Background: IBM Watson for Oncology (WFO) is a cognitive computing system helping physicians quickly identify key information in a patient's medical record, surface relevant evidence, and explore treatment options. This study assessed the possibility of using WFO for clinical treatment in lung cancer patients.
Methods: We evaluated the level of agreement between WFO and multidisciplinary team (MDT) for lung cancer. From January to December 2018, newly diagnosed lung cancer cases in Chonnam National University Hwasun Hospital were retrospectively examined using WFO version 18.4 according to four treatment categories (surgery, radiotherapy, chemoradiotherapy, and palliative care). Treatment recommendations were considered concordant if the MDT recommendations were designated 'recommended' by WFO. Concordance between MDT and WFO was analyzed by Cohen's kappa value.
Results: In total, 405 (male 340, female 65) cases with different histology (adenocarcinoma 157, squamous cell carcinoma 132, small cell carcinoma 94, others 22 cases) were enrolled. Concordance between MDT and WFO occurred in 92.4% (k=0.881, P<0.001) of all cases, and concordance differed according to clinical stages. The strength of agreement was very good in stage IV non-small cell lung carcinoma (NSCLC) (100%, k=1.000) and extensive disease small cell lung carcinoma (SCLC) (100%, k=1.000). In stage I NSCLC, the agreement strength was good (92.4%, k=0.855). The concordance was moderate in stage III NSCLC (80.8%, k=0.622) and relatively low in stage II NSCLC (83.3%, k=0.556) and limited disease SCLC (84.6%, k=0.435). There were discordant cases in surgery (7/57, 12.3%), radiotherapy (2/12, 16.7%), and chemoradiotherapy (15/129, 11.6%), but no discordance in metastatic disease patients.
Conclusions: Treatment recommendations made by WFO and MDT were highly concordant for lung cancer cases especially in metastatic stage. However, WFO was just an assisting tool in stage I-III NSCLC and limited disease SCLC; so, patient-doctor relationship and shared decision making may be more important in this stage.
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http://dx.doi.org/10.21037/tlcr.2020.04.11 | DOI Listing |
JAMA Intern Med
January 2025
Stanford Prevention Research Center, Department of Medicine, School of Medicine, Stanford University, Palo Alto, California.
JAMA Intern Med
January 2025
Department of Behavioral Science, The University of Texas MD Anderson Cancer Center, Houston.
Importance: The optimal configuration of a smoking cessation intervention in a lung cancer screening (LCS) setting has not yet been established.
Objective: To evaluate the efficacy of 3 tobacco treatment strategies of increasing integration and intensity in the LCS setting.
Design, Setting, And Participants: In this randomized clinical trial, LCS-eligible current smokers were randomized into 3 treatments: quitline (QL), QL plus (QL+), or integrated care (IC).
Int J Clin Pharm
January 2025
Center for Health Policy and Technology Evaluation, Peking University Health Science Center, Beijing, 100191, China.
Background: Lung cancer is the leading cause of cancer-related deaths in China, and pembrolizumab shows differential efficacy in advanced non-small cell lung cancer (NSCLC) with different PD-L1 expression levels.
Aim: To assess the cost-effectiveness of PD-L1 testing associated with pembrolizumab for first-line treatment of NSCLC from the perspective of Chinese healthcare system.
Method: Over a lifetime horizon, a three-state partitioned survival model was developed to assess the cost-effectiveness of PD-L1 testing and no PD-L1 testing.
Cancer Cytopathol
February 2025
Department of Pathology, Stanford University School of Medicine, Stanford, California, USA.
Background: Fumarate hydratase-deficient renal cell carcinoma (FHRCC) is an aggressive carcinoma that typically presents as advanced-stage disease. Prompt recognition of FHRCC is critical for appropriate clinical care and genetic counseling for patients and family members. However, diagnosing FHRCC from cytology specimens is challenging, with limited characterization and no reports describing prospectively identified cases.
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