BREASTAID: Clinical results from early development of a clinical decision rule for palpable solid breast masses

Value of Ultrasound in the Detection of Benign and Malignant Breast Diseases: A Diagnostic Accuracy Study

Objectives

We sought to determine the diagnostic accuracy of ultrasound for benign and malignant breast lesions.

Methods

This retrospective study was performed to evaluate the diagnostic accuracy of ultrasound in 203 patients with complete medical records who visited Mehr Medical Imaging Center for breast ultrasound between March 2014 and February 2016. The collected data comprised of demographic characteristics, ultrasound results (consisting of the anatomic area of the lesion, the involved side, and the ultrasound characteristics of the lesion), mammogram results, and pathology reports (if surgery or biopsy was performed).

Results

For the diagnosis of malignant and benign lesions, ultrasound had a sensitivity of 93.9% and specificity of 86.5%; its positive and negative predictive values were 86.9% and 93.8%, respectively. Lesion type was significantly associated with a family history of breast cancer and fertility status (p < 0.005), but there was no significant association between the involved side and tumor type (p > 0.050).

Conclusions

Mammography is the best technique for screening and identifying patients with non-mass-like breast lesions and microcalcifications. Considering the false positive and false-negative results, ultrasound is not a perfect screening modality. Future studies are recommended to study the value of ultrasound in the detection of high-risk breast cancer patients.

Tumor Staging and Grading: A Primer

Cancer staging and grading are used to predict the clinical behavior of malignancies, establish appropriate therapies, and facilitate exchange of precise information between clinicians. The internationally accepted criterion for cancer staging, the tumor-node-metastasis (TNM) system, includes: (1) tumor size and local growth (T), (2) extent of lymph node metastases (N), and (3) occurrence of distant metastases (M). Clinical stage is established before initiation of therapy and is determined by physical examination, laboratory findings, and imaging studies. Pathologic stage is determined following surgical exploration of disease and histologic examination of tissue. The TNM classification system has evolved over 70 years to accommodate increasing knowledge about cancer biology. Molecular technologies such as genomic and proteomic profiling of tumors could eventually be incorporated into the TNM staging system. This chapter describes the current TNM system using breast, lung, ovarian, and prostate cancer examples.

Joint confidence region estimation for area under ROC curve and Youden index

In the field of diagnostic studies, the area under the ROC curve (AUC) serves as an overall measure of a biomarker/diagnostic test's accuracy. Youden index, defined as the overall correct classification rate minus one at the optimal cut-off point, is another popular index. For continuous biomarkers of binary disease status, although researchers mainly evaluate the diagnostic accuracy using AUC, for the purpose of making diagnosis, Youden index provides an important and direct measure of the diagnostic accuracy at the optimal threshold and hence should be taken into consideration in addition to AUC. Furthermore, AUC and Youden index are generally correlated. In this paper, we initiate the idea of evaluating diagnostic accuracy based on AUC and Youden index simultaneously. As the first step toward this direction, this paper only focuses on the confidence region estimation of AUC and Youden index for a single marker. We present both parametric and non-parametric approaches for estimating joint confidence region of AUC and Youden index. We carry out extensive simulation study to evaluate the performance of the proposed methods. In the end, we apply the proposed methods to a real data set.

Identifying suspected breast cancer: development and validation of a clinical prediction rule

BACKGROUND

An evidence-based approach is needed to identify women with breast symptoms who are most likely to have breast cancer so that timely and appropriate referral can take place.

AIM

To report the development and validation of a clinical prediction rule for the diagnosis of breast cancer.

DESIGN AND SETTING

Cohort study with two prospective groups of women: those presenting to a symptomatic breast clinic (derivation cohort) and a separate cohort presenting to 11 general practices (validation cohort) in Tayside, Scotland.

METHOD

Regression analysis was used to derive a clinical prediction rule from presenting symptoms, personal and family history, and clinical findings. Validation consisted of estimating the number of breast cancers predicted to occur compared with the actual number of observed breast cancers across deciles of risk.

RESULTS

In the derivation cohort of 802 patients, 59 (7%) were diagnosed with breast cancer. Independent clinical predictors for breast cancer were: increasing age by year (adjusted odds ratio [AOR] 1.10, 95% confidence interval [CI] = 1.07 to 1.13); presence of a discrete lump (AOR 15.20, 95% CI = 4.88 to 47.34); breast thickening (AOR 7.64, 95% CI = 2.23 to 26.11); lymphadenopathy (AOR 3.63, 95% CI = 1.33 to 9.92); and lump ≥ 2 cm (AOR 5.41, 95% CI = 2.36 to 12.38). All eight patients with skin tethering had breast cancer. The regression model had good predictive power, identifying all five breast cancers in the validation cohort of 97 patients in the top two deciles of risk.

CONCLUSION

The clinical prediction rule discriminates between patients at high risk of breast cancer from those at low risk, and can be implemented as an evidence-based recommendation to enhance appropriate referral from general practice to a symptomatic breast clinic. Ongoing validation in further populations is required.

Predictive model of outcome of targeted nodal assessment in colorectal cancer

BACKGROUND

Improvement in staging accuracy is the principal aim of targeted nodal assessment in colorectal carcinoma. Technical factors independently predictive of false negative (FN) sentinel lymph node (SLN) mapping should be identified to facilitate operative decision making.

PURPOSE

To define independent predictors of FN SLN mapping and to develop a predictive model that could support surgical decisions.

PATIENTS AND METHODS

Data was analyzed from 2 completed prospective clinical trials involving 278 patients with colorectal carcinoma undergoing SLN mapping. Clinical outcome of interest was FN SLN(s), defined as one(s) with no apparent tumor cells in the presence of non-SLN metastases. To assess the independent predictive effect of a covariate for a nominal response (FN SLN), a logistic regression model was constructed and parameters estimated using maximum likelihood. A probabilistic Bayesian model was also trained and cross validated using 10-fold train-and-test sets to predict FN SLN mapping. Area under the curve (AUC) from receiver operating characteristics curves of these predictions was calculated to determine the predictive value of the model.

RESULTS

Number of SLNs (<3; P = 0.03) and tumor-replaced nodes (P < 0.01) independently predicted FN SLN. Cross validation of the model created with Bayesian Network Analysis effectively predicted FN SLN (area under the curve = 0.84-0.86). The positive and negative predictive values of the model are 83% and 97%, respectively.

CONCLUSION

This study supports a minimum threshold of 3 nodes for targeted nodal assessment in colorectal cancer, and establishes sufficient basis to conclude that SLN mapping and biopsy cannot be justified in the presence of clinically apparent tumor-replaced nodes.

A comparison of mammography and ultrasonography in the evaluation of breast masses

AIM

Aim of the study is to evaluate breast masses using mammography (MG) and ultrasonography (USG) independently and in combination.

MATERIALS AND METHODS

Our study group consisted of 62 female patients, with breast symptoms such as palpable lumps, pain in the breast and nipple discharge who were examined prospectively over a period of 6 months. All 62 patients were examined by both MG and USG independently. Fine needle aspiration cytology (FNAC) or core cut biopsy was done according to the findings of MG and USG and then the results were correlated with each modality finding.

RESULTS

According to this study MG showed an efficiency of 81.8 % compared to 95.5 % for USG in detecting fibrocystic mastitis. However their combined approach resulted in 100 %. In the case of fibroadenomas, MG showed 75 % efficiency and USG only 35 % and the combination resulting in 93.7 %. For carcinomas, MG had an efficiency of 77.8 % and USG 55.6 %, but the combination had an efficiency of 98.1 %. Overall, the histopathological results when correlated with each modality finding showed that MG had an efficiency of only 77.4 % and USG only 69.8 % when used alone in detecting these lesions of the breast compared to an efficiency of 98.1 % obtained by their combined approach. In our study, we showed that there was no significant difference in sensitivity between MG and USG (p = 0.3768) but there was significant difference in MG alone and MG-USG combination (p = 0.0015) and USG alone and USG-MG combination (p = 0.0001).

CONCLUSION

Our study confirmed that combined MG and USG had higher sensitivity rate than the sensitivity rate observed for either single modality. The diagnostic accuracy for carcinomas of the breast appear to improve when MG was combined with USG, even in cases which showed no evidence of microcalcification or other signs of abnormalities. Our study implies that, USG may be the only viable modality in pregnant and lactating women as it does not involve ionizing radiation and also in dense breast tissue, as density is a limiting factor for MG.

Guidelines to assure quality in breast cancer surgery

The outcome of breast cancer surgery, with respect to cosmetic results, loco regional control and prognostic information from nodal staging, may vary substantially. Optimal breast cancer care starts with a proper surgical act, which can only be performed when optimal imaging and preoperative diagnosis are available. Next, on the basis of all peroperative findings, the right surgical procedure should be indicated after multidisciplinary consultation and discussion, keeping the objective of the final outcome in mind. The surgical act itself is best performed by an experienced surgeon who has maintained their experience after sufficient training. The outcome of the different procedures can be measured according to simple criteria and prospective registration. All possible surgical procedures, the indication-objectives, the training-objectives and outcome measures are described.

Development of a clinical decision rule for triage of women with palpable breast masses

BACKGROUND AND OBJECTIVE

Given the large numbers of open breast biopsies performed in women who have benign breast masses, we developed a clinical decision rule (CDR), called BREASTAID, to triage women into open biopsy or follow-up.

METHODS

A prospective cohort design was used to obtain data on 452 palpable breast masses evaluated at a referral clinic. Breast cancer was defined as ductal carcinoma in situ or invasive cancer at open biopsy. Separate logistic regression models were developed at three logical stages of the clinical workup. Bayes' theorem was applied in a stepwise fashion to revise model probabilities to generate a final probability of cancer. Receiver operator characteristics curves were generated to determine the optimum cut-point. Results derived from the CDR were compared with actual clinical practice.

RESULTS

A total of 452 masses in 380 women were included. Clinical practice resulted in 180 masses (39.8%) undergoing open biopsy, 41 (22.8%) of which were cancers. Age, history of breast cancer in the mother, mass size, mammography findings, and fine needle aspiration biopsy results were included in the final models. When applied to the derivation dataset, BREASTAID successfully identified 40 of 41 cancer masses (sensitivity 97.6%, 95% confidence interval [CI] 94.1-99.9), and 350 of 411 noncancer masses (specificity 85.2%, 95% CI 81.8-88.5). BREASTAID would have reduced the number of biopsies performed on the 411 benign masses from 139 to 61.

CONCLUSIONS

This study demonstrated that a CDR based on routinely collected clinical variables has the potential to accurately triage women with palpable breast masses. Further validation of the rule is required before its clinical use can be considered.