Artificial intelligence predicts lung cancer radiotherapy response: A meta-analysis

Xing, Wenmin; Gao, Wenyan; Lv, Xiaoling; Zhao, Zhijin; Xu, Xiaogang; Wu, Zhibing; Mao, Genxiang

doi:10.1016/j.artmed.2023.102585

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…Its application in lung cancer treatment currently sounds limitless and is being tested from cancer screening, diagnosis, and therapy evaluation to outcome prediction. Several trials and meta-analyses showed improvements in patients' healthcare due to AI intervention in several fields [9][10][11]. In particular, AI-based oncological tools have shown great potential with performances comparable to or even higher than human capacities [10,11].…”

Section: Introductionmentioning

confidence: 99%

Lung Cancer Surgery in Octogenarians: Implications and Advantages of Artificial Intelligence in the Preoperative Assessment

Bassi,

Vaz Sousa,

Zacchini

et al. 2024

Healthcare

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The general world population is aging and patients are often diagnosed with early-stage lung cancer at an advanced age. Several studies have shown that age is not itself a contraindication for lung cancer surgery, and therefore, more and more octogenarians with early-stage lung cancer are undergoing surgery with curative intent. However, octogenarians present some peculiarities that make surgical treatment more challenging, so an accurate preoperative selection is mandatory. In recent years, new artificial intelligence techniques have spread worldwide in the diagnosis, treatment, and therapy of lung cancer, with increasing clinical applications. However, there is still no evidence coming out from trials specifically designed to assess the potential of artificial intelligence in the preoperative evaluation of octogenarian patients. The aim of this narrative review is to investigate, through the analysis of the available international literature, the advantages and implications that these tools may have in the preoperative assessment of this particular category of frail patients. In fact, these tools could represent an important support in the decision-making process, especially in octogenarian patients in whom the diagnostic and therapeutic options are often questionable. However, these technologies are still developing, and a strict human-led process is mandatory.

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Section: Introductionmentioning

confidence: 99%

Lung Cancer Surgery in Octogenarians: Implications and Advantages of Artificial Intelligence in the Preoperative Assessment

Bassi,

Vaz Sousa,

Zacchini

et al. 2024

Healthcare

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Training and validating a treatment recommender with partial verification evidence

Unnikrishnan,

Puga,

Schleicher

et al. 2024

Artificial Intelligence in Medicine

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Review of meta-analyses on the use of artificial intelligence in radiology

Vasilev,

Vladzimirskyy,

Omelyanskaya

et al. 2024

Medicinskaâ vizualizaciâ

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Introduction. Artificial intelligence (AI) is an effective tool for automating routine tasks in radiology. The diagnostic accuracy of AI in detecting various pathologies on medical images has generated considerable interest in the scientific community: the number of studies and meta-analyses has been constantly growing. The abundance of published evidence and the diversity of outcomes necessitate the need to systematize the available publications. The aim of this paper is to conduct an umbrella systematic review of contemporary meta-analyses on the use of AI in radiology.Materials and methods. PubMed was searched for studies published in the English language. Thirty-eight systematic reviews with meta-analyses published between 2021 and 2023 were selected for full-text analysis. The extracted data included the goal, study design, imaging modality, sample size, quality assessment of the included studies, AI diagnostic accuracy estimates, reference method parameters, and clinical efficacy metrics of AI implementation. The methodological quality of included systematic reviews was assessed using the AMSTAR-2 tool.Results. Nearly half (47%) of the included meta-analyses focused on the diagnosis, staging and segmentation of malignancies. Four meta-analyses were related to detection of maxillofacial structures in dentistry, while another four meta-analyses addressed the diagnosis of brain lesions. The diagnosis of COVID-19 and the diagnosis of bone fractures were each covered in three meta-analyses. One meta-analysis was reviewed for each of the following fields: colorectal polyps, pneumothorax, pulmonary embolism, osteoporosis, aneurysms, multiple sclerosis, acute cerebrovascular accident, intracranial hemorrhage, burns, and the risk of intrauterine growth restriction. Thirty-five (92%) meta-analyses assessed the risk of bias. Twenty-eight (80%) meta-analyses utilized QUADAS-2 to assess the risk of bias. 14 out of 28 papers reported low risk of bias (50%); 4 (14%) – moderate; 10 (36%) – high. The major risks were associated with samples that were unbalanced in terms of size and composition, a lack of details about the methods, a low number of prospective studies, and a lack of external validation of the outcomes. The overall results indicate that the diagnostic accuracy of AI is comparable to or even greater than that of radiologists. The mean sensitivity, specificity and area under the ROC curve for AI and radiologists were 85.2%, 89.5%, 93.5% and 84.4%, 90.0%, 92.8%, respectively. However, many studies that compared the diagnostic accuracy of AI and radiologists lack the data on the number and experience of the latter. Only one paper presented results of implementing AI into routine clinical diagnosis.Discussion. AI is capable of reducing the turnaround time for non-urgent examinations. When used to verify the primary interpretation, AI was effective in detecting false-negative results from radiologists. However, the efficacy of detecting false-positive results was inadequate. Our assessment of the quality of systematic reviews with AMSTAR-2 show that the methods of searching, selecting and analyzing literature must be improved and brought to a common standard. The development of a specialized tool for assessing the quality of systematic reviews in the AI implementation is also necessary. Due to high diagnostic accuracy, AI is currently considered a promising tool for optimizing the turnaround time. However, more evidence is needed to study the AI outcomes in routine clinical practice. Furthermore, it is necessary to standardize and improve the quality of research methodology.

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Artificial intelligence predicts lung cancer radiotherapy response: A meta-analysis

Cited by 3 publications

References 35 publications

Lung Cancer Surgery in Octogenarians: Implications and Advantages of Artificial Intelligence in the Preoperative Assessment

Lung Cancer Surgery in Octogenarians: Implications and Advantages of Artificial Intelligence in the Preoperative Assessment

Training and validating a treatment recommender with partial verification evidence

Review of meta-analyses on the use of artificial intelligence in radiology

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