Predicting oncogene mutations of lung cancer using deep learning and histopathologic features on whole-slide images

Tomita, Naofumi; Tafe, Laura J.; Suriawinata, Arief A.; Tsongalis, Gregory J.; Nasir-Moin, Mustafa; Dragnev, Konstantin H.; Hassanpour, Saeed

doi:10.1016/j.tranon.2022.101494

Cited by 15 publications

(7 citation statements)

References 25 publications

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“…This study aims to contribute to the ongoing advancements in DL-based diagnostics, in which molecular research on diseases and the performance of artificial intelligence-driven technologies has provided a strong impact 28 – 30 . The new 2021 WHO classification highlights the importance of IDH mutation and 1p/19q codeletion in the diagnosis of adult-type diffuse gliomas, which prompted us to focus on these two genetic abnormalities 3 – 5 .…”

Section: Discussionmentioning

confidence: 99%

Efficient diagnosis of IDH-mutant gliomas: 1p/19qNET assesses 1p/19q codeletion status using weakly-supervised learning

Kim,

Lee,

Ahn

et al. 2023

npj Precis. Onc.

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Accurate identification of molecular alterations in gliomas is crucial for their diagnosis and treatment. Although, fluorescence in situ hybridization (FISH) allows for the observation of diverse and heterogeneous alterations, it is inherently time-consuming and challenging due to the limitations of the molecular method. Here, we report the development of 1p/19qNET, an advanced deep-learning network designed to predict fold change values of 1p and 19q chromosomes and classify isocitrate dehydrogenase (IDH)-mutant gliomas from whole-slide images. We trained 1p/19qNET on next-generation sequencing data from a discovery set (DS) of 288 patients and utilized a weakly-supervised approach with slide-level labels to reduce bias and workload. We then performed validation on an independent validation set (IVS) comprising 385 samples from The Cancer Genome Atlas, a comprehensive cancer genomics resource. 1p/19qNET outperformed traditional FISH, achieving R2 values of 0.589 and 0.547 for the 1p and 19q arms, respectively. As an IDH-mutant glioma classifier, 1p/19qNET attained AUCs of 0.930 and 0.837 in the DS and IVS, respectively. The weakly-supervised nature of 1p/19qNET provides explainable heatmaps for the results. This study demonstrates the successful use of deep learning for precise determination of 1p/19q codeletion status and classification of IDH-mutant gliomas as astrocytoma or oligodendroglioma. 1p/19qNET offers comparable results to FISH and provides informative spatial information. This approach has broader applications in tumor classification.

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Section: Discussionmentioning

confidence: 99%

Efficient diagnosis of IDH-mutant gliomas: 1p/19qNET assesses 1p/19q codeletion status using weakly-supervised learning

Kim,

Lee,

Ahn

et al. 2023

npj Precis. Onc.

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“… Understand the idea of goodness-of-fit statistics. Pulmonology Tomita et al, 2022 [ 21 ] Describe the indications for using neural networks. Describe the high-level mechanism of Convolutional Neural Networks in solving classification tasks Describe the implications of neural networks in predicting mutation/tumor types.…”

Section: Methodsmentioning

confidence: 99%

Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study

Park,

Pinto-Powell,

Thesen

et al. 2024

Medical Education Online

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Artificial intelligence (AI) is rapidly being introduced into the clinical workflow of many specialties. Despite the need to train physicians who understand the utility and implications of AI and mitigate a growing skills gap, no established consensus exists on how to best introduce AI concepts to medical students during preclinical training. This study examined the effectiveness of a pilot Digital Health Scholars (DHS) non-credit enrichment elective that paralleled the Dartmouth Geisel School of Medicine’s first-year preclinical curriculum with a focus on introducing AI algorithms and their applications in the concurrently occurring systems-blocks. From September 2022 to March 2023, ten self-selected first-year students enrolled in the elective curriculum run in parallel with four existing curricular blocks (Immunology, Hematology, Cardiology, and Pulmonology). Each DHS block consisted of a journal club, a live-coding demonstration, and an integration session led by a researcher in that field. Students’ confidence in explaining the content objectives (high-level knowledge, implications, and limitations of AI) was measured before and after each block and compared using Mann-Whitney U tests. Students reported significant increases in confidence in describing the content objectives after all four blocks (Immunology: U = 4.5, p = 0.030; Hematology: U = 1.0, p = 0.009; Cardiology: U = 4.0, p = 0.019; Pulmonology: U = 4.0, p = 0.030) as well as an average overall satisfaction level of 4.29/5 in rating the curriculum content. Our study demonstrates that a digital health enrichment elective that runs in parallel to an institution’s preclinical curriculum and embeds AI concepts into relevant clinical topics can enhance students’ confidence in describing the content objectives that pertain to high-level algorithmic understanding, implications, and limitations of the studied models. Building on this elective curricular design, further studies with a larger enrollment can help determine the most effective approach in preparing future physicians for the AI-enhanced clinical workflow.

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“…Machine learning models have shown high performance in predicting site of origin from cancers of unknown primary, 92 or virtual prediction of cellular protein expression, 93,94 and molecular aberrations from hematoxylin and eosin glass slides alone. [95][96][97][98][99][100][101][102][103] With appropriate training data, multi-omics information can provide quantitative data for machine learning models to predict patient outcomes (e.g., survival, response to treatment). [104][105][106][107][108][109][110][111][112][113][114][115][116][117][118][119] These systems have potential to change how pathology is practiced; however, machine learning models are not replacement for human expertise.…”

Section: Machine Learning In Pathologymentioning

confidence: 99%

“…Machine learning can aid in the precision medicine era encouraging discovery of novel biomarkers and targets by identifying patterns and associations between different multimodal data (e.g., histopathology, genomic, proteomic, clinical data). Machine learning models have shown high performance in predicting site of origin from cancers of unknown primary, 92 or virtual prediction of cellular protein expression, 93,94 and molecular aberrations from hematoxylin and eosin glass slides alone 95–103 . With appropriate training data, multi‐omics information can provide quantitative data for machine learning models to predict patient outcomes (e.g., survival, response to treatment) 104–119 .…”

Section: Image Analysis and Machine Learning In Pathologymentioning

confidence: 99%

Digital pathology systems enabling quality patient care

Hanna

Ardon

2023

Genes Chromosomes & Cancer

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Pathology laboratories are undergoing digital transformations, adopting innovative technologies to enhance patient care. Digital pathology systems impact clinical, education, and research use cases where pathologists use digital technologies to perform tasks in lieu of using glass slides and a microscope. Pathology professional societies have established clinical validation guidelines, and the US Food and Drug Administration have also authorized digital pathology systems for primary diagnosis, including image analysis and machine learning systems. Whole slide images, or digital slides, can be viewed and navigated similar to glass slides on a microscope. These modern tools not only enable pathologists to practice their routine clinical activities, but can potentially enable digital computational discovery. Assimilation of whole slide images in pathology clinical workflow can further empower machine learning systems to support computer assisted diagnostics. The potential enrichment these systems can provide is unprecedented in the field of pathology. With appropriate integration, these clinical decision support systems will allow pathologists to increase the delivery of quality patient care. This review describes the digital pathology transformation process, applicable clinical use cases, incorporation of image analysis and machine learning systems in the clinical workflow, as well as future technologies that may further disrupt pathology modalities to deliver quality patient care.

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Predicting oncogene mutations of lung cancer using deep learning and histopathologic features on whole-slide images

Cited by 15 publications

References 25 publications

Efficient diagnosis of IDH-mutant gliomas: 1p/19qNET assesses 1p/19q codeletion status using weakly-supervised learning

Efficient diagnosis of IDH-mutant gliomas: 1p/19qNET assesses 1p/19q codeletion status using weakly-supervised learning

Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study

Digital pathology systems enabling quality patient care

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