Artificial intelligence-assisted decision making for prognosis and drug efficacy prediction in lung cancer patients: a narrative review

Jingwei Li; Jiayang Wu; Zhehao Zhao; Qiran Zhang; Jun Shao; Chengdi Wang; Zhixin Qiu; Weimin Li

doi:10.21037/jtd-21-864

Artificial intelligence-assisted decision making for prognosis and drug efficacy prediction in lung cancer patients: a narrative review

J Thorac Dis. 2021 Dec;13(12):7021-7033. doi: 10.21037/jtd-21-864.

Authors

Jingwei Li^{1

2}, Jiayang Wu³, Zhehao Zhao², Qiran Zhang², Jun Shao¹, Chengdi Wang¹, Zhixin Qiu¹, Weimin Li¹

Affiliations

¹ Department of Respiratory and Critical Care Medicine, West China Medical School/West China Hospital, Sichuan University, Chengdu, China.
² West China Medical School/West China Hospital, Sichuan University, Chengdu, China.
³ West China School of Public Health/West China Fourth Hospital, Sichuan University, Chengdu, China.

Abstract

Objective: In this review, we aim to present frontier studies in patients with lung cancer as it related to artificial intelligence (AI)-assisted decision-making and summarize the latest advances, challenges and future trend in this field.

Background: Despite increasing survival rate in cancer patients over the last decades, lung cancer remains one of the leading causes of death worldwide. The early diagnosis, accurate evaluation and individualized treatment are vital approaches to improve the survival rate of patients with lung cancer. Thus, decision making based on these approaches requires accuracy and efficiency beyond manpower. Recent advances in AI and precision medicine have provided a fertile environment for the development of AI-based models. These models have the potential to assist radiologists and oncologists in detecting lung cancer, predicting prognosis and developing personalized treatment plans for better outcomes of the patients.

Methods: We searched literature from 2000 through July 31^th, 2021 in Medline/PubMed, the Web of Science, the Cochrane Library, ACM Digital Library, INSPEC and EMBASE. Key words such as "artificial intelligence", "AI", "deep learning", "lung cancer", "NSCLC", "SCLC" were combined to identify related literatures. These literatures were then selected by two independent authors. Articles chosen by only one author will be examined by another author to determine whether this article was relative and valuable. The selected literatures were read by all authors and discussed to draw reliable conclusions.

Conclusions: AI, especially for those based on deep learning and radiomics, is capable of assisting clinical decision making from many aspects, for its quantitatively interpretation of patients' information and its potential to deal with the dynamics, individual differences and heterogeneity of lung cancer. Hopefully, remaining problems such as insufficient data and poor interpretability may be solved to put AI-based models into clinical practice.

Keywords: Artificial intelligence (AI); drug efficacy; lung cancer; prognosis.

Publication types

Review