Predicting Biopsy Outcome After Mammography: What Is the Likelihood the Patient Has Invasive or In Situ Breast Cancer?

Clinically significant bleeding complications of percutaneous breast biopsy: 10-year analysis and a proposed management algorithm

PURPOSE

Bleeding is a well-known risk of percutaneous breast biopsy, frequently controlled with manual pressure. However, significant bleeding complications may require further evaluation or intervention. Our objectives were to assess the rate, type, and periprocedural management of significant bleeding following percutaneous breast biopsy and to evaluate the success of any interventions.

METHODS

We retrospectively reviewed percutaneous breast biopsies at our institution over a 10-year period with documented post-biopsy bleeding complications in radiology reports. Patients were included if bleeding required intervention (interventional radiology [IR], surgery, or other), imaging follow-up, or clinical evaluation for symptoms. Additional data included patient demographics, anticoagulation, history of bleeding diathesis, biopsy details, bleeding symptoms, histopathology, and intervention details, if applicable.

RESULTS

Of 5820 unique patients who underwent percutaneous biopsy, 66 patients (66/5820; 1.1%) comprising 71 biopsy cases met inclusion for clinically significant bleeding with 5/71(7.0%) requiring surgery, 9/71(12.7%) requiring IR intervention, and 57/71(80.3%) requiring lower-acuity intervention including prolonged observation (5/57;7.0%), overnight admission (4/57;5.6%), aspiration (4/57;5.6%), lidocaine and suture (2/57;2.8%), primary care visit (7/57;10.0%), blood transfusion (1/57;1.4%), emergency room visit (6/57;8.5%), surgery consult (8/57;11.3%), IR consult (2/57;2.8%), and follow-up imaging (22/57;31.0%). Most patients requiring intervention by surgery or IR had acute signs of bleeding immediately after biopsy while most patients with delayed signs of bleeding required lower-acuity interventions.

CONCLUSION

Clinically significant bleeding is extremely rare after percutaneous breast biopsy and is most often managed non-surgically. Developing an institutional algorithm for management of bleeding complications that consults IR before surgery may help decrease the number of patients managed surgically.

Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline

In this study, we propose the Ductal Instance-Oriented Pipeline (DIOP) that contains a duct-level instance segmentation model, a tissue-level semantic segmentation model, and three-levels of features for diagnostic classification. Based on recent advancements in instance segmentation and the Mask RCNN model, our duct-level segmenter tries to identify each ductal individual inside a microscopic image; then, it extracts tissue-level information from the identified ductal instances. Leveraging three levels of information obtained from these ductal instances and also the histopathology image, the proposed DIOP outperforms previous approaches (both feature-based and CNN-based) in all diagnostic tasks; for the four-way classification task, the DIOP achieves comparable performance to general pathologists in this unique dataset. The proposed DIOP only takes a few seconds to run in the inference time, which could be used interactively on most modern computers. More clinical explorations are needed to study the robustness and generalizability of this system in the future.

Quantifying predictive capability of electronic health records for the most harmful breast cancer

Improved prediction of the "most harmful" breast cancers that cause the most substantive morbidity and mortality would enable physicians to target more intense screening and preventive measures at those women who have the highest risk; however, such prediction models for the "most harmful" breast cancers have rarely been developed. Electronic health records (EHRs) represent an underused data source that has great research and clinical potential. Our goal was to quantify the value of EHR variables in the "most harmful" breast cancer risk prediction. We identified 794 subjects who had breast cancer with primary non-benign tumors with their earliest diagnosis on or after 1/1/2004 from an existing personalized medicine data repository, including 395 "most harmful" breast cancer cases and 399 "least harmful" breast cancer cases. For these subjects, we collected EHR data comprised of 6 components: demographics, diagnoses, symptoms, procedures, medications, and laboratory results. We developed two regularized prediction models, Ridge Logistic Regression (Ridge-LR) and Lasso Logistic Regression (Lasso-LR), to predict the "most harmful" breast cancer one year in advance. The area under the ROC curve (AUC) was used to assess model performance. We observed that the AUCs of Ridge-LR and Lasso-LR models were 0.818 and 0.839 respectively. For both the Ridge-LR and Lasso-LR models, the predictive performance of the whole EHR variables was significantly higher than that of each individual component (p<0.001). In conclusion, EHR variables can be used to predict the "most harmful" breast cancer, providing the possibility to personalize care for those women at the highest risk in clinical practice.

How Many of the Biopsy Decisions Taken at Inexperienced Breast Radiology Units Were Correct?

OBJECTIVE

In this study, we aimed to determine the need for biopsy in patients referred from other clinics for the performance of biopsy with the suspicion of breast cancer.

MATERIALS AND METHODS

112 patients were included in the study. It was decided that their biopsies be performed following examinations in other clinics and they presented to the breast radiology unit of our hospital for a second opinion. The demographic characteristics, diagnostic studies completed in the other centers, properties of lesions, decision made as a result of examinations and BI-RADS (Breast Imaging Reporting and Data Systems) categorizations were recorded on the registration forms of the study patients. In addition, the quality of examinations, reasons of repeat tests, additional tests features and the last decision of our clinic were documented. The obtained data were analyzed in terms of re-examination, additional tests and change in the biopsy decision. Changes in the biopsy decisions for patients were specifically inquired.

RESULTS

The biopsy decisions were cancelled in our breast radiology unit for 63 out of 112 patients (56.3%) whose biopsy decisions were made at an external institute. For 42 patients, examinations made by the other clinics were deemed adequate, yet there was no need for biopsy in 22 of them. The biopsy decisions were cancelled for 27 out of 47 patients (57.4%) with repeat examination and 18 out of 28 patients (64.3%) with additional tests because of the insufficient test quality.

CONCLUSION

Incorrect, inadequate breast screening and false positivity were higher at inexperienced institutes.

Relationships between mammographic density, tissue microvessel density, and breast biopsy diagnosis

BACKGROUND

Women with high levels of mammographic density (MD) have a four- to six-fold increased risk of developing breast cancer; however, most neither have a prevalent tumor nor will they develop one. Magnetic resonance imaging (MRI) studies suggest that background parenchymal enhancement, an indicator of vascularity, is related to increased breast cancer risk. Correlations of microvessel density (MVD) in tissue, MD and biopsy diagnosis have not been defined, and we investigated these relationships among 218 women referred for biopsy.

METHODS

MVD was determined by counting CD31-positive vessels in whole sections of breast biopsies in three representative areas; average MVD was transformed to approximate normality. Using digital mammograms, we quantified MD volume with single X-ray absorptiometry. We used linear regression to evaluate associations between MVD and MD adjusted for age and body mass index (BMI) overall, and stratified by biopsy diagnosis: cases (in situ or invasive cancer, n = 44) versus non-cases (non-proliferative or proliferative benign breast disease, n = 174). Logistic regression adjusted for age, BMI, and MD was used to calculate odds ratios (ORs) and 95 % confidence intervals (CIs) for associations between MVD and biopsy diagnosis. We also assessed whether the MVD-breast cancer association varied by MD.

RESULTS

MVD and MD were not consistently associated. Higher MVD was significantly associated with higher odds of in situ/invasive disease (ORAdjusted = 1.69, 95 % CI = 1.17-2.44). MVD-breast cancer associations were strongest among women with greater non-dense volume.

CONCLUSIONS

Increased MVD in tissues is associated with breast cancer, independently of MD, consistent with MRI findings suggestive of its possible value as a radiological cancer biomarker.

The use of the Gail model, body mass index and SNPs to predict breast cancer among women with abnormal (BI-RADS 4) mammograms

Introduction

Mammography screening results in a significant number of false-positives. The use of pretest breast cancer risk factors to guide follow-up of abnormal mammograms could improve the positive predictive value of screening. We evaluated the use of the Gail model, body mass index (BMI), and genetic markers to predict cancer diagnosis among women with abnormal mammograms. We also examined the extent to which pretest risk factors could reclassify women without cancer below the biopsy threshold.

Methods

We recruited a prospective cohort of women referred for biopsy with abnormal (BI-RADS 4) mammograms according to the American College of Radiology’s Breast Imaging-Reporting and Data System (BI-RADS). Breast cancer risk factors were assessed prior to biopsy. A validated panel of 12 single-nucleotide polymorphisms (SNPs) associated with breast cancer were measured. Logistic regression was used to assess the association of Gail risk factors, BMI and SNPs with cancer diagnosis (invasive or ductal carcinoma in situ). Model discrimination was assessed using the area under the receiver operating characteristic curve, and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. The distribution of predicted probabilities of a cancer diagnosis were compared for women with or without breast cancer.

Results

In the multivariate model, age (odds ratio (OR) = 1.05; 95% confidence interval (CI), 1.03 to 1.08; P < 0.001), SNP panel relative risk (OR = 2.30; 95% CI, 1.06 to 4.99, P = 0.035) and BMI (≥30 kg/m² versus <25 kg/m²; OR = 2.20; 95% CI, 1.05 to 4.58; P = 0.036) were significantly associated with breast cancer diagnosis. Older women were more likely than younger women to be diagnosed with breast cancer. The SNP panel relative risk remained strongly associated with breast cancer diagnosis after multivariable adjustment. Higher BMI was also strongly associated with increased odds of a breast cancer diagnosis. Obese women (OR = 2.20; 95% CI, 1.05 to 4.58; P = 0.036) had more than twice the odds of cancer diagnosis compared to women with a BMI <25 kg/m². The SNP panel appeared to have predictive ability among both white and black women.

Conclusions

Breast cancer risk factors, including BMI and genetic markers, are predictive of cancer diagnosis among women with BI-RADS 4 mammograms. Using pretest risk factors to guide follow-up of abnormal mammograms could reduce the burden of false-positive mammograms.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-014-0509-4) contains supplementary material, which is available to authorized users.

Breast cancer detection with short-interval follow-up compared with return to annual screening in patients with benign stereotactic or US-guided breast biopsy results

PURPOSE

To compare the cancer detection rate and stage after benign stereotactic or ultrasonography (US)-guided core breast biopsy between patients with short-interval follow-up (SIFU) and those who return to annual screening.

MATERIALS AND METHODS

The Breast Cancer Surveillance Consortium (BCSC) registry and the BCSC Statistical Coordinating Center received institutional review board approval for active and passive consent processes and a waiver of consent. All procedures were HIPAA compliant. BCSC data for 1994-2010 were used to compare ipsilateral breast cancer detection rates and tumor characteristics for diagnoses within 3 months after SIFU (3-8 months) versus return to annual screening (RTAS) mammography (9-18 months) after receiving a benign pathology result from image-guided breast biopsy.

RESULTS

In total, 17 631 biopsies with benign findings were identified with SIFU or RTAS imaging. In the SIFU group, 27 ipsilateral breast cancers were diagnosed in 10 715 mammographic examinations (2.5 cancers per 1000 examinations) compared with 16 cancers in 6916 mammographic examinations in the RTAS group (2.3 cancers per 1000 examinations) (P = .88). Sixteen cancers after SIFU (59%; 95% confidence interval [CI]: 39%, 78%) were invasive versus 12 after RTAS (75%; 95% CI: 48%, 93%). The invasive cancer rate was 1.5 per 1000 examinations after SIFU (95% CI: 0.9, 2.4) and 1.7 per 1000 examinations (95% CI: 0.9, 3.0) after RTAS (P = .70). Among invasive cancers, 25% were late stage (stage 2B, 3, or 4) in the SIFU group (95% CI: 7%, 52%) versus 27% in the RTAS group (95% CI: 6%, 61%). Positive lymph nodes were found in seven (44%; 95% CI: 20%, 70%) invasive cancers after SIFU and in three (25%; 95% CI: 5%, 57%) invasive cancers after RTAS.

CONCLUSION

Similar rates of cancer detection were found between SIFU and RTAS after benign breast biopsy with no significant differences in stage, tumor size, or nodal status, although the present study was limited by sample size. These findings suggest that patients with benign radiologic-pathologic-concordant percutaneous breast biopsy results could return to annual screening.

Comparison of mammographic density assessed as volumes and areas among women undergoing diagnostic image-guided breast biopsy

BACKGROUND

Mammographic density (MD), the area of non-fatty-appearing tissue divided by total breast area, is a strong breast cancer risk factor. Most MD analyses have used visual categorizations or computer-assisted quantification, which ignore breast thickness. We explored MD volume and area, using a volumetric approach previously validated as predictive of breast cancer risk, in relation to risk factors among women undergoing breast biopsy.

METHODS

Among 413 primarily white women, ages 40 to 65 years, undergoing diagnostic breast biopsies between 2007 and 2010 at an academic facility in Vermont, MD volume (cm(3)) was quantified in craniocaudal views of the breast contralateral to the biopsy target using a density phantom, whereas MD area (cm(2)) was measured on the same digital mammograms using thresholding software. Risk factor associations with continuous MD measurements were evaluated using linear regression.

RESULTS

Percent MD volume and area were correlated (r = 0.81) and strongly and inversely associated with age, body mass index (BMI), and menopause. Both measures were inversely associated with smoking and positively associated with breast biopsy history. Absolute MD measures were correlated (r = 0.46) and inversely related to age and menopause. Whereas absolute dense area was inversely associated with BMI, absolute dense volume was positively associated.

CONCLUSIONS

Volume and area MD measures exhibit some overlap in risk factor associations, but divergence as well, particularly for BMI.

IMPACT

Findings suggest that volume and area density measures differ in subsets of women; notably, among obese women, absolute density was higher with volumetric methods, suggesting that breast cancer risk assessments may vary for these techniques.

Predicting invasive breast cancer versus DCIS in different age groups

Background

Increasing focus on potentially unnecessary diagnosis and treatment of certain breast cancers prompted our investigation of whether clinical and mammographic features predictive of invasive breast cancer versus ductal carcinoma in situ (DCIS) differ by age.

Methods

We analyzed 1,475 malignant breast biopsies, 1,063 invasive and 412 DCIS, from 35,871 prospectively collected consecutive diagnostic mammograms interpreted at University of California, San Francisco between 1/6/1997 and 6/29/2007. We constructed three logistic regression models to predict the probability of invasive cancer versus DCIS for the following groups: women ≥ 65 (older group), women 50–64 (middle age group), and women < 50 (younger group). We identified significant predictors and measured the performance in all models using area under the receiver operating characteristic curve (AUC).

Results

The models for older and the middle age groups performed significantly better than the model for younger group (AUC = 0.848 vs, 0.778; p = 0.049 and AUC = 0.851 vs, 0.778; p = 0.022, respectively). Palpability and principal mammographic finding were significant predictors in distinguishing invasive from DCIS in all age groups. Family history of breast cancer, mass shape and mass margins were significant positive predictors of invasive cancer in the older group whereas calcification distribution was a negative predictor of invasive cancer (i.e. predicted DCIS). In the middle age group—mass margins, and in the younger group—mass size were positive predictors of invasive cancer.

Conclusions

Clinical and mammographic finding features predict invasive breast cancer versus DCIS better in older women than younger women. Specific predictive variables differ based on age.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2407-14-584) contains supplementary material, which is available to authorized users.

Ductal Carcinoma In Situ of the Breast

Ductal carcinoma in situ (DCIS) of the breast is a potentially invasive neoplasm. Risk factors include high estrogen states such as use of oral contraceptive (OC) pills, nulliparity, advanced age at first birth, and also family history and genetic mutations. The incidence of this usually clinically silent condition has risen in the past few decades due to widespread screening and diagnostic mammography, with final diagnosis confirmed by biopsy. At present, treatment options include total or simple mastectomy or lumpectomy with radiation. Adjuvant therapy includes antiestrogens like tamoxifen and human epidermal growth factor receptor 2 (HER2) suppression therapy. With the latest advances in chemotherapy and better understanding on the pathogenesis of the lesion, it is anticipated that more effective modalities of treatment may soon be available.

Risk factors of breast cancer: a systematic review and meta-analysis

The etiology of breast cancer might be explained by 2 mechanisms, namely, differentiation and proliferation of breast epithelial cells mediated by hormonal factors. We performed a systematic review and meta-analysis to update effects of risk factors for both mechanisms. MEDLINE and EMBASE were searched up to January 2011. Studies that assessed association between oral contraceptives (OC), hormonal replacement therapy (HRT), diabetes mellitus (DM), or breastfeeding and breast cancer were eligible. Relative risks with their confidence intervals (CIs) were extracted. A random-effects method was applied for pooling the effect size. The pooled odds ratios of OC, HRT, and DM were 1.10 (95% CI = 1.03-1.18), 1.23 (95% CI = 1.21-1.25), and 1.14 (95% CI = 1.09-1.19), respectively, whereas the pooled odds ratio of ever-breastfeeding was 0.72 (95% CI = 0.58-0.89). Our study suggests that OC, HRT, and DM might increase risks, whereas breastfeeding might lower risks of breast cancer.

Development of a diagnostic test set to assess agreement in breast pathology: practical application of the Guidelines for Reporting Reliability and Agreement Studies (GRRAS)

Background

Diagnostic test sets are a valuable research tool that contributes importantly to the validity and reliability of studies that assess agreement in breast pathology. In order to fully understand the strengths and weaknesses of any agreement and reliability study, however, the methods should be fully reported. In this paper we provide a step-by-step description of the methods used to create four complex test sets for a study of diagnostic agreement among pathologists interpreting breast biopsy specimens. We use the newly developed Guidelines for Reporting Reliability and Agreement Studies (GRRAS) as a basis to report these methods.

Methods

Breast tissue biopsies were selected from the National Cancer Institute-funded Breast Cancer Surveillance Consortium sites. We used a random sampling stratified according to woman’s age (40–49 vs. ≥50), parenchymal breast density (low vs. high) and interpretation of the original pathologist. A 3-member panel of expert breast pathologists first independently interpreted each case using five primary diagnostic categories (non-proliferative changes, proliferative changes without atypia, atypical ductal hyperplasia, ductal carcinoma in situ, and invasive carcinoma). When the experts did not unanimously agree on a case diagnosis a modified Delphi method was used to determine the reference standard consensus diagnosis. The final test cases were stratified and randomly assigned into one of four unique test sets.

Conclusions

We found GRRAS recommendations to be very useful in reporting diagnostic test set development and recommend inclusion of two additional criteria: 1) characterizing the study population and 2) describing the methods for reference diagnosis, when applicable.

Can the Gail model increase the predictive value of a positive mammogram in a European population screening setting? Results from a Spanish cohort

AIMS OF THE STUDY

The Gail Model (GM) is the most well-known model to assess the individual risk of breast cancer (BC). Although its discriminatory accuracy is low in the clinical context, its usefulness in the screening setting is not well known. The aim of this study is to assess the utility of the GM in a European screening program.

METHODS

Retrospective cohort study of 2200 reassessed women with information on the GM available in a BC screening program in Barcelona, Spain. The 5 year-risk of BC applying the GM right after the screening mammogram was compared first with the actual woman's risk of BC in the same screening round and second with the BC risk during the next 5 years.

RESULTS

The curves of BC Gail risk overlapped for women with and without BC, both in the same screening episode as well as 5 years afterward. Overall sensitivity and specificity in the same screening episode were 22.3 and 86.5%, respectively, and 46.2 and 72.1% 5 years afterward. ROC curves were barely over the diagonal and the concordance statistics were 0.59 and 0.61, respectively.

CONCLUSION

The GM has very low accuracy among women with a positive mammogram result, predicting BC both in the concomitant episode and 5 years later. Our results do not encourage the use of the GM in the screening context to aid the referral decision or the type of procedures after a positive mammogram or to identify women at high risk among those with a false-positive outcome.

Ductal carcinoma in situ of the breast: a surgical perspective

Ductal carcinoma in situ (DCIS) of the breast is a heterogeneous neoplasm with invasive potential. Risk factors include age, family history, hormone replacement therapy, genetic mutation, and patient lifestyle. The incidence of DCIS has increased due to more widespread use of screening and diagnostic mammography; almost 80% of cases are diagnosed with imaging with final diagnosis established by biopsy and histological examination. There are various classification systems used for DCIS, the most recent of which is based on the presence of intraepithelial neoplasia of the ductal epithelium (DIN). A number of molecular assays are now available that can identify high-risk patients as well as help establish the prognosis of patients with diagnosed DCIS. Current surgical treatment options include total mastectomy, simple lumpectomy in very low-risk patients, and lumpectomy with radiation. Adjuvant therapy is tailored based on the molecular profile of the neoplasm and can include aromatase inhibitors, anti-estrogen, anti-progesterone (or a combination of antiestrogen and antiprogesterone), and HER2 neu suppression therapy. Chemopreventive therapies are under investigation for DCIS, as are various molecular-targeted drugs. It is anticipated that new biologic agents, when combined with hormonal agents such as SERMs and aromatase inhibitors, may one day prevent all forms of breast cancer.

Clinical, demographic, and situational factors linked to distress associated with benign breast biopsy

Benign breast biopsy (BBB) can be distressing for many women. Few studies have examined specific aspects of the BBB more or less distressing or risk factors for distress. Women (N = 51) who had a recent BBB reported the magnitude of distress associated with specific aspects of their experience. Clinical and demographic variables were also examined as risk factors for distress. All women reported some distress associated with the BBB with one third reporting their experience was "very stressful." Generally, biopsy-specific events were more distressing than follow-up mammography. Distress risk factors included younger age, less education, nonsurgical biopsy, and no family history of breast cancer. Clinical efforts to better manage biopsy-related distress are warranted. The authors identified clinical and demographic risk factors that furnish a simple, efficient, and potentially cost-effective means of stratifying risk for distress in the breast biopsy setting.

Probabilistic computer model developed from clinical data in national mammography database format to classify mammographic findings

PURPOSE

To determine whether a Bayesian network trained on a large database of patient demographic risk factors and radiologist-observed findings from consecutive clinical mammography examinations can exceed radiologist performance in the classification of mammographic findings as benign or malignant.

MATERIALS AND METHODS

The institutional review board exempted this HIPAA-compliant retrospective study from requiring informed consent. Structured reports from 48 744 consecutive pooled screening and diagnostic mammography examinations in 18 269 patients from April 5, 1999 to February 9, 2004 were collected. Mammographic findings were matched with a state cancer registry, which served as the reference standard. By using 10-fold cross validation, the Bayesian network was tested and trained to estimate breast cancer risk by using demographic risk factors (age, family and personal history of breast cancer, and use of hormone replacement therapy) and mammographic findings recorded in the Breast Imaging Reporting and Data System lexicon. The performance of radiologists compared with the Bayesian network was evaluated by using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.

RESULTS

The Bayesian network significantly exceeded the performance of interpreting radiologists in terms of AUC (0.960 vs 0.939, P = .002), sensitivity (90.0% vs 85.3%, P < .001), and specificity (93.0% vs 88.1%, P < .001).

CONCLUSION

On the basis of prospectively collected variables, the evaluated Bayesian network can predict the probability of breast cancer and exceed interpreting radiologist performance. Bayesian networks may help radiologists improve mammographic interpretation.

Prospective breast cancer risk prediction model for women undergoing screening mammography

BACKGROUND

Risk prediction models for breast cancer can be improved by the addition of recently identified risk factors, including breast density and use of hormone therapy. We used prospective risk information to predict a diagnosis of breast cancer in a cohort of 1 million women undergoing screening mammography.

METHODS

There were 2,392,998 eligible screening mammograms from women without previously diagnosed breast cancer who had had a prior mammogram in the preceding 5 years. Within 1 year of the screening mammogram, 11,638 women were diagnosed with breast cancer. Separate logistic regression risk models were constructed for premenopausal and postmenopausal examinations by use of a stringent (P<.0001) criterion for the inclusion of risk factors. Risk models were constructed with 75% of the data and validated with the remaining 25%. Concordance of the predicted with the observed outcomes was assessed by a concordance (c) statistic after logistic regression model fit. All statistical tests were two-sided.

RESULTS

Statistically significant risk factors for breast cancer diagnosis among premenopausal women included age, breast density, family history of breast cancer, and a prior breast procedure. For postmenopausal women, the statistically significant factors included age, breast density, race, ethnicity, family history of breast cancer, a prior breast procedure, body mass index, natural menopause, hormone therapy, and a prior false-positive mammogram. The model may identify high-risk women better than the Gail model, although predictive accuracy was only moderate. The c statistics were 0.631 (95% confidence interval [CI] = 0.618 to 0.644) for premenopausal women and 0.624 (95% CI = 0.619 to 0.630) for postmenopausal women.

CONCLUSION

Breast density is a strong additional risk factor for breast cancer, although it is unknown whether reduction in breast density would reduce risk. Our risk model may be able to identify women at high risk for breast cancer for preventive interventions or more intensive surveillance.