48 research outputs found
sj-docx-1-cix-10.1177_11769351221136081 – Supplemental material for A Random Forest Genomic Classifier for Tumor Agnostic Prediction of Response to Anti-PD1 Immunotherapy
No full textSupplemental material, sj-docx-1-cix-10.1177_11769351221136081 for A Random Forest Genomic Classifier for Tumor Agnostic Prediction of Response to Anti-PD1 Immunotherapy by Emma Bigelow, Suchi Saria, Brian Piening, Brendan Curti, Alexa Dowdell, Roshanthi Weerasinghe, Carlo Bifulco, Walter Urba, Noam Finkelstein, Elana J Fertig, Alex Baras, Neeha Zaidi, Elizabeth Jaffee and Mark Yarchoan in Cancer Informatics</p
sj-xls-2-cix-10.1177_11769351221136081 – Supplemental material for A Random Forest Genomic Classifier for Tumor Agnostic Prediction of Response to Anti-PD1 Immunotherapy
No full textsj-xls-2-cix-10.1177_11769351221136081 for A Random Forest Genomic Classifier for Tumor Agnostic Prediction of Response to Anti-PD1 Immunotherapy by Emma Bigelow, Suchi Saria, Brian Piening, Brendan Curti, Alexa Dowdell, Roshanthi Weerasinghe, Carlo Bifulco, Walter Urba, Noam Finkelstein, Elana J Fertig, Alex Baras, Neeha Zaidi, Elizabeth Jaffee and Mark Yarchoan in Cancer Informatics</p
Actualizing Personalized Healthcare for Women through Connected Data Systems: Breast Cancer Screening and Diagnosis
No full textMammography Screening in a Large Health System Following the U.S. Preventive Services Task Force Recommendations and the Affordable Care Act.
No full textPractice recommendations for mammography screening were issued by the U.S. Preventive Services Task Force in 2009 and expansion of insurance coverage was provided under the Patient Protection and Affordable Care Act soon thereafter, yet the influence of these changes on screening practices in the United States is not known.To determine changes in mammography screening and their associations with new practice recommendations and the Affordable Care Act, we examined patient-level data from 249,803 screening mammograms from January 1, 2008 through December 31, 2012 in a large community-based health system in the northwestern United States. Associations were determined by an intervention analysis of time-series data method.Among women screened, 64% were age 50-74 years; 84% self-identified as white race; 62% had commercial insurance; and 70% were seen in facilities located in metropolitan areas. Practice recommendations were associated with decreased screening volumes among women age <40 (-37.4 mammograms/month; -39.4% change; P<0.001), 40-49 (-106.0 mammograms/month; -11.2% change; P<0.001), and ≥75 (-54.7 mammograms/month; -10.0% change; P<0.001), but not women age 50-74. Implementation of the Affordable Care Act was associated with increased screening among women age 50-74 (+184.3 mammograms/month; +7.2% change; P=0.001), but not women <40 or ≥75; increases for age 40-49 were of borderline statistical significance (+56.9 mammograms/month; +6% change; P=0.06). Practice recommendations were also associated with decreased screening for women with commercial insurance, while the Affordable Care Act was associated with increased screening for women with Medicare, Medicaid, or other noncommercial sources of payment.Mammography screening volumes in a large community health system decreased among women age <50 and ≥75 in association with new U.S. Preventive Services Task Force practice recommendations, while insurance coverage changes under the Affordable Care Act were associated with increased screening volumes among women age 50-74
1296 Radiomics-based multi-modal prediction of treatment response to PD-1/PD-L1 immune checkpoint inhibitor (ICI) therapy in stage IV non-small cell lung carcinoma (mNSCLC)
No full textBackground Currently approved biomarkers that predict response to ICIs in mNSCLC are limited to PD-L1 expression levels by immunohistochemistry (IHC) and tumor mutation burden (TMB). However, the predictive performance of PD-L1 IHC and TMB are limited, and rates of testing are suboptimal. Radiomic biomarkers may offer an automated and scalable method to predict ICI response.1,2 We developed and validated multi-modal models predicting responses to ICIs in mNSCLC. In contrast to previously published models, our work focuses on generalizable models using a large multi-institutional “real-world” dataset and combines radiomics features with demographic, molecular, and laboratory values routinely available in patients’ electronic medical records [EMR]. Methods We analyzed radiomic characteristics of 6,028 primary and metastatic lesions from 1,169 mNSCLC patients treated with anti-PD-1/anti-PD-L1 ICIs from 8 institutions across the US and Europe. Data were randomly split into training (N=707 patients, n=3,625 lesions) and validation (N=462 patients, n=2,403 lesions) sets. Baseline and follow-up CT scans were manually annotated by board-certified radiologists using RECIST 1.1 criteria and all lesion volumes were manually segmented. We developed two predictive models using gradient-boosted decision tree algorithms, using 1) only manually curated baseline radiomic features quantifying textural heterogeneity and spicularity; and 2) a multi-modal model with radiomic features combined with known demographic, molecular (e.g. PD-L1 IHC), and laboratory (e.g. neutrophil-to-lymphocyte ratio) predictors of ICI response. Primary endpoints were 3- and 6-month radiological progression, defined by a 20% increase in lesion diameter. The primary evaluation metric was the area under the receiver operating characteristic curve (AUC). Models predicting response of lung lesions and lymph nodes were validated on two cohorts: ICI monotherapy and ICI plus concurrent chemotherapy. Patients with unavailable PD-L1 IHC, imaging follow-up, or oncogenic driver mutations were excluded from analysis. Results The radiomics model showed predictive accuracy comparable to tissue-based PD-L1 IHC for both endpoints and patient cohorts (tables 1, 2). However, the multi-modal model predicted lung and lymph node radiological progression with significantly higher AUC than PD-L1 IHC in all cohorts and endpoints, with 3- and 6-month progression AUCs of 0.86 (P=.00007) and 0.79 (P= .00001) in lung lesions and 0.78 (P=.003) and 0.80 (P=.002) in lymph nodes. Conclusions Radiomics-based multi-modal prediction of ICI response is feasible and accurate and may provide an opportunity for more personalized management, such as risk-based escalation/de-escalation of concurrent chemotherapy in mNSCLC patients. We will evaluate this methodology in prospective studies. References Trebeschi S, Drago S, Birkbak N, Kurilova I, Cǎlin A, Delli Pizzi A, Lalezari F, Lambregts D, Rohaan M, Parmar C, Rozeman E, Hartemink K, Swanton C, Haanen J, Blank C, Smit E, Beets-Tan R, Aerts H. Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Ann. Oncol. 2019; 30(6): 998–1004. Sun R, Limkin E, Vakalopoulou M, Dercle L, Champiat S, Han SR, Verlingue L, Brandao D, Lancia A, Ammari S, Hollebecque A, Scoazec J, Marabelle A, Massard C, Soria J, Robert C, Paragios N, Deutsch E, Ferté C. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol. 2018; 19(9): 1180–1191. Ethics Approval Ethics approval for US data: The study was conducted under IRB-approved procedures using de-identified data for patients diagnosed with Stage-IV NSCLC and treated between Jan. 1, 2017 and December 31, 2021. All records were de-identified per HIPAA guidelines at the institution level. Upon transfer, the data was quarantined and then re-inspected by authorized personnel prior to ingestion to ensure compliance and that no PHI was present in the records. Ethics approval for EU data: The study was conducted under IRB-approved procedures using de-identified data for patients diagnosed with Stage-IV NSCLC and treated between Jan. 1, 2017 and December 31, 2021. All records were de-identified per GDPR requirements at the institution level. The patients were also notified that their de-identified data would be part of a study and were given the required time and opportunity to respond if they had any objection. Upon transfer, the data was quarantined and then re-inspected by authorized personnel prior to processing to ensure compliance and that no PHI was present in the records. Consent N/
Screening Mammography Changes for Women 50–74 and ≥75.
No full text<p>The mean number of screening mammograms per month performed in the health system from 2008 through 2012. Arrows indicate the times of new screening recommendations and implementation of the Affordable Care Act. New recommendations were not associated with changes for women age 50–74, but were associated with decreased screening for ≥75, while the Affordable Care Act was associated with increased screening among women age 50–74 and no changes for ≥75.</p
Screening Mammography Changes for Women <40 and 40–49.
No full text<p>The mean number of screening mammograms per month performed in the health system from 2008 through 2012. Arrows indicate the times of new screening recommendations and implementation of the Affordable Care Act. Recommendations were associated with decreased screening for women <50, while the Affordable Care Act had no statistically significant associations.</p
Trends in Research Time, Fellowship Training, and Practice Patterns Among General Surgery Graduates
No full textCancer case trends following the onset of the COVID-19 pandemic: A community-based observational study with extended follow-up.
No full textBACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has impacted health care delivery worldwide. Cancer is a leading cause of death, and the impact of the pandemic on cancer diagnoses is an important public health concern.
METHODS: This cross-sectional study retrospectively analyzed the electronic medical records of 80,138 cancer patients diagnosed between January 1, 2019, and May 31, 2021. Outcome measures included weekly number of new cancer cases and trends in weekly cancer cases, before and after the pandemic; patient demographics; and positive COVID-19 test rates.
RESULTS: Beginning March 4, 2020, defined as the onset of the pandemic, weekly cancer cases declined precipitously (-110.0 cases per week [95% confidence interval, -190.2 to -29.8]) for 4 weeks, followed by a moderate recovery (+23.7 cases per week [9.1 to 38.4]) of 10 weeks duration. Thereafter, weekly cancer cases trended slowly back toward pre-COVID-19 baseline levels. Following the pandemic onset, there was a cumulative year-over-year decline in cancer cases overall of 7.3%, including a 20.2%, 14.3%, and 12.8% decline in nonmelanoma skin cancer, breast cancer, and prostate cancer, respectively. Changes in case volumes were accompanied by variations in patient characteristics, including region, age, gender, race, insurance coverage, and COVID-19 positive test rates (P \u3c .01 for all). Among patients tested for COVID-19, 5.3% had a positive result.
CONCLUSIONS: The data in this study demonstrate a substantial reduction in cancer diagnoses following the onset of COVID-19, which appear to reach expected pre-COVID norms 12 months later. The largest reduction was noted among cancers that are typically screen-detected or identified as part of a routine wellness examination
