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.

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.

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.

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.

BACKGROUND: Patients with a biopsy diagnosis of ductal carcinoma in situ (DCIS) might be diagnosed with invasive breast cancer at excision, a phenomenon known as underestimation. Patients with DCIS are treated based on the risk of underestimation or progression to invasive cancer. The aim of our study was to expand the knowledge on underestimation and to develop a prediction model. METHODS: Population-based data were retrieved from the Dutch Pathology Registry and the Netherlands Cancer Registry for DCIS between January 2011 and June 2012. RESULTS: Of 2892 DCIS biopsies, 21% were underestimated invasive breast cancers. In multivariable analysis, risk factors were high-grade DCIS (odds ratio (OR) 1.43, 95% confidence interval (CI): 1.05-1.95), a palpable tumour (OR 2.22, 95% CI: 1.76-2.81), a BI-RADS (Breast Imaging Reporting and Data System) score 5 (OR 2.36, 95% CI: 1.80-3.09) and a suspected invasive component at biopsy (OR 3.84, 95% CI: 2.69-5.46). The predicted risk for underestimation ranged from 9.5 to 80.2%, with a median of 14.7%. Of the 596 invasive cancers, 39% had unfavourable features. CONCLUSIONS: The risk for an underestimated diagnosis of invasive breast cancer after a biopsy diagnosis of DCIS is considerable. With our prediction model, the individual risk of underestimation can be calculated based on routinely available preoperatively known risk factors ( https://www.evidencio.com/models/show/1074 ).

Postoperative mortality after cancer surgery in octogenarians and nonagenarians: results from a series of 5,390 patients.

BACKGROUND: To support decisions about surgical treatment of elderly patients with cancer, population-based estimates of postoperative mortality (POM) rates are required. METHODS: Electronic records from the Rotterdam Cancer Registry were retrieved for octogenarians and nonagenarians who underwent resection in the period 1987-2000. POM was defined as death within 30 days of resection and both elective and emergency operations were included. RESULTS: In a series of 5.390 operated patients aged 80 years and older, POM rates were 0.5% for breast cancer, 1.7% for endometrial cancer and 4.2% for renal cancer. For patients with colorectal cancer, POM increased from 8% for the age group 80-84 to 13% for those 85-89 to 20% in nonagenarians. For stomach cancer, the respective figures were 11%, 20% and 44%. CONCLUSION: These results show that resections can be performed at acceptable risk in selected elderly patients with cancer.