Dataset Information

Quantitative breast density analysis to predict interval and node-positive cancers in pursuit of improved screening protocols: a case-control study.

ABSTRACT:

Background

This study investigates whether quantitative breast density (BD) serves as an imaging biomarker for more intensive breast cancer screening by predicting interval, and node-positive cancers.

Methods

This case-control study of 1204 women aged 47-73 includes 599 cancer cases (302 screen-detected, 297 interval; 239 node-positive, 360 node-negative) and 605 controls. Automated BD software calculated fibroglandular volume (FGV), volumetric breast density (VBD) and density grade (DG). A radiologist assessed BD using a visual analogue scale (VAS) from 0 to 100. Logistic regression and area under the receiver operating characteristic curves (AUC) determined whether BD could predict mode of detection (screen-detected or interval); node-negative cancers; node-positive cancers, and all cancers vs. controls.

Results

FGV, VBD, VAS, and DG all discriminated interval cancers (all p < 0.01) from controls. Only FGV-quartile discriminated screen-detected cancers (p < 0.01). Based on AUC, FGV discriminated all cancer types better than VBD or VAS. FGV showed a significantly greater discrimination of interval cancers, AUC = 0.65, than of screen-detected cancers, AUC = 0.61 (p < 0.01) as did VBD (0.63 and 0.53, respectively, p < 0.001).

Conclusion

FGV, VBD, VAS and DG discriminate interval cancers from controls, reflecting some masking risk. Only FGV discriminates screen-detected cancers perhaps adding a unique component of breast cancer risk.

SUBMITTER: Burnside ES

PROVIDER: S-EPMC8438060 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundContemporary treatment of node-positive (N+) colon cancer consists of adjuvant chemotherapy; however, randomized data supporting this practice were derived from lesions T2 or greater. Minimal data exist regarding the use and need for adjuvant chemotherapy in T1N+ disease.ObjectiveThe aim of this study was to determine treatment trends and the effects of adjuvant chemotherapy on T1N+ colon cancers by using the National Cancer Database.DesignThis was a retrospective study. Baseline demographics, tumor, and cancer treatment characteristics were compared. Groups were matched on the propensity to receive chemotherapy. Adjusted long-term survival stratified by chemotherapy use was compared by using the Kaplan-Meier method with the log-rank test. Predictors of not receiving chemotherapy were identified by using a multivariable logistic regression model.SettingsData were collected from the National Cancer Database, which collects cancer data from over 1500 cancer centers.PatientsWe identified patients from 1998 to 2006 with T1N+ disease, excluding those with metastatic disease or previous cancer. Patients were stratified based on whether or not they received chemotherapy.Main outcome measuresThe primary outcome measure of this study was long-term survival.ResultsThree thousand one hundred thirty-seven patients had T1N+ disease; 70.6% (n = 2216) received chemotherapy, and utilization significantly increased from 1998 to 2011 (p < 0.001). Unadjusted analysis revealed that patients treated with chemotherapy were statistically younger and healthier, and had shorter postoperative lengths of stay (all p < 0.001). Unadjusted 5-year survival was higher in patients receiving chemotherapy (87.9% vs 63.0% in patients with no chemotherapy; p < 0.001) and this persisted after propensity matching with (83.4% and 63.0% in patients with or without chemotherapy; p < 0.001). Only age (OR, 0.29; p < 0.001) predicted not receiving chemotherapy.LimitationsLimitations include potential selection bias as well as the inability to compare disease-free survival/recurrence.ConclusionsAdjuvant chemotherapy appears to significantly improve long-term survival in patients receiving chemotherapy in T1N+ disease. Thus, the use of chemotherapy in T1N+ disease is justified and provides a highly significant survival benefit.

Project description:BACKGROUND:Case-control studies show that mammographic density is a better risk factor when defined at higher than conventional pixel-brightness thresholds. We asked if this applied to interval and/or screen-detected cancers. METHOD:We conducted a nested case-control study within the prospective Melbourne Collaborative Cohort Study including 168 women with interval and 422 with screen-detected breast cancers, and 498 and 1197 matched controls, respectively. We measured absolute and percent mammographic density using the Cumulus software at the conventional threshold (Cumulus) and two increasingly higher thresholds (Altocumulus and Cirrocumulus, respectively). Measures were transformed and adjusted for age and body mass index (BMI). Using conditional logistic regression and adjusting for BMI by age at mammogram, we estimated risk discrimination by the odds ratio per adjusted standard deviation (OPERA), calculated the area under the receiver operating characteristic curve (AUC) and compared nested models using the likelihood ratio criterion and models with the same number of parameters using the difference in Bayesian information criterion (?BIC). RESULTS:For interval cancer, there was very strong evidence that the association was best predicted by Cumulus as a percentage (OPERA?=?2.33 (95% confidence interval (CI) 1.85-2.92); all ?BIC >?14), and the association with BMI was independent of age at mammogram. After adjusting for percent Cumulus, no other measure was associated with risk (all P?>?0.1). For screen-detected cancer, however, the associations were strongest for the absolute and percent Cirrocumulus measures (all ?BIC >?6), and after adjusting for Cirrocumulus, no other measure was associated with risk (all P?>?0.07). CONCLUSION:The amount of brighter areas is the best mammogram-based measure of screen-detected breast cancer risk, while the percentage of the breast covered by white or bright areas is the best mammogram-based measure of interval breast cancer risk, irrespective of BMI. Therefore, there are different features of mammographic images that give clinically important information about different outcomes.

Project description:BackgroundThe revised 8th Edition American Joint Committee on Cancer (AJCC) Head and Neck Staging Manual distinguishes HPV-mediated from non-HPV-mediated oropharyngeal cancer (OpSCC). The objective was to analyze OpSCC treatment modalities and outcomes.MethodsA retrospective study of OpSCC patients treated with radiotherapy or chemoradiotherapy between January 1st, 2000, and December 31st, 2008, as identified from the BC Cancer Registry. All patients received treatment at cancer clinics and had at least 5 years follow-up post-treatment. A total of 1259 OpSCC patients were identified. After initial chart reviews, 288 patients were excluded from further analysis and the majority (n = 198) was due to not receiving curative treatment. Based on the availability of formalin-fixed, paraffin-embedded (FFPE) tissue, patients were divided into two cohorts: Study Cohort (FFPE available, n = 244) and General Cohort (FFPE unavailable, n = 727). The Study Cohort was restaged according to AJCC 8th Edition based on p16 immunohistochemistry status. Kaplan-Meier analysis was used to estimate the 5-year overall survival (OS), disease-specific survival (DSS), and locoregional recurrence-free survival (LFS).ResultsAmong 971 patients, OpSCC age-adjusted incidence rate was observed to have increased from 2.1 to 3.5 per 100,000 between 2000 and 2008. The General Cohort was relatively older than the Study Cohort (60.1 ± 10.5 vs. 57.3 ± 9.4), but both cohorts were predominantly males (78.3% vs. 76.2%). Amongst the Study Cohort, 77.5% were p16-positive, of whom 98.4% were down staged in the 8th Edition. These early-stage patients showed OS improvement for those treated with chemoradiation, compared to radiation alone (85.8% vs. 73.1%, p = 0.05).ConclusionsOpSCC incidence is increasing in BC. The addition of chemotherapy to radiotherapy may portend a benefit in OS even for early-stage p16-positive OpSCC. Additional research is necessary to assess the safety of treatment de-escalation even among early-stage disease.

Project description:Purpose To evaluate whether changes in ultrasound features during neoadjuvant chemotherapy (NAC) could predict axillary node response in clinically node-positive breast cancer patients. Methods Patients with biopsy-proven node-positive disease receiving NAC between February 2009 and March 2021 were included. Ultrasound (US) images were obtained using a 5-12-MHz linear array transducer before NAC, after two cycles, and at the completion of NAC. Long and short diameter, cortical thickness, vascularity, and hilum status of the metastatic node were retrospectively reviewed according to breast imaging-reporting and data system (BI-RADS). The included population was randomly divided into a training set and a validation set at a 2:1 ratio using a simple random sampling method. Factors associated with node response were identified through univariate and multivariate analyses. A nomogram combining clinical and changes in ultrasonographic (US) features was developed and validated. The receiver operating characteristic (ROC) and calibration plots were applied to evaluate nomogram performance and discrimination. Results A total of 296 breast cancer patients were included, 108 (36.5%) of whom achieved axillary pathologic complete response (pCR) and 188 (63.5%) had residual nodal disease. Multivariate regression indicated that independent predictors of node pCR contain ultrasound features in addition to clinical features, clinical features including neoadjuvant HER2-targeted therapy and clinical response, ultrasound features after NAC including cortical thickness, hilum status, and reduction in short diameter ≥50%. The nomogram combining clinical features and US features showed better diagnostic performance compared to clinical-only model in the training cohort (AUC: 0.799 vs. 0.699, P=0.001) and the validation cohort (AUC: 0.764 vs. 0.638, P=0.027). Conclusions Ultrasound changes during NAC could improve the accuracy to predict node response after NAC in clinically node-positive breast cancer patients.

Project description:BackgroundCurrent chemoradiation regimens for locally advanced cervical cancer are fairly uniform despite a profound diversity of treatment response and recurrence patterns. The wide range of treatment responses and prognoses to standardized concurrent chemoradiation highlights the need for a reliable tool to predict treatment outcomes. We investigated pretreatment magnetic resonance (MR) imaging features of primary tumor and involved lymph node for predicting clinical outcome in cervical cancer patients.MethodsWe included 93 node-positive cervical cancer patients treated with definitive chemoradiotherapy at our institution between 2006 and 2017. The median follow-up period was 38?months (range, 5-128). Primary tumor and involved lymph node were manually segmented on axial gadolinium-enhanced T1-weighted images as well as T2-weighted images and saved as 3-dimensional regions of interest (ROI). After the segmentation, imaging features related to histogram, shape, and texture were extracted from each ROI. Using these features, random survival forest (RSF) models were built to predict local control (LC), regional control (RC), distant metastasis-free survival (DMFS), and overall survival (OS) in the training dataset (n?=?62). The generated models were then tested in the validation dataset (n?=?31).ResultsFor predicting LC, models generated from primary tumor imaging features showed better predictive performance (C-index, 0.72) than those from lymph node features (C-index, 0.62). In contrast, models from lymph nodes showed superior performance for predicting RC, DMFS, and OS compared to models of the primary tumor. According to the 3-year time-dependent receiver operating characteristic analysis of LC, RC, DMFS, and OS prediction, the respective area under the curve values for the predicted risk of the models generated from the training dataset were 0.634, 0.796, 0.733, and 0.749 in the validation dataset.ConclusionsOur results suggest that tumor and lymph node imaging features may play complementary roles for predicting clinical outcomes in node-positive cervical cancer.