Advances in Lung Cancer Detection and Diagnosis: An Integrative Approach Using Computational Chemistry, Statistics, Bioinformatics, Artificial Intelligence, and Machine Learning

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Year : 2026 | Volume : 13 | Issue : 02 | Page :
By

Amish Mishra,

Anshika Singh,

Ahmar Hassan,

Yukti Sabikhi,

Sanya Kaushik,

Mohammad “Sufian” Badar,

  1. Student, Department of CSE, School of Engineering Science and Technology, Jamia Hamdard University, New Delhi, India
  2. Student, Department of CSE,School of Engineering Science and Technology, Jamia Hamdard University, New Delhi, India
  3. Student, Department of CSE, School of Engineering Science and Technology, Jamia Hamdard University, New Delhi, India
  4. Student, Department of CSE, School of Engineering Science and Technology, Jamia Hamdard University, New Delhi, India
  5. Student, Department of CSE, Council of Scientific and Industrial Research – Institute of Genomics and Integrative Biology, New Delhi, India
  6. Student, Department of CSE, Council of Scientific and Industrial Research – Institute of Genomics and Integrative Biology, New Delhi, India

Abstract

Lung cancer is still one of the most common and lethal cancers globally, accounting for more than a million deaths each year. Prompt detection is important, and imaging techniques like chest X-rays, MRIs, PETs, CTs, and molecular imaging have become important tools. But still, even though all these techniques do not provide an accurate classification of the lesion, they have led to the development of computer-based high-resolution image analysis. Computer algorithms like support vector machine, random forest, and multi-layer perceptron have proven efficient in distinguishing between benign and malignant tumors in the lungs. For feature selection, usually, statistical analysis and imaging techniques along with supervised learning are employed. In some cases, even more advanced methods like Monte Carlo feature selection have been used to select features that aid in the classification of genetic variations in lung adenocarcinoma and squamous cell carcinoma. Recent advancements include the combination of deep learning with clinical information such as genomics, demographic information, and response to treatment, which makes the diagnosis and prognosis highly personalized. The application of computational chemistry in this process enables multiscale modeling to facilitate the discovery of biomarkers, drug design, and integrating molecular scale data with imaging and clinical data. In the present review, novel advances in AI and deep learning for lung cancer have been presented, emphasizing the requirement to combine different data and computing techniques with the aim of improving the diagnosis of lung cancer and patient outcomes, as well as providing insights into future directions of clinical applications.

Keywords: Machine learning, Lung tumor classification, medical imaging, bioinformatics. Statistical analysis, Computational chemistry, Cancer Diagnosis

[This article belongs to Research & Reviews: A Journal of Bioinformatics ]

How to cite this article: Amish Mishra, Anshika Singh, Ahmar Hassan, Yukti Sabikhi, Sanya Kaushik, Mohammad “Sufian” Badar. Advances in Lung Cancer Detection and Diagnosis: An Integrative Approach Using Computational Chemistry, Statistics, Bioinformatics, Artificial Intelligence, and Machine Learning. Research & Reviews: A Journal of Bioinformatics. 2026; 13(02):-.
How to cite this URL: Amish Mishra, Anshika Singh, Ahmar Hassan, Yukti Sabikhi, Sanya Kaushik, Mohammad “Sufian” Badar. Advances in Lung Cancer Detection and Diagnosis: An Integrative Approach Using Computational Chemistry, Statistics, Bioinformatics, Artificial Intelligence, and Machine Learning. Research & Reviews: A Journal of Bioinformatics. 2026; 13(02):-. Available from: https://journals.stmjournals.com/rrjobi/article=2026/view=253523

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Regular Issue Subscription Review Article
Volume 13
Issue 02
Received 28/04/2026
Accepted 26/06/2026
Published 27/08/2026
Publication Time 121 Days

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