Literature DB >> 30181315

Concordance Study Between IBM Watson for Oncology and Clinical Practice for Patients with Cancer in China.

Na Zhou1, Chuan-Tao Zhang1, Hong-Ying Lv1, Chen-Xing Hao1, Tian-Jun Li1, Jing-Juan Zhu1, Hua Zhu1, Man Jiang1, Ke-Wei Liu1, He-Lei Hou1, Dong Liu1, Ai-Qin Li2, Guo-Qing Zhang3, Zi-Bin Tian4, Xiao-Chun Zhang5.   

Abstract

BACKGROUND: IBM Watson for Oncology (WFO), which can use natural language processing to evaluate data in structured and unstructured formats, has begun to be used in China. It provides physicians with evidence-based treatment options and ranks them in three categories for treatment decision support. This study was designed to examine the concordance between the treatment recommendation proposed by WFO and actual clinical decisions by oncologists in our cancer center, which would reflect the differences of cancer treatment between China and the U.S. PATIENTS AND METHODS: Retrospective data from 362 patients with cancer were ingested into WFO from April 2017 to October 2017. WFO recommendations were provided in three categories: recommended, for consideration, and not recommended. Concordance was analyzed by comparing the treatment decisions proposed by WFO with those of the multidisciplinary tumor board. Concordance was achieved when the oncologists' treatment decisions were in the recommended or for consideration categories in WFO.
RESULTS: Ovarian cancer showed the highest concordance, which was 96%. Lung cancer and breast cancer obtained a concordance of slightly above 80%. The concordance of rectal cancer was 74%, whereas colon cancer and cervical cancer showed the same concordance of 64%. In particular, the concordance of gastric cancer was very low, only 12%, and 88% of cases were under physicians choice.
CONCLUSION: Different cancer types showed different concordances, and only gastric cancers were significantly less likely to be concordant. Incidence and pharmaceuticals may be the major cause of discordance. To be comprehensively and rapidly applied in China, WFO needs to accelerate localization. ClinicalTrials.gov Identifier: NCT03400514. IMPLICATIONS FOR PRACTICE: IBM Watson for Oncology (WFO) has begun to be used in China. In this study, concordance was examined between the treatment recommendation proposed by WFO and clinical decisions for 362 patients in our cancer center, which could reflect the differences of cancer treatment between China and the U.S. Different cancer types showed different concordances, and only gastric cancers were significantly less likely to be concordant. Incidence and pharmaceuticals may be the major causes of discordance. To be comprehensively and rapidly applied in China, WFO needs to accelerate localization. This study may have a significant effect on application of artificial intelligence systems in China. © AlphaMed Press 2018.

Entities:  

Keywords:  Artificial Intelligence; China; Concordance; Watson for Oncology

Year:  2018        PMID: 30181315      PMCID: PMC6656482          DOI: 10.1634/theoncologist.2018-0255

Source DB:  PubMed          Journal:  Oncologist        ISSN: 1083-7159


  5 in total

1.  Overview: Computational analysis and decision support systems in oncology.

Authors:  Fillia Makedon; Vangelis Karkaletsis; Ilias Maglogiannis
Journal:  Oncol Rep       Date:  2006       Impact factor: 3.906

Review 2.  Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment.

Authors:  Steven E Dilsizian; Eliot L Siegel
Journal:  Curr Cardiol Rep       Date:  2014-01       Impact factor: 2.931

3.  Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board.

Authors:  S P Somashekhar; M-J Sepúlveda; S Puglielli; A D Norden; E H Shortliffe; C Rohit Kumar; A Rauthan; N Arun Kumar; P Patil; K Rhee; Y Ramya
Journal:  Ann Oncol       Date:  2018-02-01       Impact factor: 32.976

4.  Docetaxel With Cyclophosphamide Is Associated With an Overall Survival Benefit Compared With Doxorubicin and Cyclophosphamide: 7-Year Follow-Up of US Oncology Research Trial 9735.

Authors:  Stephen Jones; Frankie Ann Holmes; Joyce O'Shaughnessy; Joanne L Blum; Svetislava J Vukelja; Kristi J McIntyre; John E Pippen; James H Bordelon; Robert L Kirby; John Sandbach; William J Hyman; Donald A Richards; Robert G Mennel; Kristi A Boehm; Wally G Meyer; Lina Asmar; Daniel Mackey; Stefan Riedel; Hyman Muss; Michael A Savin
Journal:  J Clin Oncol       Date:  2009-02-09       Impact factor: 44.544

5.  A new era of oncology through artificial intelligence.

Authors:  Alessandra Curioni-Fontecedro
Journal:  ESMO Open       Date:  2017-05-15
  5 in total
  22 in total

1.  Concordance Study in Hepatectomy Recommendations Between Watson for Oncology and Clinical Practice for Patients with Hepatocellular Carcinoma in China.

Authors:  Weiqi Zhang; Shuo Qi; Jiaming Zhuo; Sai Wen; Chihua Fang
Journal:  World J Surg       Date:  2020-06       Impact factor: 3.352

2.  Artificial Intelligence and Personalized Medicine.

Authors:  Nicholas J Schork
Journal:  Cancer Treat Res       Date:  2019

Review 3.  Can artificial intelligence overtake human intelligence on the bumpy road towards glioma therapy?

Authors:  Precilla S Daisy; T S Anitha
Journal:  Med Oncol       Date:  2021-04-03       Impact factor: 3.064

Review 4.  Artificial Intelligence in the Assessment of Female Reproductive Function Using Ultrasound: A Review.

Authors:  Zhiyi Chen; Ziyao Wang; Meng Du; Zhenyu Liu
Journal:  J Ultrasound Med       Date:  2021-09-15       Impact factor: 2.754

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

Authors:  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; Jae-Uk Jeong; Mee Sun Yoon; Sung-Ja Ahn; Su Woong Yoo; Sae-Ryung Kang; Seong Young Kwon; Hee-Seung Bom; Woo-Youl Jang; In-Young Kim; Jong-Eun Lee; Won-Gi Jeong; Yun-Hyeon Kim; Taebum Lee; Yoo-Duk Choi
Journal:  Transl Lung Cancer Res       Date:  2020-06

Review 6.  Artificial intelligence in dermatology and healthcare: An overview.

Authors:  Varadraj Vasant Pai; Rohini Bhat Pai
Journal:  Indian J Dermatol Venereol Leprol       Date:  2021 [SEASON]       Impact factor: 2.545

7.  Implementation of eHealth and AI integrated diagnostics with multidisciplinary digitized data: are we ready from an international perspective?

Authors:  Mark Bukowski; Robert Farkas; Oya Beyan; Lorna Moll; Horst Hahn; Fabian Kiessling; Thomas Schmitz-Rode
Journal:  Eur Radiol       Date:  2020-05-06       Impact factor: 5.315

8.  Differential impact of cognitive computing augmented by real world evidence on novice and expert oncologists.

Authors:  Donna M McNamara; Stuart L Goldberg; Lisa Latts; Deena M Atieh Graham; Stanley E Waintraub; Andrew D Norden; Cody Landstrom; Andrew L Pecora; John Hervey; Eric V Schultz; Ching-Kun Wang; Nicholas Jungbluth; Phillip M Francis; Jane L Snowdon
Journal:  Cancer Med       Date:  2019-09-11       Impact factor: 4.452

9.  Real world study for the concordance between IBM Watson for Oncology and clinical practice in advanced non-small cell lung cancer patients at a lung cancer center in China.

Authors:  Shuyang Yao; Ruotian Wang; Kun Qian; Yi Zhang
Journal:  Thorac Cancer       Date:  2020-03-19       Impact factor: 3.500

10.  The role of AI technology in prediction, diagnosis and treatment of colorectal cancer.

Authors:  Chaoran Yu; Ernest Johann Helwig
Journal:  Artif Intell Rev       Date:  2021-07-04       Impact factor: 8.139

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.