507 Surgical decision making in premenopausal brca carriers considering risk reducing early-salpingectomy or salpingo-oophorectomy: a qualitative study

AbstractDecision to undergo risk-reducing mastectomy (RRM) needs to consider several factors, including patient’s preference, surgeon’s preference, family history, and genetic predisposition. The aim of this study was to examine whether preoperative diagnosis of BRCA1/2 mutation status could influence surgical decision-making in newly diagnosed breast cancer patients. We retrospectively reviewed ipsilateral breast cancer patients with BRCA1/2 mutation who underwent primary surgery between January 2008 and November 2019 at a single institution in Korea. Of 344 eligible patients, 140 (40.7%) patients were aware of their mutation status ‘prior to surgery’, while 204 (59.3%) did not. Contralateral RRM rate was significantly higher in the group with BRCA1/2 mutation status identified ‘prior to surgery’ compared to the group with mutation status identified ‘after surgery’ [45.0% (63/140) vs. 2.0% (4/204)] (p < 0.001). Reduced turnaround time of BRCA1/2 testing (p < 0.001) and the use of neoadjuvant chemotherapy (p < 0.001) were associated with BRCA1/2 mutation status identified prior to surgery. Although not statistically significant, higher incidence of developing contralateral breast cancer for BRCA1/2 mutation carriers who underwent ipsilateral surgery-only compared to those who underwent contralateral RRM was observed [12.1% (95% CI: 7.7–17.7%)] (p = 0.1618). Preoperative diagnosis of BRCA1/2 mutation could impact surgical decision-making for breast cancer patients to undergo risk-reducing surgery at the time of initial surgery.

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Timely Cancer Genetic Counseling and Testing for Young Women With Breast Cancer: Impact on Surgical Decision-making for Contralateral Risk-reducing Mastectomy

10.21203/rs.3.rs-1146819/v1 ◽

2022 ◽

Author(s):

Shenin Dettwyler ◽

Darcy Thull ◽

Priscilla McAuliffe ◽

Jennifer Steiman ◽

Ronald Johnson ◽

...

Keyword(s):

Breast Cancer ◽

Decision Making ◽

Family History ◽

Young Women ◽

Young Patients ◽

Cancer Genetic Counseling ◽

Surgical Decision ◽

Pathogenic Variants ◽

Risk Reducing ◽

Surgical Decision Making

Abstract PURPOSE: Genetic testing (GT) can identify individuals with pathogenic variants (PV) in breast cancer (BC) predisposition genes, who may consider contralateral risk-reducing mastectomy (CRRM). We report on CRRM rates in young women newly diagnosed with BC who received GT through a multidisciplinary clinic. METHODS: Clinical data was reviewed for patients seen between November 2014 and June 2019. Patients with non-metastatic, unilateral BC diagnosed at age ≤45 and completed GT prior to surgery were included. Associations between surgical intervention and age, BC stage, family history, and GT results were evaluated. RESULTS: Of the 194 patients, 30 (15.5%) had a PV in a BC predisposition gene (ATM , BRCA1, BRCA2, CHEK2, NBN, NF1), with 66.7% in BRCA1 or BRCA2. Of 164 (84.5%) uninformative results, 132 (68%) were negative and 32 (16.5%) were variants of uncertain significance (VUS). Overall, 67 (34.5%) had CRRM, including 25/30 (83.3%) PV carriers and 42/164 (25.6%) non-carriers. Only a positive test result was associated with CRRM (p < 0.01). For the 164 with uninformative results, CRRM was not associated with age (p = 0.23), a VUS, (p = 0.08), family history (p = 0.19), or BC stage (p = 0.10). CONCLUSION: In this cohort of young women with BC, the identification of a PV in a BC predisposition gene was the only factor associated with the decision to pursue CRRM. Thus, incorporation of genetic services in the initial evaluation of young patients with a new BC could contribute to the surgical decision-making process.

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Surgical decision making in premenopausal BRCA carriers considering risk-reducing early salpingectomy or salpingo-oophorectomy: a qualitative study

Journal of Medical Genetics ◽

10.1136/jmedgenet-2020-107501 ◽

2021 ◽

pp. jmedgenet-2020-107501

Author(s):

Faiza Gaba ◽

Shivam Goyal ◽

Dalya Marks ◽

Dhivya Chandrasekaran ◽

Olivia Evans ◽

...

Keyword(s):

Ovarian Cancer ◽

Decision Making ◽

Cancer Risk ◽

Risk Reduction ◽

Ovarian Cancer Risk ◽

Early Menopause ◽

Surgical Decision ◽

Risk Reducing ◽

Surgical Decision Making ◽

Cancer Risk Reduction

BackgroundAcceptance of the role of the fallopian tube in ‘ovarian’ carcinogenesis and the detrimental sequelae of surgical menopause in premenopausal women following risk-reducing salpingo-oophorectomy (RRSO) has resulted in risk-reducing early-salpingectomy with delayed oophorectomy (RRESDO) being proposed as an attractive alternative risk-reducing strategy in women who decline/delay oophorectomy. We present the results of a qualitative study evaluating the decision-making process among BRCA carriers considering prophylactic surgeries (RRSO/RRESDO) as part of the multicentre PROTECTOR trial (ISRCTN:25173360).MethodsIn-depth semistructured 1:1 interviews conducted using a predeveloped topic-guide (development informed by literature review and expert consultation) until informational saturation reached. Wording and sequencing of questions were left open with probes used to elicit additional information. All interviews were audio-recorded, transcribed verbatim, transcripts analysed using an inductive theoretical framework and data managed using NVIVO-v12.ResultsInformational saturation was reached following 24 interviews. Seven interconnected themes integral to surgical decision making were identified: fertility/menopause/cancer risk reduction/surgical choices/surgical complications/sequence of ovarian-and-breast prophylactic surgeries/support/satisfaction. Women for whom maximising ovarian cancer risk reduction was relatively more important than early menopause/quality-of-life preferred RRSO, whereas those more concerned about detrimental impact of menopause chose RRESDO. Women managed in specialist familial cancer clinic settings compared with non-specialist settings felt they received better quality care, improved hormone replacement therapy access and were more satisfied.ConclusionMultiple contextual factors (medical, physical, psychological, social) influence timing of risk-reducing surgeries. RRESDO offers women delaying/declining premenopausal oophorectomy, particularly those concerned about menopausal effects, a degree of ovarian cancer risk reduction while avoiding early menopause. Care of high-risk women should be centralised to centres with specialist familial gynaecological cancer risk management services to provide a better-quality, streamlined, holistic multidisciplinary approach.

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1228: Assessing the Surgical Decision-Making Ability of Novice and Proficient Urologists

The Journal of Urology ◽

10.1016/s0022-5347(18)31442-3 ◽

2007 ◽

Vol 177 (4S) ◽

pp. 405-405

Author(s):

Suman Chatterjee ◽

Jonathon Ng ◽

Edward D. Matsumoto

Keyword(s):

Decision Making ◽

Surgical Decision ◽

Surgical Decision Making

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Myocardial infarction – An important factor for surgical decision-making?

The Thoracic and Cardiovascular Surgeon ◽

10.1055/s-2008-1037697 ◽

2008 ◽

Vol 56 (S 1) ◽

Author(s):

B Osswald ◽

U Tochtermann ◽

S Keller ◽

D Badowski-Zyla ◽

V Gegouskov ◽

...

Keyword(s):

Myocardial Infarction ◽

Decision Making ◽

Surgical Decision ◽

Surgical Decision Making

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3132 Machine Learning for Prediction of Pathologic Pneumatosis Intestinalis Using CT Scans

Journal of Clinical and Translational Science ◽

10.1017/cts.2019.142 ◽

2019 ◽

Vol 3 (s1) ◽

pp. 60-61

Author(s):

Kadie Clancy ◽

Esmaeel Dadashzadeh ◽

Christof Kaltenmeier ◽

JB Moses ◽

Shandong Wu

Keyword(s):

Machine Learning ◽

Decision Making ◽

Pneumatosis Intestinalis ◽

Learning Models ◽

Surgical Decision ◽

Prediction Task ◽

Svm Model ◽

Surgical Decision Making ◽

Intraoperative Visualization ◽

Machine Learning Models

OBJECTIVES/SPECIFIC AIMS: This retrospective study aims to create and train machine learning models using a radiomic-based feature extraction method for two classification tasks: benign vs. pathologic PI and operation of benefit vs. operation not needed. The long-term goal of our study is to build a computerized model that incorporates both radiomic features and critical non-imaging clinical factors to improve current surgical decision-making when managing PI patients. METHODS/STUDY POPULATION: Searched radiology reports from 2010-2012 via the UPMC MARS Database for reports containing the term “pneumatosis” (subsequently accounting for negations and age restrictions). Our inclusion criteria included: patient age 18 or older, clinical data available at time of CT diagnosis, and PI visualized on manual review of imaging. Cases with intra-abdominal free air were excluded. Collected CT imaging data and an additional 149 clinical data elements per patient for a total of 75 PI cases. Data collection of an additional 225 patients is ongoing. We trained models for two clinically-relevant prediction tasks. The first (referred to as prediction task 1) classifies between benign and pathologic PI. Benign PI is defined as either lack of intraoperative visualization of transmural intestinal necrosis or successful non-operative management until discharge. Pathologic PI is defined as either intraoperative visualization of transmural PI or withdrawal of care and subsequent death during hospitalization. The distribution of data samples for prediction task 1 is 47 benign cases and 38 pathologic cases. The second (referred to as prediction task 2) classifies between whether the patient benefitted from an operation or not. “Operation of benefit” is defined as patients with PI, be it transmural or simply mucosal, who benefited from an operation. “Operation not needed” is defined as patients who were safely discharged without an operation or patients who had an operation, but nothing was found. The distribution of data samples for prediction task 2 is 37 operation not needed cases and 38 operation of benefit cases. An experienced surgical resident from UPMC manually segmented 3D PI ROIs from the CT scans (5 mm Axial cut) for each case. The most concerning ~10-15 cm segment of bowel for necrosis with a 1 cm margin was selected. A total of 7 slices per patient were segmented for consistency. For both prediction task 1 and prediction task 2, we independently completed the following procedure for testing and training: 1.) Extracted radiomic features from the 3D PI ROIs that resulted in 99 total features. 2.) Used LASSO feature selection to determine the subset of the original 99 features that are most significant for performance of the prediction task. 3.) Used leave-one-out cross-validation for testing and training to account for the small dataset size in our preliminary analysis. Implemented and trained several machine learning models (AdaBoost, SVM, and Naive Bayes). 4.) Evaluated the trained models in terms of AUC and Accuracy and determined the ideal model structure based on these performance metrics. RESULTS/ANTICIPATED RESULTS: Prediction Task 1: The top-performing model for this task was an SVM model trained using 19 features. This model had an AUC of 0.79 and an accuracy of 75%. Prediction Task 2: The top-performing model for this task was an SVM model trained using 28 features. This model had an AUC of 0.74 and an accuracy of 64%. DISCUSSION/SIGNIFICANCE OF IMPACT: To the best of our knowledge, this is the first study to use radiomic-based machine learning models for the prediction of tissue ischemia, specifically intestinal ischemia in the setting of PI. In this preliminary study, which serves as a proof of concept, the performance of our models has demonstrated the potential of machine learning based only on radiomic imaging features to have discriminative power for surgical decision-making problems. While many non-imaging-related clinical factors play a role in the gestalt of clinical decision making when PI presents, we have presented radiomic-based models that may augment this decision-making process, especially for more difficult cases when clinical features indicating acute abdomen are absent. It should be noted that prediction task 2, whether or not a patient presenting with PI would benefit from an operation, has lower performance than prediction task 1 and is also a more challenging task for physicians in real clinical environments. While our results are promising and demonstrate potential, we are currently working to increase our dataset to 300 patients to further train and assess our models. References DuBose, Joseph J., et al. “Pneumatosis Intestinalis Predictive Evaluation Study (PIPES): a multicenter epidemiologic study of the Eastern Association for the Surgery of Trauma.” Journal of Trauma and Acute Care Surgery 75.1 (2013): 15-23. Knechtle, Stuart J., Andrew M. Davidoff, and Reed P. Rice. “Pneumatosis intestinalis. Surgical management and clinical outcome.” Annals of Surgery 212.2 (1990): 160.

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