Abstract
Lung cancer is one of the major causes of cancer-related deaths in many countries around the world, and its histopathological diagnosis is crucial for deciding on optimum treatment strategies. Recently, Artificial Intelligence (AI) deep learning models have been widely shown to be useful in various medical fields, particularly image and pathological diagnoses; however, AI models for the pathological diagnosis of pulmonary lesions that have been validated on large-scale test sets are yet to be seen. We trained a Convolution Neural Network (CNN) based on the EfficientNet-B3 architecture, using transfer learning and weakly-supervised learning, to predict carcinoma in Whole Slide Images (WSIs) using a training dataset of 3,554 WSIs. We obtained highly promising results for differentiating between lung carcinoma and non-neoplastic with high Receiver Operator Curve (ROC) area under the curves (AUCs) on four independent test sets (ROC AUCs of 0.975, 0.974, 0.988, and 0.981, respectively). Development and validation of algorithms such as ours are important initial steps in the development of software suites that could be adopted in routine pathological practices and potentially help reduce the burden on pathologists.
Highlights
Lung cancer is one of the major causes of cancer-related deaths in many countries around the world, and its histopathological diagnosis is crucial for deciding on optimum treatment strategies
We evaluated our model on five independent test sets: 500 from Kyushu Medical Centre, 500 from Mita Hospital, 670 from the publicly available repository of The Cancer Genome Atlas (TCGA) program[24], and 500 from the Cancer Imaging Archive (TCIA)
The model was trained on Whole Slide Images (WSIs) obtained from a single medical institution and was applied on four independent test sets obtained from different sources to demonstrate the generalisation of the model on unseen data
Summary
Lung cancer is one of the major causes of cancer-related deaths in many countries around the world, and its histopathological diagnosis is crucial for deciding on optimum treatment strategies. The application of deep learning requires an annotated dataset to use to train the models, with larger datasets typically achieving better performance What this entails is that detailed cell-level WSI annotations by expert pathologists are required, making it extremely difficult to compile a large dataset, especially for WSIs consisting of surgical specimens. The most notable application of MIL in computational pathology was done recently by Campanella et al.[15] in which they used it to train on a dataset of 44,732 WSIs using only slide-level diagnoses as labels with impressive results obtained on test sets of prostate cancer, basal cell carcinoma, and breast cancer metastases
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