Evaluating Deep Learning-based Melanoma Classification using Immunohistochemistry and Routine Histology: A Three Center Study

Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. The use of diagnostic Deep Learning (DL)-based support systems for automated examination of tissue morphology and cellular composition has been well studied in standard H&E-stained tissue slides. In contrast, there are few studies that analyze IHC slides using DL. Therefore, we investigated the separate and joint performance of ResNets trained on MelanA and corresponding H&E-stained slides. The MelanA classifier achieved an area under receiver operating characteristics curve (AUROC) of 0.82 and 0.74 on out of distribution (OOD)-datasets, similar to the H&E-based benchmark classification of 0.81 and 0.75, respectively. A combined classifier using MelanA and H&E achieved AUROCs of 0.85 and 0.81 on the OOD datasets. DL MelanA-based assistance systems show the same performance as the benchmark H&E classification and may be improved by multi stain classification to assist pathologists in their clinical routine

Examination of Subungual Hematomas and Subungual Melanocytic Lesions by Using Optical Coherence Tomography and Dermoscopy

Introduction: Examination of subungual pigmented lesions is sometimes a diagnostic challenge for clinicians. Objectives: The study was aimed to investigate characteristic patterns in optical coherence tomography (OCT) of subungual hematomas and determine distinctive features that can differentiate them from subungual melanocytic lesions. Methods: VivoSight® (Michelson Diagnostics, Maidstone, UK) was used to examine 71 subungual hematomas and 11 subungual melanocytic lesions in 69 patients (18 female and 51 male patients). Results: On OCT, bleeding was related to sharply defined black sickle-shaped (p < 0.001) or globular regions (not significant [ns]) with a hyperreflective margin (0.002), a grey center (0.013), hyperreflective lines in the area (ns) or periphery (p = 0.031), peripheral fading (p = 0.029), and red dots in the area (p = 0.001). In the 1 case of melanoma in situ examined, we found curved vessels with irregular sizes and distribution on the dermis of the nailbed, while subungual hematomas and subungual benign nevi presented as clustered red dots and/or regularly distributed curved vessels. Conclusion: Our findings indicate that the use of OCT in addition to dermoscopy provides high-resolution optical imaging information for the diagnosis of subungual hematoma and facilitates the differential diagnosis of subungual hematomas and subungual melanocytic lesions

Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study.

Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. The use of diagnostic Deep Learning (DL)-based support systems for automated examination of tissue morphology and cellular composition has been well studied in standard H&E-stained tissue slides. In contrast, there are few studies that analyze IHC slides using DL. Therefore, we investigated the separate and joint performance of ResNets trained on MelanA and corresponding H&E-stained slides. The MelanA classifier achieved an area under receiver operating characteristics curve (AUROC) of 0.82 and 0.74 on out of distribution (OOD)-datasets, similar to the H&E-based benchmark classification of 0.81 and 0.75, respectively. A combined classifier using MelanA and H&E achieved AUROCs of 0.85 and 0.81 on the OOD datasets. DL MelanA-based assistance systems show the same performance as the benchmark H&E classification and may be improved by multi stain classification to assist pathologists in their clinical routine

Obstructive sleep apnoea is associated with the development of diastolic dysfunction after myocardial infarction with preserved ejection fraction

Background Left ventricular diastolic dysfunction is a predictor of adverse outcome after acute myocardial infarction (AMI). We aimed to test if sleep-disordered breathing (SDB) contributes to the development of diastolic dysfunction in patients with preserved left ventricular ejection fraction after AMI. Method Patients with AMI, percutaneous coronary intervention and an ejection fraction ≥50% were included in this sub-analysis of a prospective observational study. Patients with AMI (n = 41) underwent cardiovascular magnetic resonance imaging (volume–time curve analysis) to define diastolic function by means of the normalised peak filling rate [nPFR; (end diastolic volume/second)]. In patients with AMI, the nPFR was assessed within <5 days and three months after AMI. Patients with AMI were stratified in patients with (apnoea-hypopnoea index, AHI ≥15/h) and without (AHI <15/h) SDB as assessed by polysomnography. Results At the time of AMI, the nPFR was similar between patients with and without SDB (2.90 ± 0.54 vs. 3.03 ± 1.20, p = 0.662). Within three months after AMI, diastolic function was significantly lower in patients with SDB than in patients without SDB (ΔnPFR: −0.83 ± 0.14 vs. 0.03 ± 0.14; p < 0.001; ANCOVA, adjusted for baseline nPFR). In contrast to central AHI, obstructive AHI was associated with a lower nPFR three months after AMI, after accounting for established risk factors for diastolic dysfunction [multiple linear regression analysis, B (95%CI): −0.036 (−0.063 to −0.009), p = 0.011]. Conclusion Our data indicate that obstructive sleep apnoea impairs diastolic function early after myocardial infarction

Description of the population in our datasets.

For continuous features we report median, range, and number of NAs, for categorical features we report the total number of observations per group. Here the training population as well as all three test populations are described. Melanoma in situ describes the early stage of a malignant melanoma that has not yet broken through the basement membrane. However, features at the cellular level do not differ between melanoma in situ and malignant melanoma.</p

ROC plot of the hierarchical compared to the combined approach with corresponding AUROC values by data source site.

A: Results from Dresden B: Results from Erlangen C: Results from Naples. Black: Results of the combined approach using H&E and MElanA for all lesions Red: Hierarchical approach using MelanA-stained tissue only for H&E-based uncertain lesions. (TIF)</p

Representative thumbnails for melanoma, melanoma in-situ, and nevus for all three cohorts.

Representative thumbnails for melanoma, melanoma in-situ, and nevus for all three cohorts.</p

S4 Dataset -

Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. The use of diagnostic Deep Learning (DL)-based support systems for automated examination of tissue morphology and cellular composition has been well studied in standard H&E-stained tissue slides. In contrast, there are few studies that analyze IHC slides using DL. Therefore, we investigated the separate and joint performance of ResNets trained on MelanA and corresponding H&E-stained slides. The MelanA classifier achieved an area under receiver operating characteristics curve (AUROC) of 0.82 and 0.74 on out of distribution (OOD)-datasets, similar to the H&E-based benchmark classification of 0.81 and 0.75, respectively. A combined classifier using MelanA and H&E achieved AUROCs of 0.85 and 0.81 on the OOD datasets. DL MelanA-based assistance systems show the same performance as the benchmark H&E classification and may be improved by multi stain classification to assist pathologists in their clinical routine.</div

Schematic diagram of the different models.

The red box shows the pipeline for MelanA-stained WSIs and the purple box the pipeline for H&E-stained WSIs. We tessellated MelanA-stained WSIs corresponding to different magnifications and trained individual models on each tile size. The class probabilities for each tile were predicted and aggregated into a slide score by averaging all tile scores. For the H&E-based model we proceeded in the same way.</p

ROC plots by data source site with corresponding AUROC values.

A: Results from Dresden B: Results from Erlangen C: Results from Naples. Red: 40x magnification Blue: 20x magnification Purple: 10x magnification Gray: 5x magnification. (TIF)</p