A whole-slide image (WSI)-based immunohistochemical feature prediction system improves the subtyping of lung cancer

Chen, Yanyang; Yang, Huan; Cheng, Zhiqiang; Chen, Lili; Peng, Sui; Wang, Jianbo; Yang, Mo; Lin, Cheng-Hao; Wang, Yuefeng; Huang, Leilei; Chen, Yangshan; Li, Weizhong; Ke, Zunfu

doi:10.1016/j.lungcan.2022.01.005

Cited by 18 publications

(15 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The prediction performance of our models is not directly comparable with those of previous work due to the use of different datasets; however, our promising performance in predicting EGFR mutations is aligned with the previous work [ 11 , 14 ]. Of note, EGFR mutations, which one of our proposed models identifies at an AUC of 0.799 based on whole-slide images in the DHMC test set, is an important factor in the targeted treatment of NSCLC patients.…”

Section: Discussionsupporting

confidence: 52%

See 1 more Smart Citation

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

Tomita¹,

Tafe²,

Suriawinata³

et al. 2022

Translational Oncology

View full text Add to dashboard Cite

Section: Discussionsupporting

confidence: 52%

“…In recent work, Chen et al. presented a two-stage CNN model to predict EGFR and KRAS mutations and achieved an AUC of 0.683 and 0.545 for EGFR and KRAS on their test set, respectively [14] . Another study by Huang et al.…”

Section: Introductionmentioning

confidence: 99%

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

Tomita¹,

Tafe²,

Suriawinata³

et al. 2022

Translational Oncology

View full text Add to dashboard Cite

“…In addition, a variety of deep learning algorithms have been developed for automatically predicting the subtypes of tumors ( 7 , 53 , 54 ), such as Lu et al. ( 53 ) proposed a CLAM (clustering-constrained-attention multiple-instance learning) method to the subtyping of renal cell carcinoma and non-small-cell lung cancer as well as the detection of lymph node metastasis.…”

Section: Discussionmentioning

confidence: 99%

A semi-supervised learning approach with consistency regularization for tumor histopathological images analysis

Jiang

Sui

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

IntroductionManual inspection of histopathological images is important in clinical cancer diagnosis. Pathologists implement pathological diagnosis and prognostic evaluation through the microscopic examination of histopathological slices. This entire process is time-consuming, laborious, and challenging for pathologists. The modern use of whole-slide imaging, which scans histopathology slides to digital slices, and analysis using computer-aided diagnosis is an essential problem.MethodsTo solve the problem of difficult labeling of histopathological data, and improve the flexibility of histopathological analysis in clinical applications, we herein propose a semi-supervised learning algorithm coupled with consistency regularization strategy, called“Semi- supervised Histopathology Analysis Network”(Semi-His-Net), for automated normal-versus-tumor and subtype classifications. Specifically, when inputted disturbing versions of the same image, the model should predict similar outputs. Based on this, the model itself can assign artificial labels to unlabeled data for subsequent model training, thereby effectively reducing the labeled data required for training.ResultsOur Semi-His-Net is able to classify patches from breast cancer histopathological images into normal tissue and three other different tumor subtypes, achieving an accuracy was 90%. The average AUC of cross-classification between tumors reached 0.893.DiscussionTo overcome the limitations of visual inspection by pathologists for histopathology images, such as long time and low repeatability, we have developed a deep learning-based framework (Semi-His-Net) for automatic classification subdivision of the subtypes contained in the whole pathological images. This learning-based framework has great potential to improve the efficiency and repeatability of histopathological image diagnosis.

show abstract

“…Jiang et al categorize the implementation of computational pathology in oncology into five purposes, which are tumor diagnosis, subtyping, grading, staging, and prognosis [30]. Thus, we can find applications of these five purposes for breast cancer [30,[105][106][107][108], lung cancer [30,[109][110][111], colorectal cancer [30,[112][113][114][115], gastric cancer [30,116,117], prostate cancer [30,118,119], and thyroid cancer [30,120,121]. Another set of applications of computational pathology lies in the automatic analysis for the identification of rejection in organ transplantation.…”

Section: Computational Pathologymentioning

confidence: 99%

The New Landscape of Diagnostic Imaging with the Incorporation of Computer Vision

Cossio¹

2023

Artificial Intelligence

View full text Add to dashboard Cite

Diagnostic medical imaging is a key tool in medical care. In recent years, thanks to advances in computer vision research, a subfield of artificial intelligence, it has become possible to use medical imaging to train and test machine learning models. Among the algorithms investigated, there has been a boom in the use of neural networks since they allow a higher level of automation in the learning process. The areas of medical imaging that have developed the most applications are X-rays, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasonography and pathology. In fact, the COVID-19 pandemic has reshaped the research landscape, especially for radiological and resonance imaging. Notwithstanding the great progress that has been observed in the field, obstacles have also arisen that had to be overcome to continue to improve applications. These obstacles include data protection and the expansion of available datasets, which involves a large investment of resources, time and academically trained manpower.

show abstract

A whole-slide image (WSI)-based immunohistochemical feature prediction system improves the subtyping of lung cancer

Cited by 18 publications

References 33 publications

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

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

A semi-supervised learning approach with consistency regularization for tumor histopathological images analysis

The New Landscape of Diagnostic Imaging with the Incorporation of Computer Vision

Contact Info

Product

Resources

About