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.

Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

Purpose To investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs). Methods This retrospective study enrolled 406 patients with pathologically confirmed low- and high-nuclear grade of CCRCCs according to the WHO/ISUP grading system, which were divided into the training and testing cohorts. Radiomics features were extracted from nephrographic-phase CT images using PyRadiomics. A support vector machine (SVM) combined with three feature selection algorithms such as least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF was performed to determine the most suitable classification model, respectively. Clinicoradiological, radiomics, and combined models were constructed using the radiological and clinical characteristics with significant differences between the groups, selected radiomics features, and a combination of both, respectively. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. Results SVM-ReliefF algorithm outperformed SVM-LASSO and SVM-RFE in distinguishing low- from high-grade CCRCCs. The combined model showed better prediction performance than the clinicoradiological and radiomics models (p < 0.05, DeLong test), which achieved the highest efficacy, with an area under the ROC curve (AUC) value of 0.887 (95% confidence interval [CI] 0.798–0.952), 0.859 (95% CI 0.748–0.935), and 0.828 (95% CI 0.731–0.929) in the training, validation, and testing cohorts, respectively. The calibration and decision curves also indicated the favorable performance of the combined model. Conclusion A combined model incorporating the radiomics features and clinicoradiological characteristics can better predict the WHO/ISUP nuclear grade of CCRCC preoperatively, thus providing effective and noninvasive assessment.

Histopathological features for coexistent invasive cancer in large colorectal adenomatous polyps

Background Histopathological features associated with coexistent invasive adenocarcinoma in large colorectal adenomas have not been described. This study aimed to determine the association of histopathological features in areas of low-grade dysplasia with coexistent invasive adenocarcinoma. Methods High-grade lesions (containing high-grade dysplasia or adenocarcinoma) from a cohort of large (at least 20 mm) colorectal adenomas removed by endoscopic resection were subjected to detailed histopathological analysis. The histopathological features in low-grade areas with coexistent adenocarcinoma were reviewed and their diagnostic performance was evaluated. Results Seventy-four high-grade lesions from 401 endoscopic resections of large adenomas were included. In the low-grade dysplastic areas, a coexistent invasive adenocarcinoma was associated significantly with a cribriform or trabecular growth pattern (P &lt; 0.001), high nuclear grade (P &lt; 0.001), multifocal intraluminal necrosis (P &lt; 0.001), atypical mitotic figures (P = 0.006), infiltrative lesion edges (P &lt; 0.001), a broad fibrous band (P = 0.001), ulceration (P &lt; 0.001), expansile nodules (P &lt; 0.001) and an extensive tumour-infiltrating lymphocyte pattern (P = 0.04). Lesions with coexistent invasive adenocarcinoma harboured at least one of these features. The area under the receiver operating characteristic curve (AUROC) for coexistent invasive adenocarcinoma, using frequencies of adverse histopathological factors in low-grade areas, was 0.92. The presence of two or more of these adverse histopathological features in low-grade areas had a sensitivity of 86 per cent and a specificity of 84 per cent for coexistent invasive adenocarcinoma. Conclusion Several histopathological features in low-grade dysplastic areas of adenomas could be predictive of coexistent adenocarcinoma.

Mucinous Tubular and Spindle Cell Carcinoma: A Review of Histopathology and Clinical and Prognostic Implications

Mucinous tubular and spindle cell carcinomas are rare kidney tumors with generally indolent behavior. As the name suggests, classic histomorphology reveals bland spindle cells, tubules, and mucinous stroma. Uncommon histologic features include mucin-poor stroma, high nuclear grade, cellular pleomorphism, and presence of necrosis. Rare cases can show aggressive growth and distant metastasis. Mucinous tubular and spindle cell carcinoma has characteristic chromosomal abnormalities and the molecular signature remains the same, irrespective of the varied histomorphology.

Ductal Breast Carcinoma In Situ: Mammographic Features and Its Relation to Prognosis and Tumour Biology in a Population Based Cohort

Casting-type calcifications and a histopathological picture with cancer-filled duct-like structures have been presented as breast cancer with neoductgenesis. We correlated mammographic features and histopathological neoductgenesis with prognosis in a DCIS cohort with long follow-up. Mammographic features were classified into seven groups according to Tabár. Histopathological neoductgenesis was defined by concentration of ducts, lymphocyte infiltration, and periductal fibrosis. Endpoints were ipsilateral (IBE) in situ and invasive events. Casting-type calcifications and neoductgenesis were both related to high nuclear grade, ER- and PR-negativity, and HER2 overexpression but not to each other. Casting-type calcifications and neoductgenesis were both related to a nonsignificant lower risk of invasive IBE, HR 0.38 (0.13–1.08) and 0.82 (0.29–2.27), respectively, and the HR of an in situ IBE was 0.90 (0.41–1.95) and 1.60 (0.75–3.39), respectively. Casting-type calcifications could not be related to a worse prognosis in DCIS. We cannot explain why a more aggressive phenotype of DCIS did not correspond to a worse prognosis. Further studies on how the progression from in situ to invasive carcinoma is driven are needed.