Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 2: Pathology Whole Slide Image De-identification, De-facing, the Role of AI in Image De-identification, and the NCI MIDI Datasets and Pipeline.

De-identification of medical images intended for research is a core requirement for data sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and Information Technology (CBIIT) of the United States National Cancer Institute (NCI) convened a two half-day virtual workshop with the intent of summarizing the state of the art in de-identification technology and processes and exploring interesting aspects of the subject. This paper summarizes the highlights of the second day of the workshop, the recordings and presentations of which are publicly available for review. The topics covered included pathology whole slide image de-identification, de-facing, the role of AI in image de-identification, and the NCI Medical Image De-Identification Initiative (MIDI) datasets and pipeline.

Evolutionary Strategies AI Addresses Multiple Technical Challenges in Deep Learning Deployment: Proof-of-Principle Demonstration for Neuroblastoma Brain Metastasis Detection.

Two significant obstacles hinder the advancement of Radiology AI. The first is the challenge of overfitting, where small training data sets can result in unreliable outcomes. The second challenge is the need for more generalizability, the lack of which creates difficulties in implementing the technology across various institutions and practices. A recent innovation, deep neuroevolution (DNE), has been introduced to tackle the overfitting issue by training on small data sets and producing accurate predictions. However, the generalizability of DNE has yet to be proven. This paper strives to overcome this barrier by demonstrating that DNE can achieve satisfactory results in diverse external validation sets. The main innovation of the work is thus showing that DNE can generalize to varied outside data. Our example use case is predicting brain metastasis from neuroblastoma, emphasizing the importance of AI with limited data sets. Despite image collection and labeling advancements, rare diseases will always constrain data availability. We optimized a convolutional neural network (CNN) with DNE to demonstrate generalizability. We trained the CNN with 60 MRI images and tested it on a separate diverse collection of images from over 50 institutions. For comparison, we also trained with the more traditional stochastic gradient descent (SGD) method, with the two variants of (1) training from scratch and (2) transfer learning. Our results show that DNE demonstrates excellent generalizability with 97% accuracy on the heterogeneous testing set, while neither form of SGD could reach 60% accuracy. DNE's ability to generalize from small training sets to external and diverse testing sets suggests that it or similar approaches may play an integral role in improving the clinical performance of AI.

Lung Cancer Survival Trends in the Veterans Health Administration.

INTRODUCTION: Lung cancer survival is improving in the United States. We investigated whether there was a similar trend within the Veterans Health Administration (VHA), the largest integrated healthcare system in the United States. MATERIALS AND METHODS: Data from the Veterans Affairs Central Cancer Registry were analyzed for temporal survival trends using Kaplan-Meier estimates and linear regression. RESULTS: A total number of 54,922 Veterans were identified with lung cancer diagnosed from 2010 to 2017. Histologies were classified as non-small-cell lung cancer (NSCLC) (64.2%), small cell lung cancer (SCLC) (12.9%), and 'other' (22.9%). The proportion with stage I increased from 18.1% to 30.4%, while stage IV decreased from 38.9% to 34.6% (both P < .001). The 3-year overall survival (OS) improved for stage I (58.6% to 68.4%, P < .001), stage II (35.5% to 48.4%, P < .001), stage III (18.7% to 29.4%, P < .001), and stage IV (3.4% to 7.8%, P < .001). For NSCLC, the median OS increased from 12 to 21 months (P < .001), and the 3-year OS increased from 24.1% to 38.3% (P < .001). For SCLC, the median OS remained unchanged (8 to 9 months, P = .10), while the 3-year OS increased from 9.1% to 12.3% (P = .014). Compared to White Veterans, Black Veterans with NSCLC had similar OS (P = .81), and those with SCLC had higher OS (P = .003). CONCLUSION: Lung cancer survival is improving within the VHA. Compared to White Veterans, Black Veterans had similar or higher survival rates. The observed racial equity in outcomes within a geographically and socioeconomically diverse population warrants further investigation to better understand and replicate this achievement in other healthcare systems.

Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles.

Machine learning (ML) has increasingly been used to assist and expand current practices in neuropathology. However, generating large imaging datasets with quality labels is challenging in fields which demand high levels of expertise. Further complicating matters is the often seen disagreement between experts in neuropathology-related tasks, both at the case level and at a more granular level. Neurofibrillary tangles (NFTs) are a hallmark pathological feature of Alzheimer disease, and are associated with disease progression which warrants further investigation and granular quantification at a scale not currently accessible in routine human assessment. In this work, we first provide a baseline of annotator/rater agreement for the tasks of Braak NFT staging between experts and NFT detection using both experts and novices in neuropathology. We use a whole-slide-image (WSI) cohort of neuropathology cases from Emory University Hospital immunohistochemically stained for Tau. We develop a workflow for gathering annotations of the early stage formation of NFTs (Pre-NFTs) and mature intracellular (iNFTs) and show ML models can be trained to learn annotator nuances for the task of NFT detection in WSIs. We utilize a model-assisted-labeling approach and demonstrate ML models can be used to aid in labeling large datasets efficiently. We also show these models can be used to extract case-level features, which predict Braak NFT stages comparable to expert human raters, and do so at scale. This study provides a generalizable workflow for various pathology and related fields, and also provides a technique for accomplishing a high-level neuropathology task with limited human annotations.