Additional Cancers Linked to Breast Implants

 The FDA issued a safety communication that cancer cases, including squamous cell carcinomas (SCC) and other lymphomas, have been linked to scar tissue around breast implants.

The pathology of these cases differs than that of anaplastic large lymphomas linked to breast implants a decade ago.

A preliminary review of published literature found fewer than 20 cases of SCC and fewer than 30 cases of various lymphomas in the scar tissue around the implant.

Women who have had implants should be aware of symptoms of cancer, which include swelling, pain, lumps or skin changes, the FDA said, noting that some cases emerged years after women received their implants. The SCC cases were linked to both textured and smooth implants, and saline and silicone implants.

AI-Can Reduce Workload in Breast Cancer Screening

A study published in Radiology determined that an artificial intelligence (AI) algorithm could reduce the digital breast tomosynthesis (DBT) and mammography (DM) workload of radiologists without impacting diagnostic accuracy.

A total of 15,987 DM and DBT examinations (which included 98 screening-detected and 15 interval cancers from the 15,987 women were evaluated.  In comparison with the double reading of the DBT images, AI with DBT would result in 72.5% less workload, noninferior sensitivity, and a 16.7% lower recall rate.  Similar results were obtained for AI and DM. 

AI could obviate over 70% of radiologists' double reading thus enable DBT adoption in breast cancer screening programs.

Artificial Intelligence (AI) Outperforms Radiologists in Mammography

The aim of screening mammography is to detect breast cancer in women as early as possible before signs of the disease become clinically obvious.  In a study published in Nature McKinney et al found that AI bested radiologists in detecting breast cancer in screening mammograms. 

Mammograms of 25,856 women in the United Kingdom and 3,097 women in the United States were used to train the AI system. AI was then used to identify the presence of breast cancer in mammograms of women who were known to have had either biopsy-proven breast cancer or normal follow-up imaging results at least 365 days later. The study included mammograms; by conventional digital (2D) mammography and tomosynthesis (also known as 3D mammography). 

The authors report that the AI system outperformed diagnoses made by the radiologists who initially interpreted the mammograms, and the decisions of 6 expert radiologists who interpreted 500 randomly selected cases.

The study reports an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. The authors also performed a simulation in which the AI system participated in the double-reading process that is common in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. 

The authors suggest that further assessment of the AI system with clinical trials may lead to improvements in the accuracy and efficiency of breast cancer screening by limiting the high rates of false positives and negatives which are known to take place in the interpretation of mammograms.

DWI-MRI Predicts Breast Cancer Response to Treatment

Diffusion-weighted MR images (DWI_MRI) acquired 12 weeks after the start of neoadjuvant chemotherapy for breast cancer may provide the best indication of how patients will respond to treatment, according to a study published in Radiology.

The researchers analyzed 242 participants who were randomized to receive 12 weekly doses of paclitaxel with four cycles of anthracycline.   The MRI protocol included, DWI imaging T2-weighted and contrast enhanced sequences.

  

The authors concluded that after 12 weeks of therapy, change in breast tumor apparent diffusion coefficient at MRI predicts complete pathologic response to neoadjuvant chemotherapy

Self-compression in mammography does not interfere with image quality

A study that was published in JAMA Internal Medicine reports suggests that breast cancer screening might be just as effective and less unpleasant when women can control the compression device themselves.  

The investigators randomly assigned 584 women to either practice self-compression or undergo traditional mammograms in which a medical person positioned the breast in the mammographic unit found that when women compressed their own breasts in the machine, they achieved breast thickness that was within 3 millimeters of what women typically had with the traditional mammogram process. 

The authors concluded that there was no difference between the two groups in the quality of images and women reported less pain when the handled the compression on their own and self-compression may be an effective option for women who want to be involved in their breast examination.

Improved breast cancer screening and treatment may have saved many lives.

A study using data from the Surveillance, Epidemiology and End Results (SEER) program of the National Cancer Institute published in Cancer estimated as many as 600,000 breast cancer deaths were avoided since 1989 in women aged 40 to 84 years thanks screening and treatment advances. 

The authors report that from 1975 to 1990, female breast cancer mortality rates in the United States increased by 0.4% per year. Since 1990, breast cancer mortality rates have fallen between 1.8% and 3.4% per year, a decrease that is attributed to increased mammography screening and improvements in treatment.

The authors concluded that since 1989, between 384,000 and 614,500 breast cancer deaths have been averted because of widespread use of screening mammography and advances in the treatment of breast cancer.

Artificial intelligence Can Predict Which Patients Will Develop Breast Cancer Within a Year

A study published in Radiology found that a deep learning artificial intelligence (AI) model from IBM Research can predict the development of malignant breast cancer in patients within a year by linking health records and mammograms. 

In this retrospective study, 52,936 images were obtained in 13,234 women who underwent at least one mammogram and who had health records for at least 1 year before undergoing mammography.  The algorithm was trained on 9,611 mammograms and health records to predict biopsy malignancy and to differentiate between normal from abnormal screenings.

The AI could correctly forecasted respective development of 87 percent and 77 percent of cancerous and non-cancerous cases, and also identified breast cancer in 48 percent of patients that otherwise would have been overlooked, with accuracy comparable to radiologists therefore it has the potential to substantially reduce missed diagnoses of breast cancer.

Double Reading Improves Breast Cancer Screening

A study by Taylor-Phillips et al published in Radiology reports that double reading of mammography screening studies increases the number of cancers detected and reduces recalls. 

The authors conducted a retrospective analysis, 805 206 women who were evaluated through screening and diagnostic studies at 33 English breast centers.

The first reader recalled (4.76%; 38 295 of 805 206 women). Two readers recalled 6.19% of women in total (49 857 of 805 206 women) but arbitration of discordant readings reduced the recall rate to 4.08%. A total of 7055 cancers were detected, of which 627 or 8.89% were detected by the second reader only. These additional cancers were more likely to be ductal carcinoma in situ 30.5%; (183 of 600 vs. 22.0%; 1344 of 6114, and additional invasive cancers were smaller (mean size, 14.2 vs. 16.7 mm), had fewer involved nodes, and were likely to be lower grade.

The authors concluded that double reading with arbitration reduces recall rates and increases cancer detection compared with single reading. 

Contrast Enhanced Ultrasound in Breast Cancer

Vraka et al published in vivo their research regarding the correlation of findings between contrast enhanced ultrasound (CEUS) and pathological and prognostic factors in breast cancer. 

They retrospectively analyzed 34 breast cancers on which CEUS studies were performed and they looked at qualitative findings and quantitative characteristics.

They found that indistinct tumor margins were characteristic of high-grade malignancy and also in estrogen negative tumors.  They also found that perilesional enhancement was seen in tumors positive for Ki-67 expression.  Finally, heterogeneous sentinel lymph node enhancement was associated with metastatic infiltration.

Although the authors suggested their study cohort was rather small, their findings correlated well with histopathological and prognostic factors used in the initial diagnosis and therapy of patients with breast cancer, the most common cancer in women. 

Breast MRI Improves depiction of DCIS Components

A study by Kuhl et al published in Radiology found magnetic resonance imaging (MRI) of the breast improves depiction of ductal carcinoma in situ (DCIS) components of invasive breast cancers prior to surgery.

The authors performed a prospective two-center study of 593 patients with biopsy proven invasive breast cancer that underwent breast MRI in addition to conventional imaging. 

The outcomes showed surgical-pathologic evaluation demonstrated DCIS components in 139 (23%) women. MRI had significantly higher sensitivity in the diagnosis of DCIS components pre-operatively in (84%; 118 women out of 139) than that of conventional imaging, which detected (36%; 51 of the 139 women). More than 50 percent of DCIS components were detected with MRI alone. 

The researchers also found the sensitivity benefits of MR imaging over conventional imaging improved with increasing relative size, and with increasing nuclear grade of DCIS components. Positive margin rates were generally low and did not diverge significantly between the 139 women with DCIS components versus the 454 women who lacked them.

The authors concluded that breast MRI improves depiction of DCIS components of invasive breast cancers before surgery.

Breast MRI Without Contrast Detects Cancer.

According to a study published in Radiology a radiomics technique that is based on diffusion weighted imaging with an adapted kurtosis model reduces false-positive results in both malignant and benign breast lesions when compared with x-ray mammography.

This institutional study included 222 women at two study sites (site 1: training set of 95 patients; mean age of 58.6 years; with 61 malignant and 34 benign lesions; and site 2: independent set of 127 patients; mean age, 58.2 years with 61 malignant with 66 benign lesions). 

Among all 222 patients, histopathology results confirmed malignant lesions in 122 women (55%); invasive ductal carcinoma was the most common finding, in 90 patients (74%). Benign lesions were found in the remaining 100 women (45%); fibrosis (21 patients, 21%) and fibroadenoma (20 patients, 20%) were the most common abnormalities.

All women presented with findings suspicious for cancer at x-ray mammography (BI-RADS 4 or 5) and an indication for biopsy. Before biopsy, diffusion-weighted MR imaging (b values, 0–1500 sec/mm²) was performed by using 1.5-T imagers from different MR imaging vendors. Lesions were segmented and voxel-based kurtosis fitting adapted to account for fat signal contamination was performed. Conventional interpretations of MR imaging were also assessed for comparison.

The kurtosis radiomics model reduced false-positive results from 66 to 20 (specificity 70.0% [46 of 66]) at the predefined sensitivity of greater than 98.0% [60 of 61] in the independent test set, with BI-RADS 4a and 4b lesions benefiting from the analysis (specificity 74.0%, [37 of 50]; 60.0% [nine of 15]) and BI-RADS 5 lesions showing no added benefit. The model significantly improved specificity compared with the median apparent diffusion coefficient (P < .001) and apparent kurtosis coefficient (P = .02) alone. Conventional reading of dynamic contrast material–enhanced MR imaging provided sensitivity of 91.8% (56 of 61) and a specificity of 74.2% (49 of 66). Accounting for fat signal intensity during fitting significantly improved the area under the curve of the model (P = .001).

 The authors concluded the radiomics model based on kurtosis diffusion-weighted imaging allowed for reliable differentiation between malignant and benign breast lesions.

Breast MRI is the study of choice for women at high risk for breast cancer.

According to a study published in Radiology Lo et al reviewed the outcomes of 3,934 screening breast studies (MRI and mammograms) performed on 1,249 high-risk women. A total of 45 cancers (33 invasive and 12 ductal carcinomas in situ) were diagnosed, 43 were seen with MR imaging and 14 with both mammography and MR imaging.   The cancer detection rate for MR imaging was 21.8 cancers per 1000 examinations and that for mammography was 7.2 cancers per 1000 examinations. Sensitivity and specificity of MR imaging were 96% and 78% respectively, and those of mammography were 31% and 89%, respectively (P < .001).  The researchers reported that all cancers found at screening mammography were also detected on breast MRI.  

The researchers concluded that annual screening mammography adds no value to women that are at high risk for breast cancer especially since they are screened each year with breast MRI.

How effective is screening mammography?

A study by Welch et al published in New Engl J Med added to the growing evidence that for every woman who was helped by screening mammography many have been harmed.

The authors used data from the SEER program from 1975 through 2012. They calculated the size-specific cancer case fatality rate in women 40 year old and older for two time periods: a baseline period before the implementation of widespread screening mammography (1975 through 1979) and a period encompassing 10 years (2000 through 2002).

Screening did result in more cancers being detected, with invasive tumors measuring <2 cm or in situ carcinomas increased from 36% to 68%; and the detected tumors that were large, which were invasive cancers and measuring ≥2 cm decreased from 64% to 32%.  Surprisingly though the data suggest that only about 30 of the 162 additional small tumors per 100,000 women that screening mammograms found would ever have progressed to a dangerous stage. That means that 132, or 81 percent, of the 162 extra tumors detected represented overdiagnosis, that resulted to treatment of tumors that were never destined to harm.

Most importantly, the incidence of metastatic cancer, which is the type that causes most deaths, was flat. 

In conclusion the findings from this study indicate that screening mammography finds many small cancers the dogma that all will grow, metastasize resulting in fatality is questionable. The authors also suggest that although screening may decrease cancer mortality as reflected by the declining incidence of larger tumors, they believe that the two thirds reduction in breast cancer mortality is due to recent advances in treatment. 

N Engl J Med 2016; 375:1438-1447

Preoperative Breast MRI detects additional cancers

A paper by Bae et al published in Radiology indicates that preoperative MRI in women whose breast cancer was detected by ultrasound found additional cancers.

The study was a retrospective review of 374 women, median age, 48 years, with breast cancer detected at screening ultrasound.

Of 374 women, 21 or 5.6% patients were diagnosed with additional cancer.  In premenopausal women with invasive breast cancer and in those with index invasive lobular histologic type had higher incidence of additional cancer detected at MR imaging.  Premenopausal status also put the women at risk.

The authors concluded that preoperative MRI detected additional sites of cancer in women with breast cancer detected at screening ultrasound.

Tailoring Screening Mammography

Research by Trentham-Dietz et al published in the Annals of Internal Medicine recommends that frequency of screening for breast cancer should be based on well-recognized risk factors.

The researchers who were breast-cancer epidemiologists and cancer modelers combined data-collection and conducted simulation modeling using national data for incidence, breast density, and screening performance.

It is an accepted fact that screening benefits and overdiagnosis increase with breast density and relative risk (RR) while false-positive mammograms and benign results on biopsy decrease with increasing risk.

Among women with fatty breasts and an RR of 1.0 or 1.3, breast cancer deaths averted were similar for triennial versus biennial screening for both age groups (50 to 74 years, median of 3.4 to 5.1 vs. 4.1 to 6.5 deaths averted; 65 to 74 years, median of 1.5 to 2.1 vs. 1.8 to 2.6 deaths averted).

Breast cancer deaths averted increased with annual versus biennial screening for women aged 50 to 74 years at all levels of breast density and an RR of 4.0, and those aged 65 to 74 years with heterogeneously or extremely dense breasts and an RR of 4.0. However, harms were almost 2-fold higher.

Triennial screening for the average-risk subgroup and annual screening for the highest-risk subgroup cost less than $100 000 per quality-adjusted life year (QALY) gained which is a value for money of medical interventions.

The authors concluded that average-risk women older than 50 without dense breasts should undergo triennial screening while higher-risk women with very dense breasts should receive annual mammograms.  Such frequency and tailoring of screening mammography will maintain a similar or better balance of benefits and harms than average-risk women receiving annual or biennial screening.

Breast density assessment varies among radiologists

Sprangue et al published in Annals of Internal Medicine the findings of their study that suggests that radiologists often do not agree on what qualifies as dense breast.

The investigators looked at 216,783 mammograms from 145,123 women aged 40 to 89 years that were interpreted by 83 radiologists in 30 radiology facilities in 4 States.

Overall, 36.9% of mammograms were rated as showing dense breasts. Across radiologists, this percentage ranged from 6.3% to 84.5% (median, 38.7%). Examination of patient subgroups revealed that variation in density assessment across radiologists was pervasive in all but the most extreme patient age and BMI combinations. Among women who had consecutive mammograms interpreted by different radiologists, 17.2% (5909 of 34 271) of them suggested different density rating on the two tests.

The authors concluded because there is wide variation in density assessment across radiologists it is a fact should be carefully considered by providers and policymakers when considering supplemental screening strategies.