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  • Srivastava, G  (7)
  • Medicine  (7)
  • 1
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2014
    In:  British Journal of Cancer Vol. 110, No. 10 ( 2014-5), p. 2405-2411
    In: British Journal of Cancer, Springer Science and Business Media LLC, Vol. 110, No. 10 ( 2014-5), p. 2405-2411
    Type of Medium: Online Resource
    ISSN: 0007-0920 , 1532-1827
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    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2014
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  • 2
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 79, No. 4_Supplement ( 2019-02-15), p. P5-14-07-P5-14-07
    Abstract: Background: Women with early breast cancer routinely face a choice between breast conservation therapy and mastectomy, and assume agency through shared decision making. However, for women with lower socioeconomic power or education, barriers such as access to understandable information, involvement of family in decision making, and a decreased sense of autonomy inhibits this agency. To better empower this population, a simple to understand, online, self-administered, conjoint analysis based decision aid called “Navya Patient Preference Tool” (PPT) is developed to be used outside the physician encounter. PPT is unique in its incorporation of several psychological scales that assess potential confounders of participation in shared decision making. Methodology: This is a pre-planned analysis of the reliability and validity of the psychological scales used in all three arms of an IRB approved randomized controlled trial to assess PPT. Women with operable node negative breast cancer eligible for BCT or MRM at one of Asia's largest academic tertiary cancer centers were eligible. PPT trial consists of an initial conjoint analysis questionnaire analyzing implicit preferences for breast conservation given to the intervention arms. The following psychological scales were given to all patients regardless of randomization: Autonomy Preference Index (API), Traditional-Egalitarian Gender Roles (TEGR), Caregiving Role, Brief Resiliency Scale (BRS), Appearances Scale, and Decisional Conflict Scale (DCS). Cronbach's alpha as a measure of internal reliability for all scales, and correlations of scores with known demographic trends as a measure of external validity are calculated. Results: Of the 102 patients enrolled, 30 completed PPT in English, 39 in Hindi, and 33 in Marathi, (vernaculars). 69/102 were in middle and lower socioeconomic groups (Kuppuswamy Index). 53/102 had completed less than high school education. Internal reliability of all scales were high, with Cronbach's alpha above 0.7: API 0.74, TEGR 0.78, Caregiving 0.7, BRS 0.7, Appearance 0.84. DCS was highly reliable at 0.91, and is the primary outcome measure for the RCT. Correlations in the dataset met those expected in real world data, suggesting external validity. For e.g., education was inversely correlated with traditional gender roles on TEGR (R -0.4, p & lt;0.01), and positively correlated with resilience on BRS (R 0.228, p & lt;0.05). Individual scale items that are unrealistic were not chosen by any of the 102 respondents (e.g.,. My doctor should not participate in my medical decisions), substantiating nuanced reading. 85% of patients “Strongly Agreed” on a 1-5 Likert scale that “The survey questions were easy to understand” (mean score 1.18/5. SD 0.4). Conclusions: Women with limited education and low socioeconomic status complete the online, self administered PPT outside of a physician encounter, with high internal reliability and external validity. Decision Aids such as Navya PPT, which account for psychosocial confounders of agency, have the potential to benefit women otherwise marginalized from shared decision making. Citation Format: Joshi S, Ramarajan L, Ramarajan N, Srivastava G, Begum F, Deshpande O, Tondare A, Nair N, Parmar V, Gupta S, Badwe RA. Accuracy of psychosocial assessments in an online surgical decision aid developed for early breast cancer patients with resource and educational constraints [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P5-14-07.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2019
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  • 3
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2017
    In:  Cancer Research Vol. 77, No. 4_Supplement ( 2017-02-15), p. P1-14-01-P1-14-01
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 77, No. 4_Supplement ( 2017-02-15), p. P1-14-01-P1-14-01
    Abstract: Introduction: Experts at tertiary care centers provide solutions to complex cases not addressed by high quality evidence. They intuitively retrieve patterns from years of experience to make treatment decisions. Short of personal consultations, there is no way to access this vast “experience database.” Experience Engine (XE) is a machine learning solution to structure experiential knowledge relevant for decision making, derive a similarity metric for patients who have received similar treatments, and predict treatment decisions that experts are likely to recommend. Methods: 277 patient histories relating to 743 breast cancer tumor board decisions at two tertiary care centers were abstracted as the training set for machine learning. 161 distinct histories relating to 496 decisions for a separate expert opinion service at one of the centers was the holdout test set. Data was structured into 690 features based on a novel ontology designed specifically for breast cancer decision making. To uncover nonlinear similarities, (for example, treatments for younger patients with multiple comorbidities and elderly patients may be similar), treatment decisions were grouped by timing and modality into 13 groups, such as primary surgery, 1st line palliative chemotherapy, etc. Similarity metric was derived using machine learning on the training set. The target for prediction was the specific treatment decision i.e. TAC or another adjuvant regimen. The primary endpoint was percent accuracy of agreement between XE's predicted decision and experts' actual decision in the holdout test set. Multiple similarity distance metrics including Bhattacharya, Eskin, Goodall, etc., and multiclass classification algorithms such as Extreme Gradient Boosted Trees, Support Vector Machines, etc., were systematically evaluated to arrive at the algorithms that best fit each treatment group. Results: The winning XE algorithms were 71% to 89% accurate for the various treatment groups, in predicting the actual treatment decisions recommended by the experts. The most frequent treatments recommended across all groups were standard evidence based therapies, as are often recommended by experts. For instance, when XE recommended standard adjuvant therapies for Her2- patients, it was 88% to 97% accurate. When XE recommended nonstandard therapies for the same treatment group, it was 72% to 90% accurate, related to larger number of nonstandard therapies within each treatment group and smaller samples of patients who underwent each type of nonstandard therapy. XE learned to weigh features relating to comorbidities and toxicities when recommending nonstandard therapies. Conclusion: Machine learning on a structured database of past treatment decisions made by experts, can yield a predicted treatment decision that an expert is likely to recommend for a new patient. By including complex decisions that consider toxicities and morbidities, a rich source of knowledge can be created. Despite the limited dataset, XE learned features that experts strongly consider when making decisions. XE has the potential to analyze variations in decision making at expert practices, assess when to recommend nonstandard therapies, and serve as a training tool for new oncologists to make expert grade treatment decisions. Citation Format: Ramarajan N, Gupta S, Perry P, Srivastava G, Kumbla A, Miller J, Feldman N, Nair N, Badwe RA. Building an experience engine to make cancer treatment decisions using machine learning [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-14-01.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2017
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  • 4
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 78, No. 4_Supplement ( 2018-02-15), p. P4-10-02-P4-10-02
    Abstract: Background: Most cancer patients in Low and Middle Income Countries (LMIC) cannot afford effective, expensive, evidence based therapies. Therefore, oncologists must tailor treatment plans to individual resource constraints. To support this, NCCN has created a Resource-Stratified Framework® (NCCN-RSF), which is an evidence-based four-tier prioritization scheme. Further, only a fraction of patients in LMIC have ready access to oncologists. In India, there are only ˜1600 oncologists for 1.8 million patients. To bridge this gap, Navya's clinical informatics based mobile ExpertApp combines learning from evidence, prior tumor board decisions, patient resource constraints, and quick review from TMC NCG oncologists to recommend tailored treatment plans to patients via an online expert opinion service. 11865 patients in 22 LMIC have reached out to receive an online expert opinion through Navya (ASCO 2017). This study maps Navya to NCCN-RSF as an evidence-based index for resource-sensitive treatment selection. Methods: All breast cancer patients who received an online expert opinion from TMC NCG Navya between July 1st 2014 and April 30th 2017 were included. Navya systematically gathered information on patient resource constraints (such as affordability for Trastuzumab). Navya recommendations (breast and nodal surgery, radiation site and fractionation, drug and dose density etc.) were mapped to NCCN-RSF resource tiers (Basic, Core, Enhanced, Parent guideline). Reasons were categorized for Navya recommendations not present in NCCN-RSF. Results: 616 patients (36.3% metastatic), mostly from India, received 1203 recommendations. At the specific treatment protocol level, 88.3% of Navya recommendations mapped with at least one NCCN-RSF resource tier (Table 1). 78.5% mapped to the Enhanced tier. Only 8.6% of recommendations mapped to Parent guidelines, and did not require tailoring for resource constraints. Fewer than 2% recommendations mapped to Core and none to Basic. 11.7% recommendations were not present in NCCN-RSF, for minor reasons such as substitution of a drug within the same class (35.8%) (e.g., Epirubicin for Adriamycin), dose dense protocols (14.3%) (e.g., 3 weekly Paclitaxel vs weekly Paclitaxel), and recommending Trastuzumab for less than a year for patients unable to afford year long therapy (14.3%), currently not included in NCCN-RSF. Table 1- Mapping Navya to NCCN RSFNCCN RSF TiersHIGH LEVEL: Multimodality treatment and sequencing (1203)INTERMEDIATE: Within modality treatment categories (1188)GRANULAR: Specific treatment protocols (1140)E.g.Neoadjuvant vs Adjuvant ChemoAnthracycline vs TaxaneHypofractionation vs Standard XRTAt least one Tier98.8%±0.696%±1.188.3%±2Enhanced94.4%±1.391%±1.778.5%±2.7Core1.9%±5.61.2%±5.71.2%±5.8Parent NCCN2.4%±5.63.8%±5.68.6%±5.5 Conclusion: Navya's treatment recommendations are sensitive to resource constraints and map to peer reviewed and evidence based NCCN RSF, primarily at the Enhanced tier. Navya's clinical informatics based online service scales access to resource constrained treatment selection for large numbers of patients in LMIC without easy access to oncologists. Citation Format: Badwe RA, Gupta S, Feldman N, Pramesh CS, Ramarajan N, Srivastava G, Nair N, Anderson BO. Validation of a clinical informatics system for online multidisciplinary expert opinions: Mapping treatment recommendations to the NCCN resource-Stratified framework [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-10-02.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2018
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    detail.hit.zdb_id: 410466-3
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  • 5
    In: Oncogene, Springer Science and Business Media LLC, Vol. 30, No. 16 ( 2011-04-21), p. 1923-1935
    Type of Medium: Online Resource
    ISSN: 0950-9232 , 1476-5594
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    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2011
    detail.hit.zdb_id: 2008404-3
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  • 6
    Online Resource
    Online Resource
    BMJ ; 1993
    In:  Journal of Clinical Pathology Vol. 46, No. 3 ( 1993-03-01), p. 204-207
    In: Journal of Clinical Pathology, BMJ, Vol. 46, No. 3 ( 1993-03-01), p. 204-207
    Type of Medium: Online Resource
    ISSN: 0021-9746
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    Language: English
    Publisher: BMJ
    Publication Date: 1993
    detail.hit.zdb_id: 2028928-5
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  • 7
    Online Resource
    Online Resource
    BMJ ; 1993
    In:  Journal of Clinical Pathology Vol. 46, No. 9 ( 1993-09-01), p. 849-851
    In: Journal of Clinical Pathology, BMJ, Vol. 46, No. 9 ( 1993-09-01), p. 849-851
    Type of Medium: Online Resource
    ISSN: 0021-9746
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    Language: English
    Publisher: BMJ
    Publication Date: 1993
    detail.hit.zdb_id: 2028928-5
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