A prediction model for underestimation of invasive breast cancer after a biopsy diagnosis of ductal carcinoma in situ: based on 2892 biopsies and 589 invasive cancers

Deep-learning model to improve histological grading and predict upstaging of atypical ductal hyperplasia / ductal carcinoma in situ on breast biopsy

AIMS

Risk stratification of atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS), diagnosed using breast biopsy, has great clinical significance. Clinical trials are currently exploring the possibility of active surveillance for low-risk lesions, whereas axillary lymph node staging may be considered during surgical planning for high-risk lesions. We aimed to develop a machine-learning algorithm based on whole-slide images of breast biopsy specimens and clinical information to predict the risk of upstaging to invasive breast cancer after wide excision.

METHODS AND RESULTS

Patients diagnosed with ADH/DCIS on breast biopsy were included in this study, comprising 592 (740 slides) and 141 (198 slides) patients in the development and independent testing cohorts, respectively. Histological grading of the lesions was independently evaluated by two pathologists. Clinical information, including biopsy method, lesion size, and Breast Imaging Reporting and Data System (BI-RADS) classification of ultrasound and mammograms, were collected. Deep DCIS consisted of three deep neural networks to evaluate nuclear grade, necrosis, and stromal reactivity. Deep DCIS output comprised five parameters: total patches, lesion extent, Deep Grade, Deep Necrosis, and Deep Stroma. Deep DCIS highly correlated with the pathologists' evaluations of both slide- and patient-level labels. All five parameters of Deep DCIS were significantly associated with upstaging to invasive carcinoma in subsequent wide excisional specimens. Using multivariate logistic regression, Deep DCIS predicted upstaging to invasive carcinoma with an area under the curve (AUC) of 0.81, outperforming pathologists' evaluation (AUC, 0.71 and 0.69). After including clinical and hormone receptor status information, performance further improved (AUC, 0.87). This combined model retained its predictive power in two subgroup analyses: the first subgroup included unequivocal DCIS (excluding cases of ADH and DCIS suspicious for microinvasion) (AUC, 0.83), while the second excluded cases of high-grade DCIS (AUC, 0.81). The model was validated in an independent testing cohort (AUC, 0.81).

CONCLUSION

This study demonstrated that deep-learning models can refine histological evaluation of ADH and DCIS on breast biopsies, which may help guide future treatment planning.

Validation and Clinical Utility of a Prediction Model for the Risk of Upstaging to Invasive Breast Cancer After a Biopsy Diagnosis Ductal Carcinoma In Situ

BACKGROUND

This study aimed to validate the DCIS-upstage model, a previously developed model to predict the risk of upstaging to invasive breast cancer in patients with biopsy-proven ductal carcinoma in situ (DCIS) in a more recent cohort and to assess the model's clinical utility.

METHODS

The model was validated in a registry cohort (n = 2269) and in an institution cohort (n = 302). A calibration plot was made, followed by a decision curve analysis (DCA). The model's area under the curve (AUC) was compared with the AUC of another published model and with the AUCs of new models using the risk factors of the DCIS-upstage model and additional risk factors.

RESULTS

The DCIS-upstage model had an AUC of 0.67 at development; in the validation, the AUC was 0.65 in the registry cohort and 0.73 in the institution cohort. The DCA showed that the model has clinical utility. The other published model had an AUC of 0.66 in the institution cohort. Adding risk factors to the DCIS-upstage model slightly increased the AUC.

CONCLUSIONS

The DCIS-upstage prediction model is valid in other cohorts. The model has clinical utility and may be used to select patients with biopsy-proven DCIS for sentinel lymph node biopsy.

Proteomics-Based Identification of Dysregulated Proteins and Biomarker Discovery in Invasive Ductal Carcinoma, the Most Common Breast Cancer Subtype

Invasive ductal carcinoma (IDC) is the most common histological subtype of malignant breast cancer (BC), and accounts for 70–80% of all invasive BCs. IDC demonstrates great heterogeneity in clinical and histopathological characteristics, prognoses, treatment strategies, gene expressions, and proteomic profiles. Significant proteomic determinants of the progression from intraductal pre-invasive malignant lesions of the breast, which characterize a ductal carcinoma in situ (DCIS), to IDC, are still poorly identified, validated, and clinically applied. In the era of “6P” medicine, it remains a great challenge to determine which patients should be over-treated versus which need to be actively monitored without aggressive treatment. The major difficulties for designating DCIS to IDC progression may be solved by understanding the integrated genomic, transcriptomic, and proteomic bases of invasion. In this review, we showed that multiple proteomics-based techniques, such as LC–MS/MS, MALDI-ToF MS, SELDI-ToF-MS, MALDI-ToF/ToF MS, MALDI-MSI or MasSpec Pen, applied to in-tissue, off-tissue, BC cell lines and liquid biopsies, improve the diagnosis of IDC, as well as its prognosis and treatment monitoring. Classic proteomics strategies that allow the identification of dysregulated protein expressions, biological processes, and interrelated pathway analyses based on aberrant protein–protein interaction (PPI) networks have been improved to perform non-invasive/minimally invasive biomarker detection of early-stage IDC. Thus, in modern surgical oncology, highly sensitive, rapid, and accurate MS-based detection has been coupled with “proteome point sampling” methods that allow for proteomic profiling by in vivo “proteome point characterization”, or by minimal tissue removal, for ex vivo accurate differentiation and delimitation of IDC. For the detection of low-molecular-weight proteins and protein fragments in bodily fluids, LC–MS/MS and MALDI-MS techniques may be coupled to enrich and capture methods which allow for the identification of early-stage IDC protein biomarkers that were previously invisible for MS-based techniques. Moreover, the detection and characterization of protein isoforms, including posttranslational modifications of proteins (PTMs), is also essential to emphasize specific molecular mechanisms, and to assure the early-stage detection of IDC of the breast.

Predicting Lymph Node Metastases in Patients with Biopsy-Proven Ductal Carcinoma In Situ of the Breast: Development and Validation of the DCIS-met Model

PURPOSE

In patients with a biopsy-proven ductal carcinoma in situ (DCIS), axillary staging is frequently performed, but in hindsight often turns out to be superfluous. The aim of this observational study was to develop a prediction model for risk of lymph node metastasis in patients with a biopsy-proven DCIS.

METHODS

Data were received from the Dutch Pathology Databank and the Netherlands Cancer Registry. The population-based cohort consisted of all biopsy-proven DCIS patients diagnosed in the Netherlands in 2011 and 2012. The prediction model was evaluated with the area under the curve (AUC) of the receiver operating characteristic, and a calibration plot and a decision curve analysis and was validated in a Dutch cohort of patients diagnosed in the period 2016-2019.

RESULTS

Of 2892 biopsy-proven DCIS patients, 127 had metastasis (4.4%). Risk factors were younger age (OR = 0.97, 95% CI 0.95-0.99), DCIS not detected by screening (OR = 1.55, 95% CI 1.01-2.38), suspected invasive component at biopsy (OR = 1.86, 95% CI 1.01-3.41), palpable tumour (OR = 2.06, 95% CI 1.34-3.18), BI-RADS score 5 (OR = 2.41, 95% CI 1.53-3.78), intermediate-grade DCIS (OR = 3.01, 95% CI 1.27-7.15) and high-grade DCIS (OR = 3.20, 95% CI 1.36-7.54). For 24% (n = 708) of the patients, the predicted risk of lymph node metastasis was above 5%. Based on the decision curve analysis, the model had a net benefit for a predicted risk below 25%. The AUC was 0.745. Of the 2269 patients in the validation cohort, 53 (2.2%) had metastasis and the AUC was 0.741.

CONCLUSIONS

This DCIS-met model can support clinical decisions on axillary staging in patients with biopsy-proven DCIS.

High rate of postoperative upstaging of ductal carcinoma in situ when prioritizing ultrasound evaluation of mammography-detected lesions: a single-center retrospective cohort study

BACKGROUND

The initial diagnosis of ductal carcinoma in situ (DCIS) can be upstaged to invasive cancer after definitive surgery. This study aimed to identify risk factors for DCIS upstaging using routine breast ultrasonography and mammography (MG) and to propose a prediction model.

METHODS

In this single-center retrospective study, patients initially diagnosed with DCIS (January 2016-December 2017) were enrolled (final sample size = 272 lesions). Diagnostic modalities included ultrasound-guided core needle biopsy (US-CNB), MG-guided vacuum-assisted breast biopsy, and wire-localized surgical biopsy. Breast ultrasonography was routinely performed for all patients. US-CNB was prioritized for lesions visible on ultrasound. Lesions initially diagnosed as DCIS on biopsy with a final diagnosis of invasive cancer at definitive surgery were defined as "upstaged."

RESULTS

The postoperative upstaging rates were 70.5%, 9.7%, and 4.8% in the US-CNB, MG-guided vacuum-assisted breast biopsy, and wire-localized surgical biopsy groups, respectively. US-CNB, ultrasonographic lesion size, and high-grade DCIS were independent predictive factors for postoperative upstaging, which were used to construct a logistic regression model. Receiver operating characteristic analysis showed good internal validation (area under the curve = 0.88).

CONCLUSIONS

Supplemental screening breast ultrasonography possibly contributes to lesion stratification. The low upstaging rate for ultrasound-invisible DCIS diagnosed by MG-guided procedures suggests that it is unnecessary to perform sentinel lymph node biopsy for lesions invisible on ultrasound. Case-by-case evaluation of DCIS detected by US-CNB can help surgeons determine if repeating biopsy with vacuum-assisted breast biopsy is necessary or if sentinel lymph node biopsy should accompany breast-preserving surgery.

TRIAL REGISTRATION

This single-center retrospective cohort study was conducted with the approval of the institutional review board of our hospital (approval number 201610005RIND). As this was a retrospective review of clinical data, it was not registered prospectively.

Mass-forming ductal carcinoma in situ: An ultrasonographic and histopathologic correlation study

Ultrasound (US) guided core needle biopsy (CNB) for mass lesions resulting in a diagnosis of ductal carcinoma in situ (DCIS) is often considered radiologically discordant and generates surgical planning difficulty. One hundred cases of US-guided CNB for mass lesions diagnosed as DCIS were collected from 2013 to 2021. Histological features were reviewed and correlated with radiology and surgical excision findings. Thirty (30%) were high-grade (HG), and seventy (70%) were low- to intermediate-grade. Seventy-one (71%) cases had a histological correlate of a mass-forming lesion, including 26 (26%) were associated with benign mass-forming lesions (category 1) such as papilloma, complex sclerosing lesion/radial scar, fibroadenoma, sclerosing adenosis, and ruptured cyst; 23 (23%) were HG with solid pattern, comedo necrosis, and stromal desmoplasia (category 2); and 22 (22%) had predominantly papillary architecture (category 3). Twenty-nine (29%) were discordant with no histologic correlate of a mass lesion (category 4). Follow-up excisions were available in 79 cases. Invasive carcinoma was identified in 14 cases (18%), of which 8 were from the radiologically discordant category (35%), 3 (17%) associated with HG DCIS with desmoplasia, 2 (10%) associated with benign mass lesion and 1(5%) was predominantly papillary architecture. US-guided CNB for mass-forming lesions with a DCIS diagnosis on CNB can be grouped into four categories. Radiology-pathology correlation is essential. This categorization emphasized rad-path correlation and had a clear difference in upgrade rate on follow-up excision. Rad-path discordant biopsy cases were more likely to be associated with a missed invasive carcinoma (p < 0.05).

Diffusion-weighted Breast MRI in Prediction of Upstaging in Women with Biopsy-proven Ductal Carcinoma in Situ

Background Accurate preoperative prediction of upstaging in women with biopsy-proven ductal carcinoma in situ (DCIS) is important for surgical planning, but published models using predictive MRI features remain lacking. Purpose To develop and validate a predictive model based on preoperative breast MRI to predict upstaging in women with biopsy-proven DCIS and to select high-risk women who may benefit from sentinel lymph node biopsy at initial surgery. Materials and methods Consecutive women with biopsy-proven DCIS who underwent preoperative 3.0-T breast MRI including dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) and who underwent surgery between June 2019 and March 2020 were retrospectively identified (development set) from an academic medical center. The apparent diffusion coefficients of lesions from DWI, lesion size and morphologic features on DCE MRI scans, mammographic findings, age, symptoms, biopsy method, and DCIS grade at biopsy were collected. The presence of invasive cancer and axillary metastases was determined with surgical pathology. A predictive model for upstaging was developed by using multivariable logistic regression and validated in a subsequent prospective internal validation set recruited between July 2020 and April 2021. Results Fifty-seven (41%) of 140 women (mean age, 53 years ± 11 [SD]) in the development set and 43 (41%) of 105 women (mean age, 53 years ± 10) in the validation set were upstaged after surgery. The predictive model combining DWI and clinical-pathologic factors showed the areas under the receiver operating characteristic curve at 0.87 (95% CI: 0.80, 0.92) in the development set and 0.76 (95% CI: 0.67, 0.84) in the validation set. The predicted probability of invasive cancer showed good interobserver agreement (intraclass correlation coefficient, 0.79); the positive predictive value was 85% (28 of 33), and the negative predictive value was 92% (22 of 24). Conclusion A predictive model based on diffusion-weighted breast MRI identified women at high risk of upstaging. © RSNA, 2022 Online supplemental material is available for this article See also the editorial by Baltzer in this issue.

Surgeons’ preferences for using sentinel lymph node biopsy in patients with ductal carcinoma in situ

Background

There is a large variation between Dutch hospitals in the use of Sentinel Lymph Node Biopsy (SLNB) in patients with a biopsy diagnosis of Ductal Carcinoma in Situ. The aim of our study was to investigate whether this variation might be explained by preferences of surgeons, organisational factors or the influence of patients preferences.

Methods

A cross-sectional web survey was conducted among 260 Dutch oncological/breast surgeons. Preferences of surgeons and the influence of the patients’ preferences were determined by means of best-worst scaling (BWS) of profile case scenarios and by ranking risk factors. The survey also explored organisational questions, the reported use of diagnostic techniques and influences on the decision.

Results

The BWS scenarios were completed by 57 surgeons. The most important reasons for performing SLNB were a suspected invasive component and DCIS grade 3. In the ranking, these were also the first and second most important factor, followed by the size of the lesion and a mass on mammogram. In 58% to 70% of the scenarios, the surgeons would not change their decisions on the use of SLNB if the patient’s chose differed. No organisational factor was significantly associated with the reported use of SLNB.

Conclusion

The inter-hospital variation in the use of SLNB could not be attributed to organisational factors or surgeons’ preferences for risk factors. The risk factors that most surgeons reported as reasons for performing SLNB are consistent with the factors described in the Dutch treatment guideline for the use of SLNB.

Pathological underestimation and biomarkers concordance rates in breast cancer patients diagnosed with ductal carcinoma in situ at preoperative biopsy

Ductal carcinoma in situ (DCIS) often upgrade to invasive breast cancer at surgery. The current study aimed to identify factors associated with pathological underestimation and evaluate concordance rates of biomarkers between biopsy and surgery. Patients diagnosed with DCIS at needle biopsy from 2009 to 2020 were retrospectively reviewed. Univariate and multivariate analyses were performed to identify factors associated with pathological underestimation. Concordance rates between paired biopsy samples and surgical specimens were evaluated. A total of 735 patients with pure DCIS at biopsy were included, and 392 patients (53.3%) underwent pathological underestimation at surgery. Multivariate analysis demonstrated that tumor size > 5.0 cm [odds ratio (OR) 1.79], MRI BI-RADS ≥ 5 categories (OR 2.03), and high nuclear grade (OR 2.01) were significantly associated with pathological underestimation. Concordance rates of ER, PR, HER2 status and Ki-67 between biopsy and surgery were 89.6%, 91.9%, 94.8%, and 76.4% in lesions without pathological underestimation, and were 86.4%, 93.2%, 98.2% and 76.3% for in situ components in lesions with pathological underestimation. Meanwhile, in situ components and invasive components at surgery had concordance rates of 92.9%, 93.8%, 97.4%, and 86.5% for those biomarkers, respectively. In conclusion, lesions diagnosed as DCIS at biopsy have a high rate of pathological underestimation, which was associated with larger tumor size, higher MRI BI-RADS category, and higher nuclear grade. High concordances were found in terms of ER, PR, and HER2 status evaluation between biopsy and surgery, regardless of the pathological underestimation.

Ductal carcinoma in situ: a risk prediction model for the underestimation of invasive breast cancer

Patients with a biopsy diagnosis of ductal carcinoma in situ (DCIS) may be diagnosed with invasive breast cancer after excision. We evaluated the preoperative clinical and imaging predictors of DCIS that were associated with an upgrade to invasive carcinoma on final pathology and also compared the diagnostic performance of various statistical models. We reviewed the medical records; including mammography, ultrasound (US), and magnetic resonance imaging (MRI) findings; of 644 patients who were preoperatively diagnosed with DCIS and who underwent surgery between January 2012 and September 2018. Logistic regression and three machine learning methods were applied to predict DCIS underestimation. Among 644 DCIS biopsies, 161 (25%) underestimated invasive breast cancers. In multivariable analysis, suspicious axillary lymph nodes (LNs) on US (odds ratio [OR], 12.16; 95% confidence interval [CI], 4.94-29.95; P < 0.001) and high nuclear grade (OR, 1.90; 95% CI, 1.24-2.91; P = 0.003) were associated with underestimation. Cases with biopsy performed using vacuum-assisted biopsy (VAB) (OR, 0.42; 95% CI, 0.27-0.65; P < 0.001) and lesion size <2 cm on mammography (OR, 0.45; 95% CI, 0.22-0.90; P = 0.021) and MRI (OR, 0.29; 95% CI, 0.09-0.94; P = 0.037) were less likely to be upgraded. No significant differences in performance were observed between logistic regression and machine learning models. Our results suggest that biopsy device, high nuclear grade, presence of suspicious axillary LN on US, and lesion size on mammography or MRI were independent predictors of DCIS underestimation.

Is Axillary Assessment of Ductal Carcinoma In Situ of the Breast Necessary in All Cases?

BACKGROUND

Staging of the axilla in women with ductal carcinoma in situ (DCIS) is a point of controversy. We aimed to assess whether there is a group of patients in whom axillary assessment can be avoided and whether the likelihood of underdiagnosis of infiltrating carcinoma is sufficient to justify this evaluation.

MATERIALS AND METHODS

This was a multicenter, prospective, observational study of patients who were operated on between 2008 and 2018 in three Spanish hospitals, with a diagnosis by radiological or excisional biopsy of DCIS and clinically and radiologically negative axilla.

RESULTS

A total of 530 patients with a preoperative diagnosis of DCIS were studied. An axillary assessment was performed in 77% of the patients. In 397 patients, selective sentinel lymph node biopsy was performed. Axillary involvement was found in 7.2% of all patients, which dropped to 2.15% if we only included DCIS diagnosed after a definitive anatomical pathology analysis. Underdiagnosis was correlated with the type of biopsy performed: the risk was 1.34 times as high if the biopsy was performed with a core needle. The risk of lymph node metastasis was higher when there was lymphovascular invasion and when mastectomy was performed.

CONCLUSIONS

We propose an axilla management algorithm in patients with a preoperative diagnosis of DCIS. The patients who would benefit from sentinel lymph node biopsy would be those who are not candidates for breast-conserving surgery, those with a BIRADS 5 lesion biopsied by core-needle biopsy, and those whose definitive diagnosis is lymphovascular invasion.

Prediction of Underestimation Using Contrast-Enhanced Spectral Mammography in Patients Diagnosed as Ductal Carcinoma In Situ on Preoperative Core Biopsy

BACKGROUND

To assess the performance of contrast-enhanced spectral mammography (CESM) for the prediction of DCIS underestimation in comparison with mammography, breast US, and breast MRI.

PATIENTS AND METHODS

We prospectively enrolled patients diagnosed with DCIS on preoperative core biopsy. Visibility, lesion type, and extent on each imaging modality, CESM gray values (CGV) were evaluated. Pathologic features of core biopsy and surgery were recorded. Chi-square or Fisher's exact test were used for univariate analysis. Multivariate logistic regression analysis was used to find independent predictors for DCIS underestimation and receiver operating characteristic (ROC) curve analysis was performed.

RESULTS

A total of 113 lesions in 108 patients were analyzed (50 pure DCIS; 63 underestimated DCIS). Visibility on mammography, breast US, CESM, and breast MRI were 44%, 76%, 58%, and 80% for pure DCIS, and 73%, 81%, 86%, and 92% for underestimated DCIS. Tumor extents on surgical pathology of pure and underestimated DCIS were 1.11 ± 1.35 cm and 2.61 ± 2.09 cm. On multivariate analysis, nuclear grade and suspected invasion on core biopsy, visibility on mammography, and extent on breast MRI were independent factors for the model 1, whereas nuclear grade on core biopsy, extent on CESM, and mean CGV on MLO-recombined image were independent factors for the model 2. Area under ROC curve (AUC) was 0.843 for model 1 including breast MRI, whereas AUC was 0.823 for model 2 including CESM, which didn't show a significant difference (P = .968).

CONCLUSION

For detecting underestimated DCIS, CESM was superior to mammography and breast US, and comparable to breast MRI.

Ductal Carcinoma in situ: Underestimation of Percutaneous Biopsy and Positivity of Sentinel Lymph Node Biopsy in a Brazilian Public Hospital

Background

Mammography screening has become widely spread and provided a marked increase in ductal carcinoma in situ (DCIS) diagnosis. In DCIS, the ductal epithelium proliferates without invasion through the basal cell membrane. However, histologic underestimation can happen in some cases.

Objective

To analyze the rate of histologic underestimation (histopathologic results upgraded to invasive carcinoma after surgery) and the rate of positive results of sentinel lymph node biopsy (SLNB) in patients diagnosed with DCIS in a Brazilian public hospital.

Methods

We reviewed medical records of all consecutive patients admitted between 2009 and 2013 whose initial diagnosis was DCIS through core needle biopsy. DCIS cases with a high risk of invasion underwent SLNB. We excluded cases with invasion or micro-invasion components in the first biopsy.

Results

A total of 86 women were included, most with microcalcifications as the primary radiological lesion (73.2%), and underwent preoperative biopsy, with an invasive component in 21 (24.4%) in the final pathology report. Most had invasive carcinoma of no special type (NST): 52.3% (n = 11) and microinvasive tumors (7 cases, 33.3%). The main factors associated with histologic underestimation were nodular lesion (61.9%, p<0.001) and an ultra-sonography-guided biopsy (71.4%, p=0.0005). The positivity rate of SLNB was 4.3%. All these patients underwent mastectomy, and the initial histologic pattern was solid DCIS.

Conclusion

The “histologic underestimation” rate among patients with DCIS was not low, and less than 5% of patients who underwent SLNB had axillary positivity. This result suggests that patients who have DCIS and a high risk of invasion and undergoing mastectomy should have SLNB. As to the patients who will undergo lumpectomy, SLNB could be omitted and could be performed if patients have upgraded to invasive breast cancer.

Surgical upstaging rates in patients meeting the eligibility for active surveillance trials

PURPOSE

Four clinical active surveillance trials including LORIS, COMET, LORD and LORETTA, are being conducted to assess whether women with low-risk ductal carcinoma in situ can safely avoid surgery. The present study aimed to determine the rate of upstaging to invasive cancer among patients with a preoperative diagnosis of ductal carcinoma in situ and to evaluate the incidence of upstaging in patients meeting the eligibility criteria for four active surveillance clinical trials.

METHODS

The present study initially enrolled 180 patients with 183 calcifications who received the diagnosis of ductal carcinoma in situ by biopsy. Patients were classified as eligible for four clinical trials according to the respective inclusion criteria.

RESULTS

In total, 152 patients with 155 calcifications were analyzed. Of these, 32 (21%) were upstaged to invasive disease based on the final pathological analysis of surgical specimens. Of the 152 patients, 53 (35%), 90 (59%), 24 (16%) and 34 (22%) met the eligibility criteria for the LORIS, COMET, LORD and LORETTA trial, respectively. Among patients with low-risk ductal carcinoma in situ, 10 (19%), 14 (16%), 6 (25%) and 4 (12%) patients were upstaged to invasive disease in LORIS, COMET, LORD and LORETTA, respectively. The upstaging to pT1b or higher rates were 2% (1/53), 3% (3/90), 0% (0/24) and 3% (1/34) in LORIS, COMET, LORD and LORETTA, respectively.

CONCLUSIONS

The upstaging rate in patients eligible for the clinical active surveillance trials was 12-25%. Although the rate of upstaging to pT1b or higher was low, further studies are required to determine the rates of upstaging to invasive cancer and the risk factors among patients with low-risk ductal carcinoma in situ.

Impact of the COVID-19 pandemic on diagnosis, stage, and initial treatment of breast cancer in the Netherlands: a population-based study

BACKGROUND

The onset of the COVID-19 pandemic forced the Dutch national screening program to a halt and increased the burden on health care services, necessitating the introduction of specific breast cancer treatment recommendations from week 12 of 2020. We aimed to investigate the impact of COVID-19 on the diagnosis, stage and initial treatment of breast cancer.

METHODS

Women included in the Netherlands Cancer Registry and diagnosed during four periods in weeks 2-17 of 2020 were compared with reference data from 2018/2019 (averaged). Weekly incidence was calculated by age group and tumor stage. The number of women receiving initial treatment within 3 months of diagnosis was calculated by period, initial treatment, age, and stage. Initial treatment, stratified by tumor behavior (ductal carcinoma in situ [DCIS] or invasive), was analyzed by logistic regression and adjusted for age, socioeconomic status, stage, subtype, and region. Factors influencing time to treatment were analyzed by Cox regression.

RESULTS

Incidence declined across all age groups and tumor stages (except stage IV) from 2018/2019 to 2020, particularly for DCIS and stage I disease (p < 0.05). DCIS was less likely to be treated within 3 months (odds ratio [OR]wks2-8: 2.04, ORwks9-11: 2.18). Invasive tumors were less likely to be treated initially by mastectomy with immediate reconstruction (ORwks12-13: 0.52) or by breast conserving surgery (ORwks14-17: 0.75). Chemotherapy was less likely for tumors diagnosed in the beginning of the study period (ORwks9-11: 0.59, ORwks12-13: 0.66), but more likely for those diagnosed at the end (ORwks14-17: 1.31). Primary hormonal treatment was more common (ORwks2-8: 1.23, ORwks9-11: 1.92, ORwks12-13: 3.01). Only women diagnosed in weeks 2-8 of 2020 experienced treatment delays.

CONCLUSION

The incidence of breast cancer fell in early 2020, and treatment approaches adapted rapidly. Clarification is needed on how this has affected stage migration and outcomes.

Development and Validation of a Simple-to-Use Nomogram for Predicting the Upgrade of Atypical Ductal Hyperplasia on Core Needle Biopsy in Ultrasound-Detected Breast Lesions

Background

The rate of carcinoma upgrade for atypical ductal hyperplasia (ADH) diagnosed on core needle biopsy (CNB) is variable on open excision. The purpose of the present study was to develop and validate a simple-to-use nomogram for predicting the upgrade of ADH diagnosed with ultrasound (US)-guided core needle biopsy in patients with US-detected breast lesions.

Methods

Two retrospective sets, the training set (n = 401) and the validation set (n = 186), from Fudan University Shanghai Cancer Center between January 2014 and December 2019 were retrospectively analyzed. Clinicopathological and US features were selected using univariate and multivariable logistic regression, and the significant features were incorporated to build a nomogram model. Model discrimination and calibration were assessed in the training set and validation set.

Results

Of the 587 ADH biopsies, 67.7% (training set: 267/401, 66.6%; validation set: 128/186, 68.8%) were upgraded to cancers. In the multivariable analysis, the risk factors were age [odds ratio (OR) 2.739, 95% confidence interval (CI): 1.525–5.672], mass palpation (OR 3.008, 95% CI: 1.624–5.672), calcifications on US (OR 4.752, 95% CI: 2.569–9.276), ADH extent (OR 3.150, 95% CI: 1.951–5.155), and suspected malignancy (OR 4.162, CI: 2.289–7.980). The model showed good discrimination, with an area under curve (AUC) of 0.783 (95% CI: 0.736–0.831), and good calibration (p = 0.543). The application of the nomogram in the validation set still had good discrimination (AUC = 0.753, 95% CI: 0.666–0.841) and calibration (p = 0.565). Instead of surgical excision of all ADHs, if those categorized with the model to be at low risk for upgrade were surveillanced and the remainder were excised, then 63.7% (37/58) of surgeries of benign lesions could have been avoided and 78.1% (100/128) malignant lesions could be treated in time.

Conclusions

This study developed a simple-to-use nomogram by incorporating clinicopathological and US features with the overarching goal of predicting the probability of upgrade in women with ADH. The nomogram could be expected to decrease unnecessary surgery by nearly two-third and to identify most of the malignant lesions, helping guide clinical decision making with regard to surveillance versus surgical excision of ADH lesions.

Morphological intratumor heterogeneity in ductal carcinoma in situ of the breast

Ductal carcinoma in situ (DCIS) of the breast is a heterogeneous disease in terms of morphological characteristics, protein expression profiles, genetic abnormalities, and potential for progression. Molecular heterogeneity has been extensively studied in DCIS. Yet morphological heterogeneity remains relatively undefined. This study investigated morphological intratumor heterogeneity in a series of 51 large DCIS. Nuclear atypia, DCIS architecture, necrosis, calcifications, stromal architecture, and stromal inflammation were assessed in one biopsy slide and three representative slides from each corresponding resection. For each histopathological feature, a histo-score was determined per slide and compared between the biopsy and the resection, as well as within a single resection. Statistical analysis comprised of Friedman tests, post hoc Wilcoxon tests with Bonferroni corrections, Mann-Whitney U tests, and chi-square tests. Despite substantial morphological heterogeneity in around 50% of DCIS, the histopathological assessment of the biopsy did not statistically significantly differ from the resection. Morphological heterogeneity was not significantly associated with patient age, DCIS size, or type of surgery, except for a weak association between heterogeneous stromal inflammation and smaller DCIS size. At the group level, the degree of heterogeneity did not significantly affect the representativity of a biopsy. At the individual patient level, however, the presence of necrosis, intraductal calcifications, myxoid stromal changes, and high-grade nuclear atypia was underestimated in a minority of DCIS patients. This study confirms the presence of morphological heterogeneity in DCIS for all six evaluated histopathological features. This should be kept in mind when taking biopsy-based treatment decisions for DCIS patients.

Preoperative breast MRI in management of patients with needle biopsy-proven ductal carcinoma in situ (DCIS)

BACKGROUND

In 20-25% of patients with biopsy-proven DCIS underestimation occurs. Sentinel lymph node biopsy (SLNB) is offered to patients with biopsy-proven ductal carcinoma in situ (DCIS) and a high risk of occult invasive cancer. However, assessment of high risk is controversial. We aimed to improve selection of patients for SLNB with preoperative breast magnetic resonance imaging (MRI).

METHODS

In this prospective observational study, MRI was offered to all subsequent patients with a biopsy-proven DCIS admitted to a large Dutch teaching hospital between April 2012 and March 2017. MRI images were analysed for signs of invasive cancer and the results were compared with the pathologic results after surgical treatment. The diagnostic accuracy of additional MRI in detecting occult invasive cancer was determined.

RESULTS

Of 211 patients eligible for additional MRI analysis, 149 underwent breast MRI. The majority (67%) received breast-conserving therapy, and the underestimation rate was 20%. Subsequent to MRI analysis, 20 additional invasive diagnostic procedures were performed. Occult invasive cancer was suspected on MRI in 46 patients (31%) and was confirmed in 18 (12%). In this study, breast MRI had a sensitivity of 67%, a specificity of 77%, and a true negative rate of 91%.

CONCLUSION

Preoperative breast MRI cannot reliably predict the presence of invasive cancer in patients with biopsy proven DCIS. Therefore, it cannot be used to in the selection of patients for a SLNB.

Role of Sentinel Lymph Node Biopsy in Microinvasive Breast Cancer

BACKGROUND

Microinvasive ductal carcinoma (DCISM), defined as DCIS with a focus of invasive carcinoma ≤ 1 mm, can be managed similarly to pure DCIS; however, management of the axilla in DCISM has been a subject of debate. Reports in the literature differ on the utility and necessity of sentinel lymph node biopsy (SLNB) for DCISM. The aim of the present study was to identify risk factors for nodal disease in patients with DCISM, which can help develop a selective approach to SLNB in this patient population.

METHODS

The National Cancer Database was used to select patients with DCISM (pT1mi), diagnosed from 2012 to 2015, who underwent SLNB. Multivariable regression analysis was performed to determine associations between sentinel lymph node metastasis and relevant clinical variables.

RESULTS

Our cohort comprised of 2609 patients with pT1mi who underwent SLNB. Of these, 76 (2.9%) were found to have sentinel lymph node metastases on final pathology. Low/intermediate grade tumors were associated with decreased SLN metastasis (OR 0.50, CI 0.28-0.92). Age and receptor status of the tumor did not have a clear association in predicting SLN metastases.

CONCLUSIONS

The rate of sentinel node metastases in DCISM is low at only 2.9% in this national study. Tumor grade was identified as influencing the risk of SLN metastases. This information can factor into shared decision-making for SLNB in patients with DCISM.

An Updated Nomogram for Predicting Invasiveness in Preoperative Ductal Carcinoma In Situ of the Breast

PURPOSE

To validate and update a nomogram for predicting ductal carcinoma in situ (DCIS) upstaging in preoperative biopsy.

MATERIALS AND METHODS

Medical records of 444 preoperative DCIS patients were evaluated and used to validate a previous version of the Severance nomogram for predicting DCIS upstaging in preoperative biopsy. Patients were divided into two groups according to the final postoperative pathology. Univariate and multivariate analyses with the chi-square test, Student's t-test, and binary logistic regression method identified new significant variables. The updated nomogram was evaluated with the C-index and Hosmer-Lemeshow goodness of fit test.

RESULTS

The area under a receiver operating characteristic curve for comparison with the previous nomogram was 0.48. In postoperative pathology, the pure DCIS and invasive cancer groups comprised 345 and 99 cases, respectively. Approximately 22.3% of patients preoperatively diagnosed with DCIS were upstaged to invasive cancer. Significant variables in the univariate analysis were operation type, human epidermal growth factor receptor 2 overexpression, comedo necrosis, sonographic mass, mammographic mass, preoperative biopsy method, and suspicious microinvasion in preoperative biopsy. In multivariate analysis, operation type, sonographic mass, mammographic mass, and suspicious microinvasion were risk factors for upstaging. The updated model with these variables showed moderate discrimination and was appropriate in the calibration test.

CONCLUSION

The previous nomogram did not effectively discriminate upstaging of preoperative DCIS in an independent cohort. An updated version of the nomogram appears to provide more accurate information for predicting preoperative DCIS upstaging.