A logistic regression nomogram to predict axillary lymph node metastasis in early invasive breast cancer patients

Comprehensive quantitative malignant risk prediction of pure grouped amorphous calcifications: clinico-mammographic nomogram

BACKGROUND

Pure grouped amorphous calcifications are classified as Breast Imaging Reporting and Data System (BI-RADS) category 4B suspicious calcifications and recommended for biopsy. However, the biopsies often reveal benign findings, especially in screening mammograms (92.4-97.2%).

METHODS

Mammograms of 699 pure grouped amorphous calcifications with final pathological results were analyzed in this retrospective study. The maximum span (MS) of the group of calcifications and the MS of the parallel/vertical direction of the mammary duct (MPS/MVS) were measured, and the MPS to MVS ratio was calculated. Based on the MS and ratio, 2 prediction nomograms with other clinic-mammographic features were developed. The discrimination performance of the models was assessed and compared by the area under the receiver operating characteristic curve (AUC). Scatterplots were created to determine the cutoff values with fewer misdiagnoses of malignant calcifications and fewer false positives.

RESULTS

Ultimately, 2 prediction models were successfully developed based on the 4 risk factors of age, purpose of the mammogram, whether multiple or single calcifications, and the MS [odds ratio (OR) =1.06, P=0.02]/ratio (OR =6.05, P<0.001). Both models had good discrimination. The ratio model performed better than the MS model in the training cohort (AUC of 0.875 and 0.834, respectively, P=0.003) and validation cohort (AUC 0.908 and 0.867, respectively, P=0.047). For the group with probably benign calcifications (as detected by the ratio nomogram), the malignancy rates were 2.7% [95% confidence interval (CI): 1.00% to 6.53%] and 1.19% (95% CI: 0.06% to 7.37%) in the training and validation cohorts, respectively, and 44.12% and 47.70% of benign biopsies were detected in the training and validation cohorts, respectively.

CONCLUSIONS

The clinico-mammographic quantitative malignancy risk prediction nomogram showed favorable discrimination and calibration performance. The ratio model showed better diagnostic efficiency than the MS model, and identified >40% of benign biopsies.

Predicting the pathological grade of breast phyllodes tumors: a nomogram based on clinical and magnetic resonance imaging features

OBJECTIVE

To explore the potential factors related to the pathological grade of breast phyllodes tumors (PTs) and to establish a nomogram to improve their differentiation ability.

METHODS

Patients with PTs diagnosed by post-operative pathology who underwent pretreatment magnetic resonance imaging (MRI) from January 2015 to June 2020 were retrospectively reviewed. Traditional clinical features and MRI features evaluated according to the fifth BI-RADS were analyzed by statistical methods and introduced to a stepwise multivariate logistic regression analysis to develop a prediction model. Then, a nomogram was developed to graphically predict the probability of non-benign (borderline/malignant) PTs.

RESULTS

Finally, 61 benign, 73 borderline and 48 malignant PTs were identified in 182 patients. Family history of tumor, diameter, lobulation, cystic component, signal on fat saturated T₂ weighted imaging (FS T₂WI), BI-RADS category and time-signal intensity curve (TIC) patterns were found to be significantly different between benign and non-benign PTs. The nomogram was finally developed based on five risk factors: family history of tumor, lobulation, cystic component, signal on FS T₂WI and internal enhancement. The AUC of the nomogram was 0.795 (95% CI: 0.639, 0.835).

CONCLUSION

Family history of tumor, lobulation, cystic components, signals on FS T₂WI and internal enhancement are independent predictors of non-benign PTs. The prediction nomogram developed based on these features can be used as a supplemental tool to pre-operatively differentiate PTs grades.

ADVANCES IN KNOWLEDGE

More sample size and characteristics were used to explore the factors related to the pathological grade of PTs and establish a predictive nomogram for the first time.

Malignancy Risk Stratification Prediction of Amorphous Calcifications Based on Clinical and Mammographic Features

Purpose

To explore the potential factors influencing the malignancy risk of amorphous calcifications and establish a predictive nomogram for malignancy risk stratification.

Patients and Methods

Consecutive mammograms from January 2013 to December 2018 were retrospectively reviewed. Traditional clinical features were recorded, and mammographic features were estimated according to the 5th BI-RADS. Included calcifications were randomly divided into the training and validation cohorts. A nomogram was developed to graphically predict the risk of malignancy (risk) based on stepwise multivariate logistic regression analysis. The discrimination and calibration performance of the model were assessed in both the training and validation cohorts.

Results

Finally, 1018 amorphous calcifications with final pathological results in 907 women were identified with a malignancy rate of 28.4% (95% CI: 25.7%, 31.3%). The malignancy rates of subgroups divided by the distribution of calcifications, quantity of calcifications, age, menopausal status and family history of cancer were significantly different. There were 712 cases and 306 cases in the training and validation cohorts. The prediction nomogram was finally developed based on four risk factors, including age and distribution, maximum diameter and quantity of calcifications. The AUC of the nomogram was 0.799 (95% CI: 0.761, 0.836) in the training cohort and 0.795 (95% CI: 0.738, 0.852) in the validation cohort.

Conclusion

On mammography, the distribution, maximum diameter and quantity of calcifications are independent predictors of malignant amorphous calcifications and can be easily obtained in the clinic. The nomogram developed in this study for individualized malignancy risk stratification of amorphous calcifications shows good discrimination performance.

Albumin-bilirubin grade-based nomogram of the BCLC system for personalized prognostic prediction in hepatocellular carcinoma

BACKGROUND & AIMS

The prognostic accuracy of individual hepatocellular carcinoma (HCC) patient in each Barcelona Clinic Liver Cancer (BCLC) stage is unclear. We aimed to develop and validate an albumin-bilirubin (ALBI) grade-based nomogram of BCLC to estimate survival for individual HCC patient.

METHODS

Between 2002 and 2016, 3690 patients with newly diagnosed HCC were prospectively enrolled and retrospectively analysed. Patients were randomly split into derivation and validation cohort by 1:1 ratio. Multivariate Cox proportional hazards model was used to generate the nomogram from tumour burden, ALBI grade and performance status (PS). The concordance index and calibration plot were determined to evaluate the performance of this nomogram.

RESULTS

Beta coefficients from the Cox model were used to assign nomogram points to different degrees of tumour burden, ALBI grade and PS. The scores of the nomogram ranged from 0 to 24, and were used to predict 3- and 5-year patient survival. The concordance index of this nomogram was 0.77 (95% confidence interval [CI]: 0.71-0.81) in the derivation cohort and 0.76 (95% CI: 0.71-0.81) in the validation cohort. The calibration plots to predict both 3- and 5-year survival rate well matched with the 45-degree ideal line for both cohorts, except for ALBI-based BCLC stage 0 in the validation cohort.

CONCLUSIONS

The proposed ALBI-based nomogram of BCLC system is a simple and feasible strategy in the precision medicine era. Our data indicate it is a straightforward and user-friendly prognostic tool to estimate the survival of individual HCC patient except for very early stage patients.