Dual-scale categorization based deep learning to evaluate programmed cell death ligand 1 expression in non-small cell lung cancer

Wang, Xiangyun; Chen, Peilin; Ding, Guofu; Xing, Yishi; Tang, Rongrong; Peng, Chaolong; Ye, Yizhou; Fu, Qiang

doi:10.1097/md.0000000000025994

Cited by 10 publications

(6 citation statements)

References 38 publications

(80 reference statements)

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“…The assessment results of trained pathologists' evaluation of PD-L1 expression are usually more accurate than those of untrained pathologists (33). It takes extensive professional learning and training for an untrained pathologist to become experienced (34). Previous studies have indicated that the assessment of PD-L1 expression by untrained pathologists has lower intraclass consistency compared with that by highly trained pathologists (35).…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer

et al. 2022

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Programmed cell death ligand 1 (PD-L1) is a critical biomarker for predicting the response to immunotherapy. However, traditional quantitative evaluation of PD-L1 expression using immunohistochemistry staining remains challenging for pathologists. Here we developed a deep learning (DL)-based artificial intelligence (AI) model to automatically analyze the immunohistochemical expression of PD-L1 in lung cancer patients. A total of 1,288 patients with lung cancer were included in the study. The diagnostic ability of three different AI models (M1, M2, and M3) was assessed in both PD-L1 (22C3) and PD-L1 (SP263) assays. M2 and M3 showed improved performance in the evaluation of PD-L1 expression in the PD-L1 (22C3) assay, especially at 1% cutoff. Highly accurate performance in the PD-L1 (SP263) was also achieved, with accuracy and specificity of 96.4 and 96.8% in both M2 and M3, respectively. Moreover, the diagnostic results of these three AI-assisted models were highly consistent with those from the pathologist. Similar performances of M1, M2, and M3 in the 22C3 dataset were also obtained in lung adenocarcinoma and lung squamous cell carcinoma in both sampling methods. In conclusion, these results suggest that AI-assisted diagnostic models in PD-L1 expression are a promising tool for improving the efficiency of clinical pathologists.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer

et al. 2022

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“…The inclusion of patients in immunochemotherapy protocols with Pembrolizumab monotherapy or its combinations passes through PDL1 positive cell count, since inter-observer variability can easily shift the therapeutic plan due to the low amount of positive cells needed to reach the TPS cut-off [9]. Indeed, attempts to use DNN for TPS computation in lung cancer already exist in literature [50]. Moreover, given the growing knowledge about disease molecular therapy targets and interest in Precision Medicine, a future increase in the number of routinely analyzed immunohistochemical predictive and prognostic markers becomes a reasonable educated guess [15].…”

Section: Discussionmentioning

confidence: 99%

Resource-Limited Automated Ki67 Index Estimation in Breast Cancer

Gliozzo,

Marinò,

Bonometti

et al. 2023

Proceedings of the 2023 10th International Conference on Bioinformatics Research and Applications

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The prediction of tumor progression and chemotherapy response has been recently tackled exploiting Tumor Infiltrating Lymphocytes (TILs) and the nuclear protein Ki67 as prognostic factors. Recently, deep neural networks (DNNs) have been shown to achieve top results in estimating Ki67 expression and simultaneous determination of intratumoral TILs score in breast cancer cells. However, in the last ten years the extraordinary progress induced by deep models proliferated at least as much as their resource demand. The exorbitant computational costs required to query (and in some cases also to store) a deep model represent a strong limitation in resource-limited contexts, like that of IoT-based applications to support healthcare personnel. To this end, we propose a resource consumption-aware DNN for the effective estimate of the percentage of Ki67-positive cells in breast cancer screenings. Our approach reduced up to 75% and 89% the usage of memory and disk space respectively, up to 1.5× the energy consumption, and preserved or improved the overall accuracy of a benchmark state-of-the-art solution. Encouraged by such positive results, we developed and structured the adopted framework so as to allow its general purpose usage, along with a public software repository to support its usage.

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“…The model's classification accuracy was 74.51%, higher than trainees (71.55%) but lower than subspecialist and non-subspecialist pathologists (97.06% and 84.03%, respectively). In another study, TPS assessment reached high performance in terms of sensitivity and specificity in both 1% and 50% cut-off points [112]. The classification was performed on slides stained with 22C3 antibody, and the proposed patch-based dual-scale categorization method based on VGG16 architecture achieved higher performance compared to VGG16.…”

Section: Pd-l1 Expression Statusmentioning

confidence: 95%

Deep Learning for Lung Cancer Diagnosis, Prognosis and Prediction Using Histological and Cytological Images: A Systematic Review

Davri

Birbas

Kanavos

et al. 2023

Cancers

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Lung cancer is one of the deadliest cancers worldwide, with a high incidence rate, especially in tobacco smokers. Lung cancer accurate diagnosis is based on distinct histological patterns combined with molecular data for personalized treatment. Precise lung cancer classification from a single H&E slide can be challenging for a pathologist, requiring most of the time additional histochemical and special immunohistochemical stains for the final pathology report. According to WHO, small biopsy and cytology specimens are the available materials for about 70% of lung cancer patients with advanced-stage unresectable disease. Thus, the limited available diagnostic material necessitates its optimal management and processing for the completion of diagnosis and predictive testing according to the published guidelines. During the new era of Digital Pathology, Deep Learning offers the potential for lung cancer interpretation to assist pathologists’ routine practice. Herein, we systematically review the current Artificial Intelligence-based approaches using histological and cytological images of lung cancer. Most of the published literature centered on the distinction between lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung carcinoma, reflecting the realistic pathologist’s routine. Furthermore, several studies developed algorithms for lung adenocarcinoma predominant architectural pattern determination, prognosis prediction, mutational status characterization, and PD-L1 expression status estimation.

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Dual-scale categorization based deep learning to evaluate programmed cell death ligand 1 expression in non-small cell lung cancer

Cited by 10 publications

References 38 publications

Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer

Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer

Resource-Limited Automated Ki67 Index Estimation in Breast Cancer

Deep Learning for Lung Cancer Diagnosis, Prognosis and Prediction Using Histological and Cytological Images: A Systematic Review

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