Deep learning classification of lung cancer histology using CT images

Chaunzwa, Tafadzwa L.; Hosny, Ahmed; Xu, Yihuan; Shafer, Andrea T.; Diao, Nancy; Lanuti, Michael; Christiani, David C.; Mak, Raymond H.; Aerts, Hugo J.W.L.

doi:10.1038/s41598-021-84630-x

Cited by 138 publications

(68 citation statements)

References 62 publications

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“…is study has helped us to analyse proposed model with various parameters to address the issues over the patients. Reference [24] presented the non-small cell lung cancer (NSCLC) tumour histology from noninvasive standard-of-care computed tomography (CT) data. is approach is used to address the histological phenotypes in lung cancer using deep learning techniques.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Diagnosis of Lung Cancer by Deep Learning Classification Using Wilcoxon Gain and Generator

Obulesu¹,

Kalyanasundaram²,

Dhiman³

et al. 2021

Journal of Healthcare Engineering

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Cancer is a complicated worldwide health issue with an increasing death rate in recent years. With the swift blooming of the high throughput technology and several machine learning methods that have unfolded in recent years, progress in cancer disease diagnosis has been made based on subset features, providing awareness of the efficient and precise disease diagnosis. Hence, progressive machine learning techniques that can, fortunately, differentiate lung cancer patients from healthy persons are of great concern. This paper proposes a novel Wilcoxon Signed-Rank Gain Preprocessing combined with Generative Deep Learning called Wilcoxon Signed Generative Deep Learning (WS-GDL) method for lung cancer disease diagnosis. Firstly, test significance analysis and information gain eliminate redundant and irrelevant attributes and extract many informative and significant attributes. Then, using a generator function, the Generative Deep Learning method is used to learn the deep features. Finally, a minimax game (i.e., minimizing error with maximum accuracy) is proposed to diagnose the disease. Numerical experiments on the Thoracic Surgery Data Set are used to test the WS-GDL method's disease diagnosis performance. The WS-GDL approach may create relevant and significant attributes and adaptively diagnose the disease by selecting optimal learning model parameters. Quantitative experimental results show that the WS-GDL method achieves better diagnosis performance and higher computing efficiency in computational time, computational complexity, and false-positive rate compared to state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

Adaptive Diagnosis of Lung Cancer by Deep Learning Classification Using Wilcoxon Gain and Generator

Obulesu¹,

Kalyanasundaram²,

Dhiman³

et al. 2021

Journal of Healthcare Engineering

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“…There have been many studies on the imaging-based deep learning model for predicting the histology of lung cancer (i.e., adenocarcinoma, squamous cell carcinoma, and small cell lung cancer), and it has shown relatively good accuracy [27][28][29][30]. However, there are few studies on radiomics-based machine learning or CT-based deep learning models predicting high grade histologic patterns of lung ADC (i.e., micropapillary or solid pattern, MPSol).…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma

Choi

Aum²,

Lee

et al. 2021

Cancers

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We aimed to develop a deep learning (DL) model for predicting high-grade patterns in lung adenocarcinomas (ADC) and to assess the prognostic performance of model in advanced lung cancer patients who underwent neoadjuvant or definitive concurrent chemoradiation therapy (CCRT). We included 275 patients with 290 early lung ADCs from an ongoing prospective clinical trial in the training dataset, which we split into internal–training and internal–validation datasets. We constructed a diagnostic DL model of high-grade patterns of lung ADC considering both morphologic view of the tumor and context view of the area surrounding the tumor (MC3DN; morphologic-view context-view 3D network). Validation was performed on an independent dataset of 417 patients with advanced non-small cell lung cancer who underwent neoadjuvant or definitive CCRT. The area under the curve value of the DL model was 0.8 for the prediction of high-grade histologic patterns such as micropapillary and solid patterns (MPSol). When our model was applied to the validation set, a high probability of MPSol was associated with worse overall survival (probability of MPSol >0.5 vs. <0.5; 5-year OS rate 56.1% vs. 70.7%), indicating that our model could predict the clinical outcomes of advanced lung cancer patients. The subgroup with a high probability of MPSol estimated by the DL model showed a 1.76-fold higher risk of death (HR 1.76, 95% CI 1.16–2.68). Our DL model can be useful in estimating high-grade histologic patterns in lung ADCs and predicting clinical outcomes of patients with advanced lung cancer who underwent neoadjuvant or definitive CCRT.

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“…Given the recent advancements in automated detection of lung cancer, future studies may want to address the limitation by exploring a hybrid model that detects a lesion, identifies an arbitrary point inside the lesion, and performs measurement using the input point. Second, it has been well documented that methods utilizing neural networks are subject to a well-recognized challenge of their black-box nature [ 37 , 38 ], which make it harder to fully explain the algorithm’s measurement behavior. A further study utilizing techniques from interpretable machine learning may be explored to address the challenge [ 39 , 40 ].…”

Section: Discussionmentioning

confidence: 99%

Deep learning for semi-automated unidirectional measurement of lung tumor size in CT

et al. 2021

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Background Performing Response Evaluation Criteria in Solid Tumor (RECISTS) measurement is a non-trivial task requiring much expertise and time. A deep learning-based algorithm has the potential to assist with rapid and consistent lesion measurement. Purpose The aim of this study is to develop and evaluate deep learning (DL) algorithm for semi-automated unidirectional CT measurement of lung lesions. Methods This retrospective study included 1617 lung CT images from 8 publicly open datasets. A convolutional neural network was trained using 1373 training and validation images annotated by two radiologists. Performance of the DL algorithm was evaluated 244 test images annotated by one radiologist. DL algorithm’s measurement consistency with human radiologist was evaluated using Intraclass Correlation Coefficient (ICC) and Bland-Altman plotting. Bonferroni’s method was used to analyze difference in their diagnostic behavior, attributed by tumor characteristics. Statistical significance was set at p < 0.05. Results The DL algorithm yielded ICC score of 0.959 with human radiologist. Bland-Altman plotting suggested 240 (98.4 %) measurements realized within the upper and lower limits of agreement (LOA). Some measurements outside the LOA revealed difference in clinical reasoning between DL algorithm and human radiologist. Overall, the algorithm marginally overestimated the size of lesion by 2.97 % compared to human radiologists. Further investigation indicated tumor characteristics may be associated with the DL algorithm’s diagnostic behavior of over or underestimating the lesion size compared to human radiologist. Conclusions The DL algorithm for unidirectional measurement of lung tumor size demonstrated excellent agreement with human radiologist.

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Deep learning classification of lung cancer histology using CT images

Cited by 138 publications

References 62 publications

Adaptive Diagnosis of Lung Cancer by Deep Learning Classification Using Wilcoxon Gain and Generator

Adaptive Diagnosis of Lung Cancer by Deep Learning Classification Using Wilcoxon Gain and Generator

Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma

Deep learning for semi-automated unidirectional measurement of lung tumor size in CT

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