Natural language processing to predict isocitrate dehydrogenase genotype in diffuse glioma using MR radiology reports

Kim, Minjae; Ong, Kim-Thanh; Choi, Seonah; Yeo, Jinyoung; Kim, Sooyon; Han, Kyunghwa; Park, Ji Eun; Kim, Ho Sung; Choi, Yoon Seong; Ahn, Sung Soo; Kim, Jinna; Lee, Seung Koo; Sohn, Beomseok

doi:10.1007/s00330-023-10061-z

Cited by 8 publications

(1 citation statement)

References 32 publications

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“…However, AD diagnosis requires understanding and reasoning over a range of clinical data, such as APOE4 allele and Clinical NLP. The success of LMs has sparked a surge in applying NLP techniques to the biomedical field (Lee et al 2020;Yue, Jimenez Gutierrez, and Sun 2020;Rajagopal et al 2021;Kim et al 2023;Feng et al 2023). For example, Lee et al (2020) fine-tune the commonly used BERT model (Kenton and Toutanova 2019) with medical corpus to endow it with biomedical knowledge, which is then implemented by Yue, Jimenez Gutierrez, and Sun (2020) to solve the clinical reading comprehension task; Rajagopal et al (2021) and Feng et al (2023) address the generation of explanations for various medical conditions via sequence-tosequence language models with template-based approaches.…”

Section: Related Workmentioning

confidence: 99%

Large Language Models Are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales

Kwon,

Ong,

Kang

et al. 2024

AAAI

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Machine reasoning has made great progress in recent years owing to large language models (LLMs). In the clinical domain, however, most NLP-driven projects mainly focus on clinical classification or reading comprehension, and under-explore clinical reasoning for disease diagnosis due to the expensive rationale annotation with clinicians. In this work, we present a "reasoning-aware" diagnosis framework that rationalizes the diagnostic process via prompt-based learning in a time- and labor-efficient manner, and learns to reason over the prompt-generated rationales. Specifically, we address the clinical reasoning for disease diagnosis, where the LLM generates diagnostic rationales providing its insight on presented patient data and the reasoning path towards the diagnosis, namely Clinical Chain-of-Thought (Clinical CoT). We empirically demonstrate LLMs/LMs' ability of clinical reasoning via extensive experiments and analyses on both rationale generation and disease diagnosis in various settings. We further propose a novel set of criteria for evaluating machine-generated rationales' potential for real-world clinical settings, facilitating and benefiting future research in this area.

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Section: Related Workmentioning

confidence: 99%

Large Language Models Are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales

Kwon,

Ong,

Kang

et al. 2024

AAAI

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Deep learning enables the differentiation between early and late stages of hip avascular necrosis

et al. 2023

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Objectives To develop a deep learning methodology that distinguishes early from late stages of avascular necrosis of the hip (AVN) to determine treatment decisions. Methods Three convolutional neural networks (CNNs) VGG-16, Inception ResnetV2, InceptionV3 were trained with transfer learning (ImageNet) and finetuned with a retrospectively collected cohort of (n = 104) MRI examinations of AVN patients, to differentiate between early (ARCO 1–2) and late (ARCO 3–4) stages. A consensus CNN ensemble decision was recorded as the agreement of at least two CNNs. CNN and ensemble performance was benchmarked on an independent cohort of 49 patients from another country and was compared to the performance of two MSK radiologists. CNN performance was expressed with areas under the curve (AUC), the respective 95% confidence intervals (CIs) and precision, and recall and f1-scores. AUCs were compared with DeLong’s test. Results On internal testing, Inception-ResnetV2 achieved the highest individual performance with an AUC of 99.7% (95%CI 99–100%), followed by InceptionV3 and VGG-16 with AUCs of 99.3% (95%CI 98.4–100%) and 97.3% (95%CI 95.5–99.2%) respectively. The CNN ensemble the same AUCs Inception ResnetV2. On external validation, model performance dropped with VGG-16 achieving the highest individual AUC of 78.9% (95%CI 51.6–79.6%) The best external performance was achieved by the model ensemble with an AUC of 85.5% (95%CI 72.2–93.9%). No significant difference was found between the CNN ensemble and expert MSK radiologists (p = 0.22 and 0.092 respectively). Conclusion An externally validated CNN ensemble accurately distinguishes between the early and late stages of AVN and has comparable performance to expert MSK radiologists. Clinical relevance statement This paper introduces the use of deep learning for the differentiation between early and late avascular necrosis of the hip, assisting in a complex clinical decision that can determine the choice between conservative and surgical treatment. Key Points • A convolutional neural network ensemble achieved excellent performance in distinguishing between early and late avascular necrosis. • The performance of the deep learning method was similar to the performance of expert readers.

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To BERT or not to BERT: advancing non-invasive prediction of tumor biomarkers using transformer-based natural language processing (NLP)

Tejani

2023

Eur Radiol

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Natural language processing to predict isocitrate dehydrogenase genotype in diffuse glioma using MR radiology reports

Cited by 8 publications

References 32 publications

Large Language Models Are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales

Large Language Models Are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales

Deep learning enables the differentiation between early and late stages of hip avascular necrosis

To BERT or not to BERT: advancing non-invasive prediction of tumor biomarkers using transformer-based natural language processing (NLP)

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