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

Abstract

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.

The future of artificial intelligence in thoracic surgery for non-small cell lung cancer treatment a narrative review

“…A salient concern extracted from the systematic review is the conspicuous lack of interpretability and explanation within certain AI applications ( 47 ). While the proficiency of AI models in classification tasks is evident, the paucity of concerted efforts in addressing common sense reasoning, especially in deciphering the intricate physical characteristics of cells, poses a critical challenge ( 36 ).…”

“…Overfitting tendencies, wherein models excel in performance on training data but falter when presented with new or unlabeled data, pose a substantial challenge. In the context of lung cancer diagnosis, characterized by variations in imaging techniques and equipment, achieving robust generalization becomes a formidable task ( 47 ). The demand for models that can seamlessly adapt to diverse clinical settings is not only an academic concern but a practical necessity for the broader implementation of AI in lung cancer care.…”

Applying the Deep Learning Techniques to Solve Classification Tasks Using Gene Expression Data

“…The research underscores how deep learning facilitates high-resolution insights, crucial for advancing precision oncology, including early cancer detection, diagnosis, patient survival rate assessment, and cancer treatment planning. Similarly, [4] addresses the diagnostic challenges of lung cancer using single histological slides. Employing recent advancements in digital pathology, this study illustrates the potential of DL in classifying lung cancer subtypes, predicting outcomes, deciphering mutational patterns, and estimating expression from histological and cytological images.…”

“…Considering the results of previous studies [10], [11], [21], the activation functions applied were the sigmoid function (sigmoid) for convolutional layers, the SELU (Scaled Exponential Linear Unit) function for the dense layer, and the softmax function for the output layer of neurons. The range of values for the relevant hyperparameters was as follows: num f ilters = [8, 64], kernel s ize = [3,10], max p ooling = [2,4], and dense k ernel = [16,256]. The initial number of points in the hyperparameter feature space was set at 10, and the number of subsequent iterations to search for the optimal hyperparameter combination was 50 when applying a one-layer CNN and 70 for a twolayer CNN.…”

Applying a Recurrent Neural Network-Based Deep Learning Model for Gene Expression Data Classification

“…CNNs have emerged as a pivotal tool for classifying gene expression data, thanks to their autonomous feature-learning capability, which curtails the need for manual intervention in high-dimensional genomic data extraction [5][6][7]. By recognizing patterns effectively, they capture both local and global spatial hierarchies of gene expression profiles, a key aspect in identifying complex biological states.…”