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

Abstract

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.

Machine learning and new insights for breast cancer diagnosis

“…124 The current review extracted and organized the data in tabular form and summarized the application of MRI in breast cancer diagnosis (Table 3). 109,119,120,123, Others X-ray mammography is often employed for BC identification. However, it is a highly invasive procedure because X-rays damage tissues and quite frequently fails to determine the tumour size.…”

“…174 The current review extracted and organized the data in tabular form and summarized the application of CT, breast thermal imaging, PET and microwave imaging technology in the diagnosis of breast cancer (Table 4). 92,109,147,149,151,167,[174][175][176][177][178][179][180][181][182][183][184][185][186][187]…”

“…Models, classes and performance for breast magnetic resonance imaging data in selected papers 109,119,120,123,. …”

Crossroads: the role of biomarkers in the management of lumps in the breast

“…(At 1.6 million biopsies per year in the US, the presumed overestimation of risk in 24% of patients leads to overtreatment of 384,000 patients per year at a cost of $61,000/patient. The presumed underestimation of risk in 21% of DCIS cases leads to missed intervention in 336,000 patients, who may return as stage 4 patients and require treatment at $135,000/year [8][9][10]). With a markerbased approach, relevant information is obtainable from one stained slide, not requiring step sections.…”

Evaluating Upstaging in Ductal Carcinoma In Situ Using Preoperative MRI‐Based Radiomics

“…Preoperative evaluation of occult infiltrative lesions and pathological nuclear grade is necessary, which determined the potential risks of DCIS patients developing invasive cancer. 9,10 The feasibility of traditional clinicopathologic risk factors for the postoperative pathological upstaging of DCIS has been reported, 5,[11][12][13] but the prediction performance was dissatisfactory. [14][15][16][17] Against this background, MRI could serve as an effective examination tool for the diagnosis and staging of breast cancer, 18,19 but for radiologists, it is quite challenging to assess whether DCIS has invasive components and to define the nuclear grade based on images.…”