ArticleCancer management and research2020
Concordance of Treatment Recommendations for Metastatic Non-Small-Cell Lung Cancer Between Watson for Oncology System and Medical Team.
Article in Cancer management and research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Leveraging artificial intelligence to support surgical oncology multidisciplinary team decision-making: a systematic review.Frontiers in artificial intelligence · 2026Pooled it
- Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes.Journal of cancer research and clinical oncology · 2026Review
- A Novel Microfluidic Platform for Circulating Tumor Cell Identification in Non-Small-Cell Lung Cancer.Micromachines · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review.Cancers · 2024Review
- Watson for oncology decision system for treatment consistency study in breast cancer.Clinical and experimental medicine · 2023Article
- Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis.Frontiers in oncology · 2023Review
- Assessing the decision quality of artificial intelligence and oncologists of different experience in different regions in breast cancer treatment.Frontiers in oncology · 2023Article
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
- A narrative review of artificial intelligence-assisted histopathologic diagnosis and decision-making for non-small cell lung cancer: achievements and limitations.Journal of thoracic disease · 2021Review
- In vitro studies of H520 cell cycle and apoptosis by anlotinib combined with radiotherapy.Thoracic cancer · 2021Article
- Artificial intelligence (AI) and big data in cancer and precision oncology.Computational and structural biotechnology journal · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe disease complexity of metastatic non-small-cell lung cancer (mNSCLC) makes it difficult for physicians to make clinical decisions efficiently and accurately. The Watson for Oncology (WFO) system of artificial intelligence might help physicians by providing fast and precise treatment regimens. This study measured the concordance of the medical treatment regimens of the WFO system and actual clinical regimens, with the aim of determining the suitability of WFO recommendations for Chinese patients with mNSCLC.
methodsRetrospective data of mNSCLC patients were input to the WFO, which generated a treatment regimen (WFO regimen). The actual regimen was made by physicians in a medical team for patients (medical-team regimen). The factors influencing the consistency of the two treatment options were analyzed by univariate and multivariate analyses.
resultsThe concordance rate was 85.16% between the WFO and medical-team regimens for mNSCLC patients. Logistic regression showed that the concordance differed significantly for various pathological types and gene mutations in two treatment regimens. Patients with adenocarcinoma had a lower rate of "recommended" regimen than those with squamous cell carcinoma. There was a statistically significant difference in EGFR-mutant patients for "not recommended" regimens with inconsistency rate of 18.75%. In conclusion, the WFO regimen has 85.16% consistency rate with medical-team regimen in our treatment center. The different pathological type and different gene mutation markedly influenced the agreement rate of the two treatment regimens.
conclusionWFO recommendations have high applicability to mNSCLC patients in our hospital. This study demonstrates that the valuable WFO system may assist the doctors better to determine the accurate and effective treatment regimens for mNSCLC patients in the Chinese medical setting.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.