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Review
. 2023 Oct 4:13:1224347.
doi: 10.3389/fonc.2023.1224347. eCollection 2023.

Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis

Robert Oehring  1 , Nikitha Ramasetti  1 , Sharlyn Ng  1 , Roland Roller  2 , Philippe Thomas  2 , Axel Winter  1 , Max Maurer  1 , Simon Moosburner  1 , Nathanael Raschzok  1 , Can Kamali  1 , Johann Pratschke  1 , Christian Benzing  1 , Felix Krenzien  1   3
Affiliations
Review

Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis

Robert Oehring et al. Front Oncol. .

Abstract

Background: For therapy planning in cancer patients multidisciplinary team meetings (MDM) are mandatory. Due to the high number of cases being discussed and significant workload of clinicians, Clinical Decision Support System (CDSS) may improve the clinical workflow.

Methods: This review and meta-analysis aims to provide an overview of the systems utilized and evaluate the correlation between a CDSS and MDM.

Results: A total of 31 studies were identified for final analysis. Analysis of different cancers shows a concordance rate (CR) of 72.7% for stage I-II and 73.4% for III-IV. For breast carcinoma, CR for stage I-II was 72.8% and for III-IV 84.1%, P≤ 0.00001. CR for colorectal carcinoma is 63% for stage I-II and 67% for III-IV, for gastric carcinoma 55% and 45%, and for lung carcinoma 85% and 83% respectively, all P>0.05. Analysis of SCLC and NSCLC yields a CR of 94,3% and 82,7%, P=0.004 and for adenocarcinoma and squamous cell carcinoma in lung cancer a CR of 90% and 86%, P=0.02.

Conclusion: CDSS has already been implemented in clinical practice, and while the findings suggest that its use is feasible for some cancers, further research is needed to fully evaluate its effectiveness.

Keywords: artificial intelligence; clinical decision support system; concordance between CDSS and MDS; machine learning; multidisciplinary team meetings.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Possible workflow of AI supporting MDMs. An automated program using artificial intelligence (machine learning, natural language processing) runs in the background of the hospital information system and can extract relevant data for MDM from the system. Afterwards, the tumor board protocol can be automatically prepared and filled out with all relevant patient data in preparation for the MDM. At the same time, the program could provide treatment suggestions based on the available data and support these with existing guidelines or studies. Based on this, the physicians in the MDM can then make the therapy decision. In the end, both physicians and patients could benefit. Created with BioRender.com.
Figure 2
Figure 2
Flow diagram of the study selection process. This figure was designed according to the PRISMA-Statement (13).
Figure 3
Figure 3
Overall concordance of various cancers in stages I–II and III-IV.
Figure 4
Figure 4
Overall concordance in breast cancer in stages I-II and III-IV.
Figure 5
Figure 5
Overall concordance in colorectal cancer in stages I-II and III-IV.
Figure 6
Figure 6
Overall concordance in gastric cancer in stages I-II and III-IV.
Figure 7
Figure 7
Overall concordance in cervical cancer in stages I-II and III-IV.
Figure 8
Figure 8
Overall concordance in lung cancer in stages I-II and III-IV.
Figure 9
Figure 9
Overall concordance in different lung cancer types for SCLC and NSCLC.
Figure 10
Figure 10
Overall concordance in NSCLC for histopathology type for adenocarcinoma and squamous cell carcinoma.
Figure 11
Figure 11
Overall concordance for ECOG 0-1 and 2-5.
See this image and copyright information in PMC

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