Evidence map›Paper›PMID 32676314›Full record

ArticleTranslational lung cancer research2020

Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board.

Min-Seok Kim, Ha-Young Park, Bo-Gun Kho, Cheol-Kyu Park, In-Jae Oh, Young-Chul Kim, Seok Kim, Ju-Sik Yun, Sang-Yun Song, Kook-Joo Na and 14 more

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Translational lung cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 2 pooled it
3.6field-weighted citation impact, top 5% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed, 2 syntheses or guidelines pooled it, 59 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

24 authors at 3 institutions in 1 country.

Min-Seok KimLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Ha-Young ParkLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Bo-Gun KhoLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Cheol-Kyu ParkLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
In-Jae OhLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Young-Chul KimLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Seok KimLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Ju-Sik YunLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Sang-Yun SongLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Kook-Joo NaLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Jae-Uk JeongLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Mee Sun YoonLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Sung-Ja AhnLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Su Woong YooLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Sae-Ryung KangLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Seong Young KwonLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Hee-Seung BomLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Woo-Youl JangLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
In-Young KimLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Jong-Eun LeeDepartment of Radiology, Chonnam National University Medical School, Gwangju, Republic of Korea.
Won-Gi JeongLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Yun-Hyeon KimDepartment of Radiology, Chonnam National University Medical School, Gwangju, Republic of Korea.
Taebum LeeDepartment of Pathology, Chonnam National University Medical School, Gwangju, Republic of Korea.
Yoo-Duk ChoiLung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Chonnam National University Hwasun Hospital · KRChonnam National University · KRChonnam National University Hospital · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIBM 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.

methodsWe 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.

resultsIn 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.

conclusionsTreatment 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.

Indexed as

lung cancermultidisciplinary tumor boardWatson for Oncology (WFO)

Identifiers

PMID32676314
PMCPMC7354125
OpenAlexW3023412939

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.