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  • 1
    In: The Lancet, Elsevier BV, Vol. 401, No. 10387 ( 2023-05), p. 1499-1507
    Type of Medium: Online Resource
    ISSN: 0140-6736
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    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
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    detail.hit.zdb_id: 3306-6
    detail.hit.zdb_id: 1476593-7
    SSG: 5,21
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  • 2
    Online Resource
    Online Resource
    American Society of Clinical Oncology (ASCO) ; 2019
    In:  Journal of Clinical Oncology Vol. 37, No. 15_suppl ( 2019-05-20), p. e18067-e18067
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 37, No. 15_suppl ( 2019-05-20), p. e18067-e18067
    Abstract: e18067 Background: Factors affecting cancer treatment may include evidence for effectiveness, cost, and preference. These influences can lead to treatment variation across institutions and populations. Decision-support systems have been proposed as tools to reduce variation. This study quantified concordance between treatment provided by oncologists in China and therapeutic options presented by a decision-support tool. Methods: We identified and analyzed concordance studies in nine unique institutions located in seven provinces in China, published in 2017-2018 using Watson for Oncology (WFO), a clinical decision-support tool. Published rates of concordance were compared by cancer type and institution. Results: Concordance of all combined cases was 59% (2012/3388). Concordance rates varied by cancer type and institution (Table). Concordance rates were highest for ovarian (96%), rectal (94%) and breast (89%) cancers but lowest in gastric (12%), ovarian (43%) and breast (55%) cancers. Conclusions: Concordance between treatments and therapeutic options from an oncology decision-support tool varied significantly across cancer types and institutions in China, suggesting significant practice variation. Without established guidelines for treatment, clinical decisions may be influenced by preferences and local factors. Future studies are needed to identify reasons for variation and improve adherence to regional evidence-based guidelines. [Table: see text]
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2019
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  • 3
    Online Resource
    Online Resource
    American Society of Clinical Oncology (ASCO) ; 2023
    In:  Journal of Clinical Oncology Vol. 41, No. 16_suppl ( 2023-06-01), p. 1520-1520
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 41, No. 16_suppl ( 2023-06-01), p. 1520-1520
    Abstract: 1520 Background: The growing cancer burden in Africa is a major public health challenge. There were 8,944 new cases in Rwanda, with over 6,044 deaths reported in 2020. Unfortunately, there are only 13 oncologists in Rwanda for a population of 13 million people. The use of artificial intelligence (AI) tools and digital health in cancer treatment has the potential to mitigate the oncology workforce shortages, support clinical decision-making, and increase access to care. Hurone AI is a Seattle MedTech startup building culturally sensitive AI applications to address the oncologist-patient gaps and improve drug safety in underserved populations. Hurone’s premier application, Gukiza, launched at the Rwanda Cancer Center in 2022 to conduct beta testing. Gukiza is a remote patient monitoring system that ensures patients can report side effects or symptoms from their phones and oncology care teams can get real-time treatment insights and provide timely and effective care. Methods: Hurone received ethical approval from the Rwanda National Ethical Committee to launch a pilot in September 2022. An initial 45 breast cancer patients aged 20 – 65 who were either newly diagnosed or on active treatment were recruited. Each patient periodically received prompts that asked them about side effects and to score the degree of severity. The questions were adapted from the NCI’s adverse events repository. Gukiza analyzed each response and presented a visual analysis of each patient, enabling the cancer care team to send text-based interventions to the patient’s phone. Potential emergencies are flagged and sent emergency numbers to call. Through Amazon’s cloud analytic tools, Gukiza provides treatment insights to support oncologists' clinical decisions. Results: Results are summarized. Conclusions: Resource-appropriate digital technologies can be a useful tool in mitigating adverse events during cancer treatment and increasing access to timely care for patients. The data built in such systems can be a useful resource to individualize and improve cancer care and treatment outcomes.[Table: see text] [Table: see text]
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    RVK:
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2023
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  • 4
    Online Resource
    Online Resource
    American Society of Clinical Oncology (ASCO) ; 2018
    In:  Journal of Clinical Oncology Vol. 36, No. 30_suppl ( 2018-10-20), p. 67-67
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 36, No. 30_suppl ( 2018-10-20), p. 67-67
    Abstract: 67 Background: Recent advances in artificial intelligence (AI) carry underexplored practical and ethical implications for the practice of clinical oncology. As oncologic applications of AI proliferate, a framework for guiding their ethical implementations and equitable distribution will be crucial. Methods: We reviewed the current landscape of AI applications in oncology research and clinical practice by reviewing the current body of evidence in PubMed and Medline. Key ethical challenges and opportunities to address health equity are critically evaluated and highlighted. Ethical implications for patients, clinicians and society at large are delineated, with particular focus on the impact and ramifications of AI with respect to healthcare disparities and equity of oncology care delivery. Results: Growing concerns that AI may widen disparities in oncologic care by virtue of lack of affordability, inconsistent accessibility and biased machine-learning models are addressed. Although there is potential for AI to widen disparities in oncology care, using foresight in application, AI has the potential to (1) democratize access to specialized clinical knowledge, (2) improve the accuracy of predicting cancer susceptibility, recurrence and mortality, (3) prevent diagnostic errors in under-resourced settings, (4) minimize unintended bias and (5) enable access to tailored therapeutic options including clinical trials if appropriately deployed. Separately, AI can be harnessed to identify areas of underserved needs and optimize systems of health-information sharing and reimbursements as blockchain technology converges with AI. As AI advances it will have a larger presence in oncology research and clinical practice. Conclusions: A strategic framework integrating ethical standards and emphasizing equitable implementation can help ensure that the potential of AI to address disparities in oncology are maximally captured and its perils averted. Further work is being done on exploring these challenges and will be submitted as a manuscript.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    RVK:
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2018
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  • 5
    Online Resource
    Online Resource
    American Society of Clinical Oncology (ASCO) ; 2020
    In:  Journal of Clinical Oncology Vol. 38, No. 29_suppl ( 2020-10-10), p. 124-124
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 38, No. 29_suppl ( 2020-10-10), p. 124-124
    Abstract: 124 Background: In the US, the incidence of colorectal cancer (CRC) is increasing in patients younger than 50 years who may present with advanced stage, high grade, left-sided colon or rectal cancers with signet ring cell histopathology, aggressive clinical course, and reduced overall survival. Understanding the characteristics of this population could inform screening, early detection, and optimal treatment. In this study, we describe the attributes of adults who are 50 years and younger with a first diagnosis of CRC and ascertain molecular testing rates and time to surgery by using data from a commercially insured cohort in the U.S. Methods: This retrospective study of patients ages 50 and younger with a first diagnosis of CRC utilizes the IBM MarketScan database, and focuses on claims from January 2013 to December 2018. Included patients had continuous insurance enrollment of 12 months before and 6 months after diagnosis. We determined rates of tumor testing for microsatellite instability (MSI) or immunohistochemistry (IHC) for mismatch repair (MMR) proteins and referral to genetic services in all patients, as well as mutational analysis of KRAS, NRAS, and BRAF in metastatic CRC patients. Time to surgical resection of primary tumor (TTS) in non-metastatic colon cancer patients was measured. Results: During the 5-year period, 10,577 patients ages 18 to 50 years had a first diagnosis of CRC, which was 15.6% of the 67,921 adults of all ages with CRC. Claims for MSI or IHC for MMR proteins within 120 days of initial diagnosis were done in 4,429 (41.9%) patients and referral to genetics services/counseling within 1 year of initial diagnosis were done in 443 (4.1%) patients. Among metastatic CRC patients, KRAS, NRAS, or BRAF tumor mutational analyses within 120 days of initial diagnosis were documented in 323 (31.5%). The median TTS ranged from 7 to 15 days with no statistically significant differences based on geographic region or health insurance plan type. Conclusions: Younger patients with early onset CRC had low rates of referral to genetics services, tumor MSI or IHC for MMR proteins testing, and KRAS, NRAS, and BRAF mutational analysis. There were no geographic or insurance type trends in TTS in non-metastatic colon cancer patients. Although underreporting is possible in our study, the findings of low utilization of genetic services and tumor genomic testing in these younger patients with early onset CRC should alert the oncology community to critical management gaps in the care of this population.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2020
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  • 6
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 35, No. 15_suppl ( 2017-05-20), p. 6501-6501
    Abstract: 6501 Background: IBM Watson for Clinical Trial Matching (CTM) is a cognitive computing solution that uses natural language processing (NLP) to help increase the efficiency and accuracy of the clinical trial matching process. This solution helps providers locate suitable protocols for their patients by reading the trial criteria and matching it to the structured and unstructured patient characteristics when integrated with the Electronic Medical Record (EMR). It is also designed to determine which sites have the most viable patient population and identify inclusion and exclusion criteria that limit enrollment. Methods: This project was a collaboration among Highlands Oncology Group (HOG), Novartis and IBM Watson Health to explore the use of CTM in a community oncology practice. HOG is in Northeast Arkansas and has 15 physicians and 310 staff members working across 3 sites. During the 16-week pilot period, data from 2,620 visits by lung and breast cancer patients were processed by the CTM system. Using NLP capabilities, CTM read the clinical trial protocols provided by Novartis, and evaluated the patient data against the protocols’ inclusion and exclusion criteria. Watson excluded ineligible patients, determined those that needed further screening, and assisted in that process. Feedback on the user experience was also obtained. Results: In an initial pre-screening test, the HOG clinical trial coordinator (CTC) took 1 hour and 50 minutes to process 90 patients against 3 breast cancer protocols. Conversely, when the CTM screening solution was used, it took 24 minutes. This represents a significant reduction in time of 86 minutes or 78%. Watson excluded 94% of the patients automatically, providing criteria level evidence regarding the reason for exclusion, thus reducing the screening workload dramatically. Conclusions: IBM Watson CTM can help expedite the screening of patient charts for clinical trial eligibility and therefore may also help determine the feasibility of protocols to optimize site selection and enable higher and more efficient trial accruals.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2017
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  • 7
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 35, No. 15_suppl ( 2017-05-20), p. e18204-e18204
    Abstract: e18204 Background: IBM Watson for Oncology (WFO) is a Memorial Sloan Kettering-trained cognitive computing system that provides oncologists with evidence-based treatment options for cancer. Treatments are presented in three categories: “Recommended”, “For Consideration” and “Not Recommended”. We examined the concordance of treatment options between WFO and the tumor board from Gachon University Gil Medical Centre (GMC), Incheon, South Korea. GMC is an urban center that cares for 50,000 cancer patients annually. Methods: We enrolled 340 patients with stage II, III and IV colon cancer and 185 with chemotherapy-naïve advanced gastric cancer, all treated between 2012 and 2016. Cases were processed using WFO, and the output was compared to blinded tumor board recommendations. Treatment options were considered concordant when the GMC recommendation was included in the “Recommended” or “For Consideration” categories. Results: Treatment recommendations were concordant in 248 (73%) of the 340 evaluated colon cancer cases. Of 250 patients treated in the adjuvant setting, 212 (85%) were concordant. Of 90 patients with metastatic disease, 36 (40%) were concordant. Treatment recommendations were concordant in 90 (49%) of 185 chemotherapy-naïve gastric cancer patients. Low concordance rates in gastric cancer were explained by two observations: (1) The trastzumab/FOLFOX regimen is not covered by the Korean National Health Insurance System, and (2) A regimen known as S-1 (tegafur, gimeracil, and oteracil) plus cisplatin is routinely used in Korea and is not used in the U.S. Conclusions: Treatment options suggested by WFO were concordant with the therapeutic decisions of GMC in the large majority of colon cancer patients treated in the adjuvant setting. Lower degrees of concordance were seen in patients with metastatic colon and gastric cancer, reflecting differences in practice patterns between the United States, where WFO was trained, and GMC, in Korea. Geography-specific customization is available in WFO and should enable physicians and patients to benefit from WFO worldwide. WFO's ability to learn from gastric cancer cases in a part of the world with increased incidence may reveal insights that are applicable elsewhere.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2017
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  • 8
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 38, No. 15_suppl ( 2020-05-20), p. e14070-e14070
    Abstract: e14070 Background: Watson for Oncology (WfO) is an artificial intelligence-based clinical decision-support system that offers potential therapeutic options to cancer-treating physicians. We reviewed studies of concordance between therapeutic options offered by WfO and treatment decisions made by individual clinicians (IC) and multidisciplinary tumor boards (MTB) in practice in gynecological cancers. Methods: We searched PubMed and an internal database to identify peer-reviewed WfO concordance studies of gynecological cancers published between 01/01/2015 and 06/30/2019. Concordance was defined as agreement between therapeutic options recommended or offered for consideration by WfO and treatment decisions made by IC or MTB. Mean concordance was calculated as a weighted average based on the number of patients per study. Statistical significance was evaluated by z-test of two proportions. Results: Our search identified 5 retrospective studies with 635 patients with cervical and ovarian cancers in China and Thailand; 4 compared WfO to MTB and 1 to IC. Overall WfO concordance with MTB and IC for both cancers was 77.2% (SD 11.6%). The concordance between MTB and WfO in cervical and ovarian cancers was 80.5% and 86.2%, respectively ( P = .21); IC concordance with WfO in cervical and ovarian cancers was 65.2% and 73.2%, respectively ( P = .18). MTB concordance with WfO for both cancers combined was 81.5%, significantly higher than the 67.9% IC concordance with WfO for both cancers ( P = .01). Conclusions: Studies of cervical and ovarian cancers demonstrated a statistically significantly higher concordance of MTB and WfO than IC and WFO, suggesting a role for WfO in supporting treatment-decision making in gynecological cancers that aligns with decisions made by MTB. Larger prospective studies are needed to evaluate the technical performance, usability, workflow integration, and clinical impact of WfO in gynecological cancers.[Table: see text]
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    RVK:
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2020
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  • 9
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 38, No. 15_suppl ( 2020-05-20), p. e14114-e14114
    Abstract: e14114 Background: Watson for Oncology (WfO) is an artificial intelligence-based clinical decision-support system which provides therapeutic options and associated scientific evidence to cancer-treating physicians. Oncologists at Bumrungrad International Hospital (BIH) have used WfO since 2015. We examined the association between concordance of WfO therapeutic options and BIH treatment decisions with short-term clinical outcomes for lung cancer patients. Methods: This study included lung cancer patients seen at BIH for treatment and follow-up care and for whom WfO was used from 2015 to 2018. Charts were reviewed for concordance with WfO, documentation of disease progression, response to treatment, and survival. We evaluated concordance between oncologists’ treatments and therapeutic options listed as “recommended” by WfO. We evaluated association between WfO concordance and partial or complete response rates over a 24-month period by comparison of proportions with odds ratio. Progression-free survival (PFS, time from diagnosis until progression or death) was evaluated by Kaplan-Meier log-rank test. Results: Seventy-nine lung cancer patients were included. We identified a trend towards higher response rates in concordant cases (59.2%, N = 32), as compared to discordant (48.0%, N = 12), with an odds ratio of 1.56 (see table). There was not a significant difference in PFS between concordant and discordant cohorts. Conclusions: In this small-cohort, retrospective study, lung cancer patients receiving treatments that are concordant with WfO recommended therapeutic options trended towards higher response rates than patients with discordant treatments. Use of a clinical decision-support system may help support cancer-treating physicians in delivering best practice and evidence-based care that may improve short-term outcomes. Prospective studies with larger samples and other cancer types are underway. [Table: see text]
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2020
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  • 10
    Online Resource
    Online Resource
    American Society of Clinical Oncology (ASCO) ; 2020
    In:  Journal of Clinical Oncology Vol. 38, No. 15_suppl ( 2020-05-20), p. 534-534
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 38, No. 15_suppl ( 2020-05-20), p. 534-534
    Abstract: 534 Background: Adjuvant treatment after breast conserving surgery (BCS) has been shown to improve outcomes, but the degree of uptake varies considerably. We sought to examine factors associated with post-BCS receipt of and time to treatment (TTT) for adjuvant radiation therapy (ART), cytotoxic chemotherapy (ACT) and endocrine therapy (AET) among women with breast cancer. Methods: IBM MarketScan claims data were used to select women diagnosed with non-metastatic invasive breast cancer from 01/01/2012 to 03/31/2018, who received primary BCS without any neoadjuvant therapy, and who had continuous insurance eligibility 60 days post-BCS. Logistic and quantile regressions were used to identify factors associated with receipt of adjuvant therapy (ART, ACT, AET) and median TTT in days for ART (rTTT), ACT (cTTT), and AET (eTTT), respectively, after adjustment for covariates including age, year, region, insurance plan type, comorbidities, and a vector of ZIP3-level measures (e.g., community race/ethnicity-density, education level) from the 2019 Area Health Resource Files. Results: 36,270 patients were identified: 11,996 (33%) received ART only, 4,837 (13%) received ACT only, 3,458 (10 %) received AET only, 5,752 (16%) received both ART and AET, and 9,909 (27%) received no adjuvant therapy within 6 months of BCS. (318) 1% of patients received combinations of either ART, AET or ACT. Relative to having no adjuvant therapy, patients 〉 80 years were significantly less likely to receive ART only (relative risk ratio [RRR] 0.65), ACT only (RRR 0.05), or combination ART/AET (RRR 0.66) but more likely to receive AET alone (RRR 3.61) (all p 〈 .001). Patients from communities with high proportions of Black (RRR 0.14), Asian (RRR 0.13), or Hispanic (RRR 0.45) residents were significantly less likely to receive combination ART and AET (all p 〈 .001). Having HIV/AIDS (+11 days; p = .01) and residing in highly concentrated Black (+8.5 days; p = .01) and Asian (+12.2 days; p = .04) communities were associated with longer rTTT. Longer cTTT was associated with having comorbidities of cerebrovascular disease (+6.0 days; p 〈 .001), moderate to severe liver disease (+12.3 days; p 〈 .001) and residing in high-density Asian communities (+18.0 days; p 〈 .001). Shorter eTTT (-11.4 days; p = .06) and cTTT (-14.8 days; p 〈 .001) was observed in patients with comorbidities of dementia. Conclusions: Results from this cohort of privately insured patients demonstrate disparities in receipt of post-BCS adjuvant radiation and systemic therapy along multiple demographic dimensions and expose opportunities to promote timely receipt of care.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2020
    detail.hit.zdb_id: 2005181-5
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