Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer

Zhang, Yibo; Yang, Zijian; Chen, Ruanqi; Zhu, Yanli; Liu, Li; Dong, Jiyan; Zhang, Zicheng; Sun, Xujie; Ying, Jianming; Lin, Dongmei; Yang, Lin; Zhou, Meng

doi:10.1038/s41746-024-01003-0

Cited by 20 publications

(1 citation statement)

References 24 publications

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“…Lung cancer remains one of the most prevalent malignancies and represents the foremost cause of cancer-related mortality worldwide ( 1 , 2 ), the majority of lung cancers (80–90%) manifest as non-small cell lung cancer (NSCLC), often diagnosed at an advanced stage (65%), potentially with concurrent local or distant metastasis ( 3 ). Recent advances in immunotherapy, particularly the use of immune checkpoint inhibitors (ICIs), have shown promising outcomes in enhancing the prognosis of lung cancer patients ( 4 ). Nevertheless, not all patients experience the benefits of immunotherapy, highlighting the need for additional research into predictive biomarkers of immune response.…”

Section: Introductionmentioning

confidence: 99%

Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis

Yuan,

Shen,

Shan

et al. 2024

Front. Oncol.

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BackgroundPathomics has emerged as a promising biomarker that could facilitate personalized immunotherapy in lung cancer. It is essential to elucidate the global research trends and emerging prospects in this domain.MethodsThe annual distribution, journals, authors, countries, institutions, and keywords of articles published between 2018 and 2023 were visualized and analyzed using CiteSpace and other bibliometric tools.ResultsA total of 109 relevant articles or reviews were included, demonstrating an overall upward trend; The terms “deep learning”, “tumor microenvironment”, “biomarkers”, “image analysis”, “immunotherapy”, and “survival prediction”, etc. are hot keywords in this field.ConclusionIn future research endeavors, advanced methodologies involving artificial intelligence and pathomics will be deployed for the digital analysis of tumor tissues and the tumor microenvironment in lung cancer patients, leveraging histopathological tissue sections. Through the integration of comprehensive multi-omics data, this strategy aims to enhance the depth of assessment, characterization, and understanding of the tumor microenvironment, thereby elucidating a broader spectrum of tumor features. Consequently, the development of a multimodal fusion model will ensue, enabling precise evaluation of personalized immunotherapy efficacy and prognosis for lung cancer patients, potentially establishing a pivotal frontier in this domain of investigation.

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Section: Introductionmentioning

confidence: 99%

Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis

Yuan,

Shen,

Shan

et al. 2024

Front. Oncol.

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Spatial Transcriptome‐Wide Profiling of Small Cell Lung Cancer Reveals Intra‐Tumoral Molecular and Subtype Heterogeneity

Zhang,

Sun,

Liu

et al. 2024

Advanced Science

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Small cell lung cancer (SCLC) is a highly aggressive malignancy characterized by rapid growth and early metastasis and is susceptible to treatment resistance and recurrence. Understanding the intra‐tumoral spatial heterogeneity in SCLC is crucial for improving patient outcomes and clinically relevant subtyping. In this study, a spatial whole transcriptome‐wide analysis of 25 SCLC patients at sub‐histological resolution using GeoMx Digital Spatial Profiling technology is performed. This analysis deciphered intra‐tumoral multi‐regional heterogeneity, characterized by distinct molecular profiles, biological functions, immune features, and molecular subtypes within spatially localized histological regions. Connections between different transcript‐defined intra‐tumoral phenotypes and their impact on patient survival and therapeutic response are also established. Finally, a gene signature, termed ITHtyper, based on the prevalence of intra‐tumoral heterogeneity levels, which enables patient risk stratification from bulk RNA‐seq profiles is identified. The prognostic value of ITHtyper is rigorously validated in independent multicenter patient cohorts. This study introduces a preliminary tumor‐centric, regionally targeted spatial transcriptome resource that sheds light on previously unexplored intra‐tumoral spatial heterogeneity in SCLC. These findings hold promise to improve tumor reclassification and facilitate the development of personalized treatments for SCLC patients.

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Application of quantitative histomorphometric features in computational pathology

Shi,

Hu,

et al. 2024

Interdisciplinary Medicine

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Computer vision has facilitated the execution of various computer‐aided diagnostic tasks. From a methodological perspective, these tasks are primarily implemented using two dominant strategies: end‐to‐end Deep learning (DL)‐based methods and traditional feature engineering‐based methods. DL methods are capable of automatically extracting, analyzing, and filtering features, leading to final decision‐making from whole slide images. However, these methods are often criticized for the “black box” issue, a significant limitation of DL. In contrast, traditional feature engineering‐based methods involve well‐defined quantitative input features. But it was considered as less potent than DL methods. Advances in segmentation technology and the development of quantitative histomorphometric (QH) feature representation have propelled the evolution of feature engineering‐based methods. This review contrasts the performance differences between the two methods and traces the development of QH feature representation. The conclusion is that, with the ongoing progress in QH feature representation and segmentation technology, methods based on QH features will leverage their advantages—such as explainability, reduced reliance on large training datasets, and lower computational resource requirements—to play a more significant role in some clinical tasks. They may even replace DL methods somewhat or be used in conjunction with them to achieve accurate and understandable results.

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Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer

Cited by 20 publications

References 24 publications

Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis

Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis

Spatial Transcriptome‐Wide Profiling of Small Cell Lung Cancer Reveals Intra‐Tumoral Molecular and Subtype Heterogeneity

Application of quantitative histomorphometric features in computational pathology

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