Discordance, accuracy and reproducibility study of pathologists' diagnosis of melanoma and melanocytic tumors.

Accurate melanoma diagnosis is crucial for patient outcomes and reliability of AI diagnostic tools. We assess interrater variability among eight expert pathologists reviewing histopathological images and clinical metadata of 792 melanoma-suspicious lesions prospectively collected at eight German hospitals. Moreover, we provide access to the largest panel-validated dataset featuring dermoscopic and histopathological images with metadata.

Large language model use in clinical oncology.

Large language models (LLMs) are undergoing intensive research for various healthcare domains. This systematic review and meta-analysis assesses current applications, methodologies, and the performance of LLMs in clinical oncology. A mixed-methods approach was used to extract, summarize, and compare methodological approaches and outcomes.

Noninvasive Technologies for the Diagnosis of Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.

Early cutaneous squamous cell carcinoma (cSCC) diagnosis is essential to initiate adequate targeted treatment. Noninvasive diagnostic technologies could overcome the need of multiple biopsies and reduce tumor recurrence. To assess performance of noninvasive technologies for cSCC diagnostics, 947 relevant records were identified through a systematic literature search.

Evaluation of a medical student-delivered smoking prevention program utilizing a face-aging mobile app for secondary schools in Germany: The Education Against Tobacco cluster-randomized controlled trial.

Background: To reduce smoking uptake in adolescents, the medical students' network Education Against Tobacco (EAT) has developed a school-based intervention involving a face-aging mobile app (Smokerface).

Methods: A two-arm cluster-randomized controlled trial was conducted, evaluating the 2016 EAT intervention, which employed the mobile app Smokerface and which was delivered by medical students. Schools were randomized to intervention or control group.

Deep learning for dual detection of microsatellite instability and POLE mutations in colorectal cancer histopathology.

In the spectrum of colorectal tumors, microsatellite-stable (MSS) tumors with DNA polymerase ε (POLE) mutations exhibit a hypermutated profile, holding the potential to respond to immunotherapy similarly to their microsatellite-instable (MSI) counterparts. Yet, due to their rarity and the associated testing costs, systematic screening for these mutations is not commonly pursued. Notably, the histopathological phenotype resulting from POLE mutations is theorized to resemble that of MSI.

Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide images.

The variation in histologic staining between different medical centers is one of the most profound challenges in the field of computer-aided diagnosis. The appearance disparity of pathological whole slide images causes algorithms to become less reliable, which in turn impedes the wide-spread applicability of downstream tasks like cancer diagnosis. Furthermore, different stainings lead to biases in the training which in case of domain shifts negatively affect the test performance.

3-Dimensional Reconstruction From Histopathological Sections: A Systematic Review.

Although pathological tissue analysis is typically performed on single 2-dimensional (2D) histologic reference slides, 3-dimensional (3D) reconstruction from a sequence of histologic sections could provide novel opportunities for spatial analysis of the extracted tissue. In this review, we analyze recent works published after 2018 and report information on the extracted tissue types, the section thickness, and the number of sections used for reconstruction. By analyzing the technological requirements for 3D reconstruction, we observe that software tools exist, both free and commercial, which include the functionality to perform 3D reconstruction from a sequence of histologic images.

Clinical criteria for filum terminale resection in occult tethered cord syndrome.

Objective: Tethered cord syndrome (TCS) comprises three symptom categories: back/leg pain, bowel/bladder, and neurological complaints. MRI typically reveals a low-lying conus medullaris, filum terminale (FT) pathology, or lumbosacral abnormalities. FT resection is established in TCS but not in radiologically occult TCS (OTCS).

Automated curation of large-scale cancer histopathology image datasets using deep learning.

Background: Artificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In reality, especially for large surgical resection specimens, dozens of slides can be available for each patient.

Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics.

Importance: The development of artificial intelligence (AI)-based melanoma classifiers typically calls for large, centralized datasets, requiring hospitals to give away their patient data, which raises serious privacy concerns. To address this concern, decentralized federated learning has been proposed, where classifier development is distributed across hospitals.

Objective: To investigate whether a more privacy-preserving federated learning approach can achieve comparable diagnostic performance to a classical centralized (ie, single-model) and ensemble learning approach for AI-based melanoma diagnostics.

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.

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma.

Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet often lack precise, domain-specific explanations. Moreover, the impact of XAI methods on dermatologists' decisions has not yet been evaluated.

End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study.

Background: Precise prognosis prediction in patients with colorectal cancer (ie, forecasting survival) is pivotal for individualised treatment and care. Histopathological tissue slides of colorectal cancer specimens contain rich prognostically relevant information. However, existing studies do not have multicentre external validation with real-world sample processing protocols, and algorithms are not yet widely used in clinical routine.

Deep learning to predict breast cancer sentinel lymph node status on INSEMA histological images.

Background: Sentinel lymph node (SLN) status is a clinically important prognostic biomarker in breast cancer and is used to guide therapy, especially for hormone receptor-positive, HER2-negative cases. However, invasive lymph node staging is increasingly omitted before therapy, and studies such as the randomised Intergroup Sentinel Mamma (INSEMA) trial address the potential for further de-escalation of axillary surgery. Therefore, it would be helpful to accurately predict the pretherapeutic sentinel status using medical images.

Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning.

Studies have shown that colorectal cancer prognosis can be predicted by deep learning-based analysis of histological tissue sections of the primary tumor. So far, this has been achieved using a binary prediction. Survival curves might contain more detailed information and thus enable a more fine-grained risk prediction.

A 3-dimensional histology computer model of malignant melanoma and its implications for digital pathology.

Background: Historically, cancer diagnoses have been made by pathologists using two-dimensional histological slides. However, with the advent of digital pathology and artificial intelligence, slides are being digitised, providing new opportunities to integrate their information. Since nature is 3-dimensional (3D), it seems intuitive to digitally reassemble the 3D structure for diagnosis.

Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise.

In the past years, deep learning has seen an increase in usage in the domain of histopathological applications. However, while these approaches have shown great potential, in high-risk environments deep learning models need to be able to judge their uncertainty and be able to reject inputs when there is a significant chance of misclassification. In this work, we conduct a rigorous evaluation of the most commonly used uncertainty and robustness methods for the classification of Whole Slide Images, with a focus on the task of selective classification, where the model should reject the classification in situations in which it is uncertain.

A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma.

Purpose: To develop and validate an interpretable deep learning model to predict overall and disease-specific survival (OS/DSS) in clear cell renal cell carcinoma (ccRCC).

Methods: Digitised haematoxylin and eosin-stained slides from The Cancer Genome Atlas were used as a training set for a vision transformer (ViT) to extract image features with a self-supervised model called DINO (self-distillation with no labels). Extracted features were used in Cox regression models to prognosticate OS and DSS.