Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations

BACKGROUND: The clinical utility of machine-learning (ML) algorithms for breast cancer risk prediction and screening practices is unknown. We compared classification of lifetime breast cancer risk based on ML and the BOADICEA model. We explored the differences in risk classification and their clinical impact on screening practices. METHODS: We used three different ML algorithms and the BOADICEA model to estimate lifetime breast cancer risk in a sample of 112,587 individuals from 2481 families from the Oncogenetic Unit, Geneva University Hospitals. Performance of algorithms was evaluated using the area under the receiver operating characteristic (AU-ROC) curve. Risk reclassification was compared for 36,146 breast cancer-free women of ages 20-80. The impact on recommendations for mammography surveillance was based on the Swiss Surveillance Protocol. RESULTS: The predictive accuracy of ML-based algorithms (0.843 </= AU-ROC </= 0.889) was superior to BOADICEA (AU-ROC = 0.639) and reclassified 35.3% of women in different risk categories. The largest reclassification (20.8%) was observed in women characterised as 'near population' risk by BOADICEA. Reclassification had the largest impact on screening practices of women younger than 50. CONCLUSION: ML-based reclassification of lifetime breast cancer risk occurred in approximately one in three women. Reclassification is important for younger women because it impacts clinical decision- making for the initiation of screening

Performance of BOADICEA and BRCAPRO genetic models and of empirical criteria based on cancer family history for predicting BRCA mutation carrier probabilities: A retrospective study in a sample of Italian cancer genetics clinics

Abstract Purpose To evaluate in current practice the performance of BOADICEA and BRCAPRO risk models and empirical criteria based on cancer family history for the selection of individuals for BRCA genetic testing. Patients and methods The probability of BRCA mutation according to the three tools was retrospectively estimated in 918 index cases consecutively undergone BRCA testing at 15 Italian cancer genetics clinics between 2006 and 2008. Results 179 of 918 cases (19.5%) carried BRCA mutations. With the strict use of the criteria based on cancer family history 173 BRCA (21.9%) mutations would have been detected in 789 individuals. At the commonly used 10% threshold of BRCA mutation carrier probability, the genetic models showed a similar performance [PPV (38% and 37%), sensitivity (76% and 77%) and specificity (70% and 69%)]. Their strict use would have avoided around 60% of the tests but would have missed approximately 1 every 4 carriers. Conclusion Our data highlight the complexity of BRCA testing referral in routine practice and question the strict use of genetic models for BRCA risk assessment

Predictive control of fast output-sampled digital feedback systems via a polynomial approach

This paper describes the synthesis of model-based predictive controllers for multivariable digital feedback systems in which the plant output signals are measured at a faster rate than the controls are applied. In contrast with the recent literature devoted to multirate sampled-data system theory, where state-variable system representations are used exclusively, a polynomial modelling approach that exploits the frequency and switch decomposition techniques established in the 1950s is adopted. It is demonstrated that the principal advantage of fast output signal-sampled predictive control algorithms lies in their potential to suppress process and, particularly, measurement noise disturbances

Clinical factors associated with prolonged response and survival under olaparib as maintenance therapy in BRCA mutated ovarian cancers.

To investigate clinical factors associated with prolonged progression-free survival (PFS) and overall survival (OS) in relapsing epithelial ovarian cancer (EOC) patients with BRCA mutations and receiving olaparib as maintenance therapy in daily practice. Multicenter (8 hospitals) European retrospective study of relapsing EOC patients having germline or somatic mutations of BRCA1/BRCA2 genes and treated with olaparib as maintenance therapy after platinum-based chemotherapy. One hundred and fifteen patients were included. Median age was 54 years. There were 90 BRCA1 carriers, 24 BRCA2 carriers and one patient had germline mutation of BRCA1 and BRCA2. Six patients had somatic mutations (all BRCA1) and 109 had germline mutations. Ninety percent had serous carcinomas and were platinum-sensitive. Following ultimate platinum-based chemotherapy, 69% of the patients had normalization of CA-125 levels and 87% had RECIST objective responses, either partial (53%) or complete (34%). After a median follow-up of 21 months, median PFS was 12.7 months and median OS was 35.4 months. In multivariate analysis, factors associated with prolonged PFS under olaparib were: platinum-free interval (PFI) ≥ 12 months, RECIST complete response (CR) or partial response (PR) and normalization of CA-125 upon ultimate platinum-based chemotherapy. Factors associated with prolonged OS were PFI ≥ 12 months, CR and normalization of CA-125. Platinum-free interval ≥ 12 months, complete response and normalized CA-125 levels after ultimate platinum-based chemotherapy are associated with prolonged PFS and OS in relapsing BRCA1/BRCA2 mutated ovarian cancer patients who received olaparib as maintenance therapy