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

Variation of the omega-3 content of Australian food products

Abstract from the 2008 Annual Scientific Meeting of the Nutrition Society of Australia, 30 November - 3 December 2008, Glenelg, Australia

Comparison of the pressure dependences of Tc in the trivalent d-electron superconductors

Whereas dhcp La superconducts at ambient pressure with Tc = 5 K, the other trivalent d-electron metals Sc, Y, and Lu only superconduct if high pressures are applied. Earlier measurements of the pressure dependence of Tc for Sc and Lu metal are here extended to much higher pressures. Whereas Tc for Lu increases monotonically with pressure to 12.4 K at 174 GPa (1.74 Mbar). Tc for Sc reaches 19.6 K at 107 GPa, the 2nd highest value observed for any elemental superconductor. At higher pressures a phase transition occurs whereupon Tc drops to 8.31 K at 111 GPa. The Tc(P) dependences for Sc and Lu are compared to those of Y and La. An interesting correlation is pointed out between the value of Tc and the fractional free volume available to the conduction electrons outside the ion cores, a quantity which is directly related to the number of d electrons in the conduction band

ProtoDESI: First On-Sky Technology Demonstration for the Dark Energy Spectroscopic Instrument

The Dark Energy Spectroscopic Instrument (DESI) is under construction to measure the expansion history of the universe using the baryon acoustic oscillations technique. The spectra of 35 million galaxies and quasars over 14,000 square degrees will be measured during a 5-year survey. A new prime focus corrector for the Mayall telescope at Kitt Peak National Observatory will deliver light to 5,000 individually targeted fiber-fed robotic positioners. The fibers in turn feed ten broadband multi-object spectrographs. We describe the ProtoDESI experiment, that was installed and commissioned on the 4-m Mayall telescope from August 14 to September 30, 2016. ProtoDESI was an on-sky technology demonstration with the goal to reduce technical risks associated with aligning optical fibers with targets using robotic fiber positioners and maintaining the stability required to operate DESI. The ProtoDESI prime focus instrument, consisting of three fiber positioners, illuminated fiducials, and a guide camera, was installed behind the existing Mosaic corrector on the Mayall telescope. A Fiber View Camera was mounted in the Cassegrain cage of the telescope and provided feedback metrology for positioning the fibers. ProtoDESI also provided a platform for early integration of hardware with the DESI Instrument Control System that controls the subsystems, provides communication with the Telescope Control System, and collects instrument telemetry data. Lacking a spectrograph, ProtoDESI monitored the output of the fibers using a Fiber Photometry Camera mounted on the prime focus instrument. ProtoDESI was successful in acquiring targets with the robotically positioned fibers and demonstrated that the DESI guiding requirements can be met.Comment: Accepted versio

Machine learning techniques for personalized breast cancer risk prediction : comparison with the BCRAT and BOADICEA models

Comprehensive breast cancer risk prediction models enable identifying and targeting women at high-risk, while reducing interventions in those at low-risk. Breast cancer risk prediction models used in clinical practice have low discriminatory accuracy (0.53-0.64). Machine learning (ML) offers an alternative approach to standard prediction modeling that may address current limitations and improve accuracy of those tools. The purpose of this study was to compare the discriminatory accuracy of ML-based estimates against a pair of established methods-the Breast Cancer Risk Assessment Tool (BCRAT) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) models.; We quantified and compared the performance of eight different ML methods to the performance of BCRAT and BOADICEA using eight simulated datasets and two retrospective samples: a random population-based sample of U.S. breast cancer patients and their cancer-free female relatives (N = 1143), and a clinical sample of Swiss breast cancer patients and cancer-free women seeking genetic evaluation and/or testing (N = 2481).; Predictive accuracy (AU-ROC curve) reached 88.28% using ML-Adaptive Boosting and 88.89% using ML-random forest versus 62.40% with BCRAT for the U.S. population-based sample. Predictive accuracy reached 90.17% using ML-adaptive boosting and 89.32% using ML-Markov chain Monte Carlo generalized linear mixed model versus 59.31% with BOADICEA for the Swiss clinic-based sample.; There was a striking improvement in the accuracy of classification of women with and without breast cancer achieved with ML algorithms compared to the state-of-the-art model-based approaches. High-accuracy prediction techniques are important in personalized medicine because they facilitate stratification of prevention strategies and individualized clinical management

Intention to Inform Relatives, Rates of Cascade Testing, and Preference for Patient-Mediated Communication in Families Concerned with Hereditary Breast and Ovarian Cancer and Lynch Syndrome: The Swiss CASCADE Cohort.

Cascade screening for Tier 1 cancer genetic conditions is a significant public health intervention because it identifies untested relatives of individuals known to carry pathogenic variants associated with hereditary breast and ovarian cancer (HBOC) and Lynch syndrome (LS). The Swiss CASCADE is a family-based, open-ended cohort, including carriers of HBOC- and LS-associated pathogenic variants and their relatives. This paper describes rates of cascade screening in relatives from HBOC- and LS- harboring families, examines carriers' preferences for communication of testing results, and describes theory-based predictors of intention to invite relatives to a cascade screening program. Information has been provided by 304 index cases and 115 relatives recruited from September 2017 to December 2021. On average, 10 relatives per index case were potentially eligible for cascade screening. Approximately 65% of respondents wanted to invite relatives to the cohort, and approximately 50% indicated a preference for patient-mediated communication of testing results, possibly with the assistance of digital technology. Intention to invite relatives was higher for first- compared to second- and third-degree relatives, but was not different between syndromes or based on relatives' gender. The family environment and carrying pathogenic variants predicts intention to invite relatives. Information helps optimize delivery of tailored genetic services

A novel genetic programming approach to the design of engine control systems for the voltage stabilisation of hybrid electric vehicle generator outputs

This paper describes a Genetic Programming based automatic design methodology applied to the maintenance of a stable generated electrical output from a series-hybrid vehi- cle generator set. The generator set comprises a 3-phase AC generator whose output is subsequently rectified to DC.The engine/generator combination receives its control input via an electronically actuated throttle, whose control integration is made more complex due to the significant system time delay. This time delay problem is usually addressed by model predictive design methods, which add computational complexity and rely as a necessity on accurate system and delay models. In order to eliminate this reliance, and achieve stable operation with disturbance rejection, a controller is designed via a Genetic Programming framework implemented directly in Matlab, and particularly, Simulink. the principal objective is to obtain a relatively simple controller for the time-delay system which doesn’t rely on computationally expensive structures, yet retains inherent disturabance rejection properties. A methodology is presented to automatically design control systems directly upon the block libraries available in Simulink to automatically evolve robust control structures

First-line temozolomide combined with bevacizumab in metastatic melanoma: a multicentre phase II trial (SAKK 50/07)

Background: Oral temozolomide has shown similar efficacy to dacarbazine in phase III trials with median progression-free survival (PFS) of 2.1 months. Bevacizumab has an inhibitory effect on the proliferation of melanoma and sprouting endothelial cells. We evaluated the addition of bevacizumab to temozolomide to improve efficacy in stage IV melanoma. Patients and methods: Previously untreated metastatic melanoma patients with Eastern Cooperative Oncology Group performance status of two or more were treated with temozolomide 150 mg/m2 days 1-7 orally and bevacizumab 10 mg/kg body weight i.v. day 1 every 2 weeks until disease progression or unacceptable toxicity. The primary end point was disease stabilisation rate [complete response (CR), partial response (PR) or stable disease (SD)] at week 12 (DSR12); secondary end points were best overall response, PFS, overall survival (OS) and adverse events. Results: Sixty-two patients (median age 59 years) enrolled at nine Swiss centres. DSR12 was 52% (PR: 10 patients and SD: 22 patients). Confirmed overall response rate was 16.1% (CR: 1 patient and PR: 9 patients). Median PFS and OS were 4.2 and 9.6 months. OS (12.0 versus 9.2 months; P = 0.014) was higher in BRAF V600E wild-type patients. Conclusions: The primary end point was surpassed showing promising activity of this bevacizumab/temozolomide combination with a favourable toxicity profile. Response and OS were significantly higher in BRAF wild-type patient