CroReLU: Cross-Crossing Space-Based Visual Activation Function for Lung Cancer Pathology Image Recognition

MPZL1 suppresses the cancer stem-like properties of lung cancer through β-catenin/TCF4 signaling

This study was designed to explore the influence of myelin protein zero-like protein 1 (MPZL1) on the stem-like properties of cancer cells and the underlying mechanism in lung adenocarcinoma. Real-time quantitative polymerase chain reaction (RT-qPCR) was utilized to evaluate mRNA expression level. CCK8, wound healing, and transwell assays were applied to assess cell proliferation, migration, and invasion. Tumorsphere-formation assay was utilized to assess cancer stem cell-like properties. LF3 was used to block the β-catenin/Transcription factor 4 (TCF-4) signaling. Xenograft nude mouse model was conducted; tumor weight and volume were recorded. Western blot assay was utilized to detect the expression levels of CD44, CD133, β-catenin, TCF-4, and MPZL1. Following MPZL1 knockdown, the mRNA expression levels of MPZL1, β-catenin, and TCF-4 were inhibited, while the mRNA expression levels of the above genes were increased after the MPZL1 overexpression. MPZL1 knockdown suppressed cell proliferation, migration, and invasion, reduced the tumorsphere-formation capacity, and restrained the expression levels of CD44 and CD133. However, MPZL1 overexpression promoted the cell proliferation, migration, and invasion, enhanced the tumorsphere-formation capacity, and increased the expression levels of CD44 and CD133. Interestingly, LF3 treatment partially revised the effect of MPZL1 overexpression. These findings were further corroborated by in vivo experiments. We concluded that MPZL1 could suppress the lung adenocarcinoma cells' proliferation, migration, invasion, and lung cancer stem cells characteristics. The underlying mechanism is involved in the activation of β-catenin/TCF-4 signaling.

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

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.

A multilayer perceptron-based model applied to histopathology image classification of lung adenocarcinoma subtypes

Objective

Lung cancer is one of the most common malignant tumors in humans. Adenocarcinoma of the lung is another of the most common types of lung cancer. In clinical medicine, physicians rely on the information provided by pathology tests as an important reference for the fifinal diagnosis of many diseases. Thus, pathological diagnosis is known as the gold standard for disease diagnosis. However, the complexity of the information contained in pathology images and the increase in the number of patients far exceeds the number of pathologists, especially in the treatment of lung cancer in less-developed countries.

Methods

This paper proposes a multilayer perceptron model for lung cancer histopathology image detection, which enables the automatic detection of the degree of lung adenocarcinoma infifiltration. For the large amount of local information present in lung cancer histopathology images, MLP IN MLP (MIM) uses a dual data stream input method to achieve a modeling approach that combines global and local information to improve the classifification performance of the model. In our experiments, we collected 780 lung cancer histopathological images and prepared a lung histopathology image dataset to verify the effectiveness of MIM.

Results

The MIM achieves a diagnostic accuracy of 95.31% and has a precision, sensitivity, specificity and F1-score of 95.31%, 93.09%, 93.10%, 96.43% and 93.10% respectively, outperforming the diagnostic results of the common network model. In addition, a number of series of extension experiments demonstrated the scalability and stability of the MIM.

Conclusions

In summary, MIM has high classifification performance and substantial potential in lung cancer detection tasks.

Histopathological Analysis for Detecting Lung and Colon Cancer Malignancies Using Hybrid Systems with Fused Features

Lung and colon cancer are among humanity's most common and deadly cancers. In 2020, there were 4.19 million people diagnosed with lung and colon cancer, and more than 2.7 million died worldwide. Some people develop lung and colon cancer simultaneously due to smoking which causes lung cancer, leading to an abnormal diet, which also causes colon cancer. There are many techniques for diagnosing lung and colon cancer, most notably the biopsy technique and its analysis in laboratories. Due to the scarcity of health centers and medical staff, especially in developing countries. Moreover, manual diagnosis takes a long time and is subject to differing opinions of doctors. Thus, artificial intelligence techniques solve these challenges. In this study, three strategies were developed, each with two systems for early diagnosis of histological images of the LC25000 dataset. Histological images have been improved, and the contrast of affected areas has been increased. The GoogLeNet and VGG-19 models of all systems produced high dimensional features, so redundant and unnecessary features were removed to reduce high dimensionality and retain essential features by the PCA method. The first strategy for diagnosing the histological images of the LC25000 dataset by ANN uses crucial features of GoogLeNet and VGG-19 models separately. The second strategy uses ANN with the combined features of GoogLeNet and VGG-19. One system reduced dimensions and combined, while the other combined high features and then reduced high dimensions. The third strategy uses ANN with fusion features of CNN models (GoogLeNet and VGG-19) and handcrafted features. With the fusion features of VGG-19 and handcrafted features, the ANN reached a sensitivity of 99.85%, a precision of 100%, an accuracy of 99.64%, a specificity of 100%, and an AUC of 99.86%.