Background IBM Watson for Oncology (WFO) is an artificial intelligence cognitive computing system that provides confidence-ranked, evidence-based treatment recommendations for cancer. We examine the level of agreement for breast cancer chemotherapy between WFO recommended and clinical use in a large population of breast cancer cases. Methods A total of 1,301 breast cancer patients were reviewed in The First Affiliated Hospital with Nanjing Medical University, China from June 2013 to December 2017. Patients’ data were entered manually into WFO by the trained senior oncology fellows. Chemotherapy recommendations were provided in 3 categories, “Recommended”, “For Consideration”, and “Not Recommended”. Concordance was achieved when oncologists’ treatment suggestions were in the “Recommended” or “For Consideration” categories. Results The chemotherapy regimen concordance was 69.4% among all breast cancer cases, 65.0% among the cases in adjuvant chemotherapy (AC) group and 96.7% among the cases in neoadjuvant chemotherapy (NAC) group. The concordance varied greatly in subset analysis with respect to TNM stage and molecular subtype. AC recommendations were concordant in 92.3% of stage III breast cancer and 50.8% of stage I. However, the concordance varied by molecular subtype, which was higher for triple negative breast cancer (89.3%) than others. The chemotherapy regimen concordance declined significantly with increasing age, except for the age group 41–50 years. Conclusions Chemotherapy regimens provided by WFO did not exhibit a high degree of agreement with those suggested by oncologists in clinical practice in the hospital in China. The current effort is underway to enhance WFO’s capabilities as a cognitive decision support tool by incorporating regional guidelines, enabling oncologists and patients to benefit from WFO worldwide.
Purpose Chemotherapy is a comprehensive therapy for breast cancer; nevertheless, its associated adverse effects are drawing increasing attention with the continuous improvement of the efficacy. The changes in serum lipids of breast cancer patients caused by chemotherapy have been reported by previous studies, whereby the former increase the incidence rate of cardiovascular disorders. However, the variations in the changes of serum lipids with different chemotherapy regimens have seldom been reported. Methods From January 2011 to December 2017, 1740 breast cancer patients treated with chemotherapy were recruited at the First Affiliated Hospital of Nanjing Medical University. The chemotherapy regimens included anthracycline-based, taxane-based, and anthracycline-plus-taxane-based regimens, dose-dense and standard-interval regimens. Lipid profiles that contained TG (triglyceride), TC (total cholesterol), HDL-C (high-density lipoprotein cholesterol), LDL-C (low-density lipoprotein cholesterol) and Lpa (lipoprotein a) levels were collected prior to the first, second and last cycles of chemotherapy. The changes of serum lipids with the same or different chemotherapy regimens were analyzed and compared. Results It was observed that the levels of TG, TC, LDL-C and Lpa increased significantly while that of HDL-C decreased after adjuvant chemotherapy in breast cancer patients (P<0.05). Besides, dose-dense regimens had more influence in TG and HDL-C and less influence in TC and LDL-C than standard-interval regimens. HDL-C was more sensitive to anthracycline-based regimens than taxane-based regimens. The level of TG with anthracycline-plus-taxane-based regimens was higher than that with only anthracycline-based or taxane-based regimens, and the level of HDL-C with anthracycline-plus-taxane-based regimen showed lower than that with taxane-based regimen. Conclusion In summary, this study proposed that dyslipidemia was strongly associated with chemotherapy in Chinese breast cancer patients after operative treatment. Furthermore, the changes in levels of serum lipids varied among patients with different chemotherapy regimens and taxane had less effect on dyslipidemia than anthracycline.
Background: Thyroid cancer is a common endocrine tumor, the incidence of which is increasing each year.Early diagnosis and treatment can effectively prevent thyroid cancer. This article uses Chinese's ultrasound reports to determine the value of early diagnosis. Methods:The clinical data center of the First Affiliated Hospital of Nanjing Medical University was screened for patients diagnosed with a thyroid nodule, who had undergone a thyroid function test, ultrasound records and pathological assessment. A total of 811 patients with a total of 1,290 pathologically confirmed nodules (506 benign and 784 malignant) were enrolled. Logistic regression was used to analyze the variables that significantly affected malignant nodules. The sensitivity and specificity of ultrasound thyroid imagingreporting and data system (TI-RADS) classification results for benign and malignant tumors were calculated.Results: The age of the patients had a very significant difference in the classification of benign and malignant nodules (P<0.001), and the marital status was significantly different (P<0.05). Gender and medical insurance had no significant effect (P>0.05). Thyroglobulin (TG), free thyroxine (FT4), and free triiodothyronine (FT3) had significant effects (P=0.003) on the incidence of malignant nodules in patients, while thyroid-stimulating hormone (TSH) had no significant effect (P>0.05). Ultrasound analysis showed a Youden's index of 78.97%, a positive predictive value of 93.20%, and a negative predicted value of 84.10% at the most excellent classification effect. The sensitivity was 89.0%, the specificity was 89.9%; much greater than the classification model based on the thyroid function test (sensitivity =80.6%, specificity =55.8%). Conclusions:The present study verifies the effectiveness of using TI-RADS classification for diagnosis of benign and malignant thyroid nodules, and explores the use of new analysis methods for clinical data. To reduce dependence on the doctors, ultrasound image data and clinical phenotypic data can be further used to assist clinical decision making.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.