Cancer occurs in cells in a particular area of the body proliferating and replicating uncontrollably. These cells can penetrate and ravage nearby healthy tissue, including organs. The common type of cancers includes breast, lung, colon, skin, prostate, and ovarian cancer. With an estimated 1.8 million annual fatalities, lung cancer is the most common type of cancer. About 20% of lung cancers are squamous cell carcinomas (SCC), which are more strongly linked to smoking than lung adenocarcinomas. It often starts in the basal cells of the bronchial mucosa and usually develops in the proximal region of the airway. The preinvasive precursor lesion of SCC is squamous cell carcinoma in situ (SCIS). 30% of SCIS regress but clinical management is quite challenging. Clinical decisions are made more difficult by the patients’ various comorbid conditions.
Lung Cancer
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Deep learning is a better approach
Recent years have seen a rise in the use of low-dose helical CT screening as a potential means of increasing SCC patient survival. Sadly, a CT scan may not always pick up preinvasive tumors. To help direct future monitoring and treatment, it would be extremely helpful to be able to anticipate which SCIS will develop into SCC. The molecular profiles of SCIS have been defined in earlier research to forecast whether they will develop into SCC or spontaneously regress. However, morphology was not directly used to predict progression because regressive and progressive lesions are identical to one another. A better approach known as deep learning(DL) is a branch of machine learning that makes use of artificial neural networks (ANN) to extract patterns from extremely complicated data. Due to the high information density that the DL approach provides, histopathology in particular and medical imaging, in general, are suited for investigation.
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A study was carried out by a group of scientists to assess the efficiency of DL. This technique was used on H&E stained picture to determine which SCIS lesions develop into SCC and which lesions regress. WSI dataset of SCIS patients that was made available to the public was used. Among patients with these kinds of lesions, this cohort is the largest. The total number of the dataset was 112 H&E stained WSI in SVI format with the designation for the corresponding regression or progression. A thoracic pathologist chose regions of interest containing the lesions to create the picture dataset used to train the deep convolutional neural network (DCNN). Conclusively, It is thought that DL is a potential approach for forecasting the evolution of preinvasive lesions because the model was trained using a very little dataset by the standards of a DCNN. The method showed to be quite effective at identifying the development of SCIS into SCC.
Clinical significance
With Deep learning technology, the progress of a disease can be assessed. DL has the potential to be employed as a low-cost technique that could give patients with preinvasive lesions prognostic information. DL has the potential to be a low-cost, high-throughput technique to give patients and clinicians more prognostic data while also being a component of the screening toolset.
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Conclusion
AI plays an important role in healthcare. This is the first study that attempts to forecast the progression of a preinvasive lesion using DL on pathology images. It is expected that this study will encourage additional research into the application of DL on WSI to forecast the development of other preinvasive precursor lesions.
References
Predicting the evolution of lung squamous cell carcinoma in situ using deep learning
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