Breast cancer is the most common cancer detected in women and current screening methods for the disease are not sensitive. Volatile organic compounds (VOCs) include endogenous metabolites that provide information about health and disease which might be useful to develop a better screening method for breast cancer. The goal of this study was to classify mice with and without tumors and compare tumors localized to the mammary pad and tumor cells injected into the iliac artery by differences in VOCs in urine. After 4T1.2 tumor cells were injected into BALB/c mice either in the mammary pad or into the iliac artery, urine was collected, VOCs from urine headspace were concentrated by solid phase microextraction and results were analyzed by gas chromatography-mass spectrometry quadrupole time-of-flight. Multivariate and univariate statistical analyses were employed to find potential biomarkers for breast cancer and metastatic breast cancer in mice models. A set of six VOCs classified mice with and without tumors with an area under the receiver operator characteristic (ROC AUC) of 0.98 (95% confidence interval [0.85, 1.00]) via five-fold cross validation. Classification of mice with tumors in the mammary pad and iliac artery was executed utilizing a different set of six VOCs, with a ROC AUC of 0.96 (95% confidence interval [0.75, 1.00]).
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.
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.
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.
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.
- Award ID(s):
- 1734853
- NSF-PAR ID:
- 10163688
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- British Journal of Cancer
- Volume:
- 123
- Issue:
- 5
- ISSN:
- 0007-0920
- Page Range / eLocation ID:
- p. 860-867
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Summary Cancer is a major public health burden and is the second leading cause of death in the USA. The US National Cancer Institute estimated overall costs of cancer in 2007 at $219.2 billion. Breast cancer has the highest cancer incidence rates among women and is the second leading cause of cancer death among women. The ‘Surveillance, epidemiology, and end results’ programme of the National Cancer Institute collects and publishes cancer survival data from 17 population-based cancer registries. The CANSURV software of the National Cancer Institute analyses cancer survival data from the programme by using parametric and semiparametric mixture cure models. Another popular approach in cancer survival is the competing risks approach which considers the simultaneous risks from cancer and various other causes. The paper develops a model that unifies the mixture cure and competing risks approaches and that can handle the masked causes of death in a natural way. Markov chain sampling is used for Bayesian analysis of this model, and modelling and computational issues of general and restricted structures are discussed. The various model structures are compared by using Bayes factors. This Bayesian model is used to analyse survival data for the approximately 620000 breast cancer cases from the programme. The estimated cumulative probabilities of death from breast cancer from the proposed mixture cure competing risks model is found to be lower than the estimates that are obtained from the CANSURV software. Whereas the estimate of the cure fraction is found to be dependent on the modelling assumptions, the survival and cumulative probability estimates are not sensitive to these assumptions. Breast cancer survival in different ethnic subgroups, in different age subgroups and in patients with localized, regional and distant stages of the disease are compared. The risk of mortality from breast cancer is found to be the dominant cause of death in the beginning part of the follow-up whereas the risk from other competing causes often became the dominant cause in the latter part. This interrelation between breast cancer and other competing risks varies among the different ethnic groups, the different stages and the different age groups.
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Abstract Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer. However, current AS protocols rely on detecting tumor progression through direct observation according to population-based monitoring strategies. This approach limits the design of patient-specific AS plans and may delay the detection of tumor progression. Here, we present a pilot study to address these issues by leveraging personalized computational predictions of prostate cancer growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data (T2-weighted MRI and apparent diffusion coefficient maps from diffusion-weighted MRI). Our results show that our technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients from 0.93 to 0.99 across our cohort (n = 7). In addition, we identify a model-based biomarker of higher-risk prostate cancer: the mean proliferation activity of the tumor (P = 0.041). Using logistic regression, we construct a prostate cancer risk classifier based on this biomarker that achieves an area under the ROC curve of 0.83. We further show that coupling our tumor forecasts with this prostate cancer risk classifier enables the early identification of prostate cancer progression to higher-risk disease by more than 1 year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for patients with prostate cancer.
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Background Rates of contralateral prophylactic mastectomy (CPM)—removal of the healthy breast following breast cancer diagnosis—have increased, particularly among women for whom CPM provides no survival benefit. Affective (i.e., emotional) decision making is often blamed for this increase. We studied whether greater negative breast cancer affect could motivate uptake of CPM through increased cancer risk perceptions and biased treatment evaluations.
Methods We randomly assigned healthy women with average breast-cancer risk ( N = 1,030; Mage= 44.14, SD = 9.23 y) to 1 of 3 affect conditions (negative v. neutral v. positive narrative manipulation) in a hypothetical online experiment in which they were asked to imagine being diagnosed with cancer in one breast. We assessed 1) treatment choice, 2) affect toward CPM, and 3) perceived risk of future breast cancer in each breast (cancer affected and healthy) following lumpectomy, single mastectomy, and CPM.
Results The manipulation caused women in the negative and neutral narrative conditions (26.9% and 26.4%, respectively) to choose CPM more compared with the positive narrative condition (19.1%). Across conditions, women’s CPM affect did not differ. However, exploratory analyses addressing a possible association of affect toward cancer-related targets suggested that women in the negative narrative condition may have felt more positively toward CPM than women in the positive narrative condition. The manipulation did not have significant effects on breast cancer risk perceptions.
Limitations The manipulation of affect had a small effect size, possibly due to the hypothetical nature of this study and/or strong a priori knowledge and attitudes about breast cancer and its treatment options.
Conclusion Increased negative affect toward breast cancer increased choice of CPM over other surgical options and might have motivated more positive affective evaluations of CPM.
Highlights This study used narratives to elicit different levels of negative integral affect toward breast cancer to investigate the effects of affect on breast cancer treatment choices. Increased negative affect toward breast cancer increased the choice of double mastectomy over lumpectomy and single mastectomy to treat a hypothetical, early-stage cancer. The narrative manipulation of negative affect toward breast cancer did not change the perceived risks of future cancer following any of the surgical interventions. Negative affect toward breast cancer may have biased affective evaluations of double mastectomy.
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