest (N). In the second row this is displayed for the microvessel density (Q_A). In the third row this is displayed for the number of points in the grid hitting a vessel profile in a region of interest (V). In the last row this is displayed for the areal fraction of vessel profiles (A_A). In addition are the ICCs in relation to the heterogeneity level (low or high) and the cancer type (colorectal carcinoma (CRC), glioblastoma multiforme (GBM), ovarian carcinoma (OC), and renal cell carcinoma (RCC)) shown.</p

Distribution of 1000 bootstrap results.

<p>Here the results for CRC sample 19 is displayed. These were calculated based on the counting by the second observer during the second round of counting. Tukey boxplots were constructed for amounts of regions of interest evaluated. Ten regions are sufficient for accurate microvessel density calculation.</p

Inter-observer variability for the old counting rules.

<p>This was calculated by the intraclass correlation coefficients (ICC) between the averaged counting of observer 1 (KM) and 2 (VC).</p

Inter-observer variability for the new counting rules.

<p>This was calculated by the intraclass correlation coefficients (ICC) between the counting of observer 1 (KM), 4 (PV), 5 (YW) and 6 (WW).</p

Inter-observer variation for the old counting rules between observer 1 (KM) and 2 (VC) for colorectal cancer samples.

<p>This was displayed by Bland-Altman (a, c, e, g) and prediction plots with prediction intervals (two black lines) (b, d, f, h) for the number of vessel profiles (N) (a, b), the microvessel density (Q_A) (c,d), the number of points in the grid hitting a vessel profile (V) (e, f) and the areal fraction of vessel profiles (A_A) (g, h). A systemic bias for N, Q_A, and A_A was present as illustrated by the prediction plots (large distance between the x = y line (black and dashed) and the linear regression line of the measurements (red)).</p

Example of a region of interest.

<p>It was captured in Pannoramic Viewer (3DHISTECH, Budapest, Hungary) and combined with a digital 81-points grid in Adobe Photoshop CS4. CD31-stained vessel profiles in the grid were counted as N (green arrow). Vessel profiles that cross the virtually extended left or lower line of the grid were not counted (shaded green arrow). The grid points that hit a CD31-stained vascular profile were counted as V (red arrow). Scale bar represents 100 μm.</p