Automated extraction of information of lung cancer staging from unstructured reports of PET-CT interpretation: natural language processing with deep-learning

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

As a disease with high morbidity and high mortality, lung cancer has seriously harmed people's health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.

Improving severity classification of Hebrew PET-CT pathology reports using test-time augmentation

Classifying medical reports written in Hebrew is challenging due to the ambiguity and complexity of the language. This study proposes Text Test Time Augmentation (TTTA), a novel method to improve the classification accuracy of cancer severity levels from PET-CT diagnostic reports in Hebrew. Hebrew, being a morphologically rich language, often leads to each word having multiple ambiguous interpretations. TTTA leverages test-time augmentation to enhance text information retrieval and model robustness. During training and testing phases, this method generates and evaluates sets of augmentations to enhance the semantics extracted from each report. Experiments utilize a large institutional report repository from Ziv hospital, Israel, where physicians manually labeled the reports. The results demonstrate that the proposed TTTA approach achieves superior performance over baseline models without TTA, improving PR-AUC by 15.18% on classifying cancer severity levels. The study highlights the efficacy of TTTA in extracting essential medical concepts from free text reports and accurately classifying the severity of cancer. The approach addresses the limitations of prior methods and contributes towards improved automated analysis of Hebrew medical reports. TTTA has the potential to assist physicians in cancer diagnosis and treatment planning.

Automatic Detection of Distant Metastasis Mentions in Radiology Reports in Spanish

PURPOSE

A critical task in oncology is extracting information related to cancer metastasis from electronic health records. Metastasis-related information is crucial for planning treatment, evaluating patient prognoses, and cancer research. However, the unstructured way in which findings of distant metastasis are often written in radiology reports makes it difficult to extract information automatically. The main aim of this study was to extract distant metastasis findings from free-text imaging and nuclear medicine reports to classify the patient status according to the presence or absence of distant metastasis.

MATERIALS AND METHODS

We created a distant metastasis annotated corpus using positron emission tomography-computed tomography and computed tomography reports of patients with prostate, colorectal, and breast cancers. Entities were labeled M1 or M0 according to affirmative or negative metastasis descriptions. We used a named entity recognition model on the basis of a bidirectional long short-term memory model and conditional random fields to identify entities. Mentions were subsequently used to classify whole reports into M1 or M0.

RESULTS

The model detected distant metastasis mentions with a weighted average F1 score performance of 0.84. Whole reports were classified with an F1 score of 0.92 for M0 documents and 0.90 for M1 documents.

CONCLUSION

These results show the usefulness of the model in detecting distant metastasis findings in three different types of cancer and the consequent classification of reports. The relevance of this study is to generate structured distant metastasis information from free-text imaging reports in Spanish. In addition, the manually annotated corpus, annotation guidelines, and code are freely released to the research community.