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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Global Explainability of A Deep Abstaining Classifier for Cancer Pathology Reports

We present a global explainability method to characterize sources of errors in a real-world multitask deep abstaining classifier (DAC), in the context of cancer histology prediction. Our multitask classifier, currently deployed for automated annotation of cancer pathology reports from NCI-SEER registries, was trained and evaluated on 1.04 million hand-annotated samples and makes simultaneous predictions of cancer site, subsite, histology, laterality, and behavior for each report. The DAC framework enables the model to abstain on ambiguous reports and confusing classes to achieve the target accuracy on the retained (non-abstained) samples, but at the cost of decreased coverage. Requiring 97% accuracy on the histology task caused our model to retain only 22% of all samples, mostly the less ambiguous and common classes. Local explainability with the GradInp technique provided a computationally efficient way of obtaining contextual reasoning for hundreds of thousands of individual predictions. Our method, involving dimensionality reduction of approximately 13000 aggregated local explanations (ALE), offers a tractable path to true global explainability. It enabled identification of sources of errors in histology classification, globally, as hierarchical complexity among classes, label noise, insufficient information, and conflicting evidence. This suggests several strategies for iterative improvement of our DAC, including well-designed exclusion criteria, focused annotation, and reduced penalties for errors involving hierarchically related classes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Filter pruning of Convolutional Neural Networks for text classification: A case study of cancer pathology report comprehension

Convolutional Neural Networks (CNN) have recently demonstrated effective performance in many Natural Language Processing tasks. In this study, we explore a novel approach for pruning a CNN's convolution filters using our new data-driven utility score. We have applied this technique to an information extraction task of classifying a dataset of cancer pathology reports by cancer type, a highly imbalanced dataset. Compared to standard CNN training, our new algorithm resulted in a nearly .07 increase in the micro-averaged F1-score and a strong .22 increase in the macro-averaged F1-score using a model with nearly a third fewer network weights. We show how directly utilizing a network's interpretation of data can result in strong performance gains, particularly with severely imbalanced datasets.

Yoon, Hong-Jun↗
DOE OSTI2019

Model-based Hyperparameter Optimization of Convolutional Neural Networks for Information Extraction from Cancer Pathology Reports on HPC

Finding optimal hyperparameters is necessary to identify the best performing deep learning models but the process is costly. In this paper, we applied model-based optimization, also known as Bayesian optimization, using the CANDLE framework implemented on a High-Performance Computing environment. As a use case we selected information extraction from cancer pathology reports using a multi-task convolutional neural network, and hierarchical convolutional attention network to be optimized. We utilized a synthesized text corpus of 8,000 training cases and 2,000 validation cases with four types of clinical task labels including primary cancer site, laterality, behavior, and histological grade. We demonstrated that hyperparameter optimization using the CANDLE framework is a feasible approach with respect to both scalability and clinical task performance.

Yoon, Hong-Jun↗
DOE OSTI2019

Information Extraction from Cancer Pathology Reports with Graph Convolution Networks for Natural Language Texts

Graph-of-words is a flexible and efficient text representation which addresses well-known challenges, such as word ordering and variation of expressions, to natural language processing. In this paper, we consider the latest graph-based convolutional neural network technique, the Text Graph Convolutional Network (Text GCN), in the context of performing classification tasks on free-form natural language texts. To do this, we designed a study of multi-task information extraction from medical text documents. We implemented multi-task learning in the Text GCN, performed hyperparameter optimization, and measured the clinical task performances. We evaluated micro and macro-F1 scores of four information extraction tasks, including subsite, laterality, behavior, and histological grades from cancer pathology reports. The scores for the Text GCN significantly outperformed our previous studies with convolutional neural networks, suggesting that the Text GCN model is superior to traditional models in task performance.

Yoon, Hong-Jun↗
DOE OSTI2013

Tumor Size on Abdominal MRI Versus Pathologic Specimen in Resected Pancreatic Adenocarcinoma: Implications for Radiation Treatment Planning

Purpose: We assessed the accuracy of abdominal magnetic resonance imaging (MRI) for determining tumor size by comparing the preoperative contrast-enhanced T1-weighted gradient echo (3-dimensional [3D] volumetric interpolated breath-hold [VIBE]) MRI tumor size with pathologic specimen size. Methods and Materials: The records of 92 patients who had both preoperative contrast-enhanced 3D VIBE MRI images and detailed pathologic specimen measurements were available for review. Primary tumor size from the MRI was independently measured by a single diagnostic radiologist (P.M.) who was blinded to the pathology reports. Pathologic tumor measurements from gross specimens were obtained from the pathology reports. The maximum dimensions of tumor measured in any plane on the MRI and the gross specimen were compared. The median difference between the pathology sample and the MRI measurements was calculated. A paired t test was conducted to test for differences between the MRI and pathology measurements. The Pearson correlation coefficient was used to measure the association of disparity between the MRI and pathology sizes with the pathology size. Disparities relative to pathology size were also examined and tested for significance using a 1-sample t test. Results: The median patient age was 64.5 years. The primary site was pancreatic head in 81 patients, body in 4, and tail in 7. Three patients were American Joint Commission on Cancer stage IA, 7 stage IB, 21 stage IIA, 58 stage IIB, and 3 stage III. The 3D VIBE MRI underestimated tumor size by a median difference of 4 mm (range, −34-22 mm). The median largest tumor dimensions on MRI and pathology specimen were 2.65 cm (range, 1.5-9.5 cm) and 3.2 cm (range, 1.3-10 cm), respectively. Conclusions: Contrast-enhanced 3D VIBE MRI underestimates tumor size by 4 mm when compared with pathologic specimen. Advanced abdominal MRI sequences warrant further investigation for radiation therapy planning in pancreatic adenocarcinoma before routine integration into the treatment planning process.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

FrESCO: Framework for Exploring Scalable Computational Oncology

The National Cancer Institute (NCI) monitors population level cancer trends as part of its Surveillance, Epidemiology, and End Results (SEER) program. This program consists of state or regional level cancer registries which collect, analyze, and annotate cancer pathology reports. From these annotated pathology reports, each individual registry aggregates cancer phenotype information from electronic health records. This data is then used to create summary statistics about cancer incidence and mortality to facilitate population health monitoring. Extracting phenotypic information from these reports is a labor intensive task, requiring specialized knowledge about the reports and cancer. Automating the information extraction process from cancer pathology reports has the potential to improve data quality by extracting information in a consistent manner across registries. It can also improve patient outcomes by reducing the time from diagnosis, enabling rapid case ascertainment for clinical trials. Here we present FrESCO, a modular deep-learning natural language processing (NLP) library initially designed for extracting pathology information from clinical text documents. This repository is not solely limited to clinical medical text, but may also be used by researchers just getting started with NLP methods and those looking for a robust solution for their classification problems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

FrESCO

The National Cancer Institute (NCI) monitors population level cancer trends as part of its Surveillance, Epidemiology, and End Results (SEER) program. This program consists of state or regional level cancer registries which collect, analyze, and annotate cancer pathology reports. From these annotated pathology reports, each individual registry aggregates cancer phenotype information and summary statistics about cancer prevalence to facilitate population level monitoring of cancer incidence. Extracting cancer phenotype from these reports is a labor intensive task, requiring specialized knowledge about the reports and cancer. Automating this information extraction process from cancer pathology reports has the potential to improve not only the quality of the data by extracting information in a consistent manner across registries, but to improve the quality of patient outcomes by reducing the time to assimilate new data and enabling time-sensitive applications such as precision medicine. Here we present FrESCO: Framework for Exploring Scalable Computational Oncology, a modular deep-learning natural language processing (NLP) library for extracting pathology information from clinical text documents.

Spannaus, Adam [Oak Ridge National Lab. (ORNL), Oa↗
DOE OSTI2018

Retrofitting Word Embeddings with the UMLS Metathesaurus for Clinical Information Extraction

Deep learning has surged in popularity and proven to be effective for various artificial intelligence applications including information extraction from cancer pathology reports. Since word representation is a core unit that enables deep learning algorithms to understand words and be able to perform NLP, this representation must include as much information as possible to help these algorithms achieve high classification performance. Therefore, in this work in addition to the distributional information of words in large sized corpora, we use UMLS vocabulary resources to enrich the vector space representation of words with the semantic relations between words. These resources provide many terminologies pertaining to cancer. The refined word embeddings are used with a convolutional neural (CNN) model to extract four data elements from cancer pathology reports; ICD-O-3 tumor topography codes, tumor laterality, behavior, and histological grade. We observed that using UMLS vocabulary resources to enrich word embeddings of CNN models consistently outperformed CNN models without pre-training word embeddings and even with pre-trained word embeddings on a domain specific corpus across all four tasks. The results show marginal improvement on the laterality task, but a significant improvement on the other tasks, especially for the macro-f score. Specifically, the improvements are 3%, 13%, and 15% for tumor site, histological grade, and behavior tasks, respectively. This approach is encouraging to enrich word embeddings with more clinical data resources to be used for information abstraction tasks from clinical pathology reports.

Alawad, Mohammed↗
DOE OSTI2017

PathBot: A Radiology-Pathology Correlation Dashboard

Pathology is considered the “gold standard” of diagnostic medicine. The importance of radiology-pathology correlation is seen in interdepartmental patient conferences such as “tumor boards” and by the tradition of radiology resident immersion in a radiologic-pathology course at the American Institute of Radiologic Pathology. In practice, consistent pathology follow-up can be difficult due to time constraints and cumbersome electronic medical records. We present a radiology-pathology correlation dashboard that presents radiologists with pathology reports matched to their dictations, for both diagnostic imaging and image-guided procedures. In creating our dashboard, we utilized the RadLex ontology and National Center for Biomedical Ontology (NCBO) Annotator to identify anatomic concepts in pathology reports that could subsequently be mapped to relevant radiology reports, providing an automated method to match related radiology and pathology reports. Radiology-pathology matches are presented to the radiologist on a web-based dashboard. We found that our algorithm was highly specific in detecting matches. Our sensitivity was slightly lower than expected and could be attributed to missing anatomy concepts in the RadLex ontology, as well as limitations in our parent term hierarchical mapping and synonym recognition algorithms. By automating radiology-pathology correlation and presenting matches in a user-friendly dashboard format, we hope to encourage pathology follow-up in clinical radiology practice for purposes of self-education and to augment peer review. We also hope to provide a tool to facilitate the production of quality teaching files, lectures, and publications. Diagnostic images have a richer educational value when they are backed up by the gold standard of pathology.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Multi-Task Convolutional Neural Networks for Natural Text Classification

Information extraction and coding of free-text pathology reports is an important activity for cancer registries to support national cancer surveillance. Cancer registrars must process high volumes of pathology reports on an annual basis. We investigated an automated approach using a convolutional neural networks (CNNs) for extracting the primary site, laterality, histology, behavior, and histological grade from unstructured cancer pathology text reports. Our multi-task learning (MTL) with hard parameter sharing approach is used to train a multi-task MT-CNN model for all the tasks. The performance of our proposed approach was compared against a state of-the-art CNN and the commonly used SVM classifier. We observed that the proposed model consistently outperformed the baseline models, especially for the less prevalent classes. Also, the results demonstrate the potential of the proposed method for handling class imbalance within each task.

Yoon, HongJun↗
DOE OSTI2023

Deep learning uncertainty quantification for clinical text classification

Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network’s confidence, in-depth analyses are needed to establish whether they are well calibrated. In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) population-based cancer registries. In particular, we introduce multiple methods for selective classification to achieve a target level of accuracy on multiple classification tasks while minimizing the rejection amount—that is, the number of electronic pathology reports for which the model’s predictions are unreliable. We evaluate the proposed methods by comparing our approach with the current in-house deep learning-based abstaining classifier. Overall, all the proposed selective classification methods effectively allow for achieving the targeted level of accuracy or higher in a trade-off analysis aimed to minimize the rejection rate. On in-distribution validation and holdout test data, with all the proposed methods, we achieve on all tasks the required target level of accuracy with a lower rejection rate than the deep abstaining classifier (DAC). Interpreting the results for the out-of-distribution test data is more complex; nevertheless, in this case as well, the rejection rate from the best among the proposed methods achieving 97% accuracy or higher is lower than the rejection rate based on the DAC. We show that although both approaches can flag those samples that should be manually reviewed and labeled by human annotators, the newly proposed methods retain a larger fraction and do so without retraining—thus offering a reduced computational cost compared with the in-house deep learning-based abstaining classifier.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Machine learning and deep learning tools for the automated capture of cancer surveillance data

The National Cancer Institute and the Department of Energy strategic partnership applies advanced computing and predictive machine learning and deep learning models to automate the capture of information from unstructured clinical text for inclusion in cancer registries. Applications include extraction of key data elements from pathology reports, determination of whether a pathology or radiology report is related to cancer, extraction of relevant biomarker information, and identification of recurrence. With the growing complexity of cancer diagnosis and treatment, capturing essential information with purely manual methods is increasingly difficult. These new methods for applying advanced computational capabilities to automate data extraction represent an opportunity to close critical information gaps and create a nimble, flexible platform on which new information sources, such as genomics, can be added. This will ultimately provide a deeper understanding of the drivers of cancer and outcomes in the population and increase the timeliness of reporting. These advances will enable better understanding of how real-world patients are treated and the outcomes associated with those treatments in the context of our complex medical and social environment.

60 APPLIED LIFE SCIENCES↗