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

Background: Breast cancer oncologists are challenged to personalize care with rapidly changing scientific evidence, drug approvals, and treatment guidelines. Artificial intelligence (AI) clinical decision-support systems (CDSSs) have the potential to help address this challenge. We report here the results of examining the level of agreement (concordance) between treatment recommendations made by the AI CDSS Watson for Oncology (WFO) and a multidisciplinary tumor board for breast cancer. Patients and methods: Treatment recommendations were provided for 638 breast cancers between 2014 and 2016 at the Manipal Comprehensive Cancer Center, Bengaluru, India. WFO provided treatment recommendations for the identical cases in 2016. A blinded second review was carried out by the center's tumor board in 2016 for all cases in which there was not agreement, to account for treatments and guidelines not available before 2016. Treatment recommendations were considered concordant if the tumor board recommendations were designated 'recommended' or 'for consideration' by WFO. Results: Treatment concordance between WFO and the multidisciplinary tumor board occurred in 93% of breast cancer cases. Subgroup analysis found that patients with stage I or IV disease were less likely to be concordant than patients with stage II or III disease. Increasing age was found to have a major impact on concordance. Concordance declined significantly (P ≤ 0.02; P < 0.001) in all age groups compared with patients <45 years of age, except for the age group 55-64 years. Receptor status was not found to affect concordance. Conclusion: Treatment recommendations made by WFO and the tumor board were highly concordant for breast cancer cases examined. Breast cancer stage and patient age had significant influence on concordance, while receptor status alone did not. This study demonstrates that the AI clinical decision-support system WFO may be a helpful tool for breast cancer treatment decision making, especially at centers where expert breast cancer resources are limited.

Spectrum of systemic bacterial infections during febrile neutropenia in pediatric oncology patients in tertiary care pediatric center.

BACKGROUND: Outcome of pediatric cancers has significantly improved with modern chemotherapy and good supportive care. However, febrile neutropenia remains one of the important limiting factors in these patients especially with the emergence of resistant organisms. Choosing appropriate antimicrobials is possible only if we understand the local microbial spectrum and their sensitivity pattern. AIMS: To study the likely etiologic agents and their antibiotic sensitivity pattern among systemic infections in children with cancer. SETTINGS AND DESIGN: This is a prospective study. MATERIALS AND METHODS: The study was conducted at a tertiary care center for pediatrics, in which culture samples representing blood stream infections and others like urinary tract infections sent from the Oncology services of the Hospital during the year of 2013 were analyzed. The microbiological profile and antibiotic sensitivity pattern of these isolates were studied. RESULTS: There were 89 isolates that represented blood and urinary tract infections in neutropenic patients with cancer.Out of 89 positive cultures 76 were gram negative isolates. The most common gram negative bacterial isolates were Escherichia coli 33 (37%), followed by Pseudomonas 21 (23.5%). Acinetobacter grew in 2 patients (2.2%). Extended spectrum beta-lactamases (ESBL's), carbepenem resistant and pan-resistant organisms seen in 28 (31.4%), 5 (5.6%) and 2 cases (2.3%) respectively. Over all Gram-positive organisms were 13/89 (12.3%). Staphylococcus was the most common Gram-positive organism and methicillin resistant Staphylococcus aureus seen in 5 each. CONCLUSION: Gram-negative organism is a common isolate in cancer children with febrile neutropenia, which is resistant to first-line antibiotic cefepime. Meropenem is most sensitive antibiotic and ESBL's are sensitive to piperacillin-tazobactam.