The other side of technology: risk of overdiagnosis of breast cancer with mammography screening

Intraductal delivery of nanocarriers for ductal carcinoma in situ treatment: a strategy to enhance localized delivery

Ductal carcinoma in situ describes the most commonly occurring, noninvasive malignant breast disease, which could be the leading factor in invasive breast cancer. Despite remarkable advancements in treatment options, poor specificity, low bioavailability and dose-induced toxicity of chemotherapy are the main constraint. A unique characteristic of nanocarriers may overcome these problems. Moreover, the intraductal route of administration serves as an alternative approach. The direct nanodrug delivery into mammary ducts results in the accumulation of anticancer agents at targeted tissue for a prolonged period with high permeability, significantly decreasing the tumor size and improving the survival rate. This review focuses mainly on the intraductal delivery of nanocarriers in treating ductal carcinoma in situ, together with potential clinical translational research.

Dual-Intended Deep Learning Model for Breast Cancer Diagnosis in Ultrasound Imaging

Automated medical data analysis demonstrated a significant role in modern medicine, and cancer diagnosis/prognosis to achieve highly reliable and generalizable systems. In this study, an automated breast cancer screening method in ultrasound imaging is proposed. A convolutional deep autoencoder model is presented for simultaneous segmentation and radiomic extraction. The model segments the breast lesions while concurrently extracting radiomic features. With our deep model, we perform breast lesion segmentation, which is linked to low-dimensional deep-radiomic extraction (four features). Similarly, we used high dimensional conventional imaging throughputs and applied spectral embedding techniques to reduce its size from 354 to 12 radiomics. A total of 780 ultrasound images—437 benign, 210, malignant, and 133 normal—were used to train and validate the models in this study. To diagnose malignant lesions, we have performed training, hyperparameter tuning, cross-validation, and testing with a random forest model. This resulted in a binary classification accuracy of 78.5% (65.1–84.1%) for the maximal (full multivariate) cross-validated model for a combination of radiomic groups.

[Breast cancer incidence related with a population-based screening program]

OBJECTIVE

To compare breast cancer cumulative incidence, time evolution and stage at diagnosis between participants and non-participant women in a population-based screening program.

METHODS

Cohort study of breast cancer incidence in relation to participation in a population screening program. The study population included women from the target population of the screening program. The source of information for diagnostics and stages was the population-based cancer registry. The analysis period was 1999-2010.

RESULTS

The Relative Risk for invasive, in situ, and total cancers diagnosed in participant women compared with non-participants were respectively 1.16 (0.94-1.43), 2.98 (1.16-7.62) and 1.22 (0.99-1.49). The Relative Risk for participants versus non-participants was 2.47 (1.55-3.96) for diagnosis at stagei, 2.58 (1.67-3.99) for T1 and 2.11 (1.38-3.23) for negative lymph node involvement. The cumulative incidence trend had two joint points in both arms, with an Annual Percent of Change of 92.3 (81.6-103.5) between 1999-2001, 18.2 (16.1-20.3) between 2001-2005 and 5.9 (4.0-7.8) for the last period in participants arm, and 72.6 (58.5-87.9) between 1999-2001, 12.6 (7.9-17.4) between 2001-2005, and 8.6 (6.5-10.6) in the last period in the non-participant arm.

CONCLUSIONS

Participating in the breast cancer screening program analyzed increased the in situ cumulative cancer incidence, but not the invasive and total incidence. Diagnoses were earlier in the participant arm.

Overdiagnosis: epidemiologic concepts and estimation

Overdiagnosis of thyroid cancer was propounded regarding the rapidly increasing incidence in South Korea. Overdiagnosis is defined as 'the detection of cancers that would never have been found were it not for the screening test', and may be an extreme form of lead bias due to indolent cancers, as is inevitable when conducting a cancer screening programme. Because it is solely an epidemiological concept, it can be estimated indirectly by phenomena such as a lack of compensatory drop in post-screening periods, or discrepancies between incidence and mortality. The erstwhile trials for quantifying the overdiagnosis in screening mammography were reviewed in order to secure the data needed to establish its prevalence in South Korea.

Overdiagnosis by mammographic screening for breast cancer studied in birth cohorts in The Netherlands

A drawback of early detection of breast cancer through mammographic screening is the diagnosis of breast cancers that would never have become clinically detected. This phenomenon, called overdiagnosis, is ideally quantified from the breast cancer incidence of screened and unscreened cohorts of women with follow-up until death. Such cohorts do not exist, requiring other methods to estimate overdiagnosis. We are the first to quantify overdiagnosis from invasive breast cancer and ductal carcinoma in situ (DCIS) in birth cohorts using an age-period-cohort -model (APC-model) including variables for the initial and subsequent screening rounds and a 5-year period after leaving screening. Data on the female population and breast cancer incidence were obtained from Statistics Netherlands, "Stichting Medische registratie" and the Dutch Cancer Registry for women aged 0-99 years. Data on screening participation was obtained from the five regional screening organizations. Overdiagnosis was calculated from the excess breast cancer incidence in the screened group divided by the breast cancer incidence in presence of screening for women aged 20-99 years (population perspective) and for women in the screened-age range (individual perspective). Overdiagnosis of invasive breast cancer was 11% from the population perspective and 17% from the invited women perspective in birth cohorts screened from age 49 to 74. For invasive breast cancer and DCIS together, overdiagnosis was 14% from population perspective and 22% from invited women perspective. A major strength of an APC-model including the different phases of screening is that it allows to estimate overdiagnosis in birth cohorts, thereby preventing overestimation.

Can mammographic and sonographic imaging features predict the Oncotype DX™ recurrence score in T1 and T2, hormone receptor positive, HER2 negative and axillary lymph node negative breast cancers?

To determine whether mammographic or sonographic features can predict the Oncotype DX™ recurrence scores (RS) in patients with TI-II, hormone receptor (HR) positive, HER2/neu negative and node negative breast cancers. Institutional board review was obtained and informed consent was waived for this retrospective study. Seventy-eight patients with stage I-II invasive breast cancer that was HR positive, HER2 negative, and lymph node negative for whom mammographic and or sonographic imaging and Oncotype DX™ assay scores were available were included in the study Four breast dedicated radiologists blinded to the RS retrospectively described the lesions according to BI-RADS lexicon descriptors. Multivariable logistic regression was used to test for significant independent predictors of low (<18) versus intermediate to high range (≥18). Two imaging features reached statistical significance in predicting low from intermediate or high risk RS: pleomorphic microcalcifications within a mass (P = 0.017); OR 8.37, 95 % CI (1.47-47.79) on mammography and posterior acoustic enhancement in a mass on ultrasound (P = 0.048); OR 4.35, 95 % CI (1.01-18.73) on multivariable logistic regression. A mass with pleomorphic microcalcifications on mammography or the presence of posterior acoustic enhancement on ultrasound may predict an intermediate to high RS as determined by the Oncotype DX(TM) assay in patients with stage I-II HR positive, HER2 negative, and lymph node negative invasive breast cancer.

Breast-cancer early detection in low-income and middle-income countries: do what you can versus one size fits all

In general, rates of breast cancer are lower in low-income and middle-income countries (LMCs) than they are in more industrialised countries of North America and Europe. This lower incidence means that screening programmes aimed at early detection in asymptomatic women would have a lower yield--ie, substantially more women would need to be examined to find a true case of breast cancer. Because the average age of breast cancer is generally younger in LMCs, it has been suggested that breast-cancer screening programmes begin at an earlier age in these settings. However, the younger average age of breast cancer is mainly driven by the age distribution of the population, and fewer older women with breast cancer, rather than by higher age-specific incidence rates in younger women. Resources in LMCs might be better used to raise awareness and encourage more women with palpable breast lumps to seek and receive treatment in a timely manner.

Interpreting overdiagnosis estimates in population-based mammography screening

Estimates of overdiagnosis in mammography screening range from 1% to 54%. This review explains such variations using gradual implementation of mammography screening in the Netherlands as an example. Breast cancer incidence without screening was predicted with a micro-simulation model. Observed breast cancer incidence (including ductal carcinoma in situ and invasive breast cancer) was modeled and compared with predicted incidence without screening during various phases of screening program implementation. Overdiagnosis was calculated as the difference between the modeled number of breast cancers with and the predicted number of breast cancers without screening. Estimating overdiagnosis annually between 1990 and 2006 illustrated the importance of the time at which overdiagnosis is measured. Overdiagnosis was also calculated using several estimators identified from the literature. The estimated overdiagnosis rate peaked during the implementation phase of screening, at 11.4% of all predicted cancers in women aged 0–100 years in the absence of screening. At steady-state screening, in 2006, this estimate had decreased to 2.8%. When different estimators were used, the overdiagnosis rate in 2006 ranged from 3.6% (screening age or older) to 9.7% (screening age only). The authors concluded that the estimated overdiagnosis rate in 2006 could vary by a factor of 3.5 when different denominators were used. Calculations based on earlier screening program phases may overestimate overdiagnosis by a factor 4. Sufficient follow-up and agreement regarding the chosen estimator are needed to obtain reliable estimates.