Factors Affecting Pathologic Complete Response Following Neoadjuvant Chemotherapy in Breast Cancer: Development and Validation of a Predictive Nomogram

A clinicopathological-imaging nomogram for the prediction of pathological complete response in breast cancer cases administered neoadjuvant therapy

OBJECTIVE

To construct a user-friendly nomogram with MRI and clinicopathological parameters for the prediction of pathological complete response (pCR) after neoadjuvant therapy (NAT) in patients with breast cancer (BC).

METHODS

We retrospectively enrolled consecutive female patients pathologically confirmed with breast cancer who received NAT followed by surgery between January 2018 and December 2022 as the development cohort. Additionally, we prospectively collected eligible candidates between January 2023 and December 2023 as an external validation group at our institution. Pretreatment MRI features and clinicopathological variables were collected, and the pre- and post-treatment background parenchymal enhancement (BPE) and the changes in BPE on two MRIs were compared between patients who achieved pCR and those who did not. Multivariable logistic regression analysis was used to identify independent variables associated with pCR in the development cohort. These independent variables were combined into a predictive nomogram for which performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plot, decision curve analysis, and external validation.

RESULTS

In the development cohort, there were a total of 276 female patients with a mean age of 48.3 ± 8.7 years, while in the validation cohort, there were 87 female patients with a mean age of 49.0 ± 9.5 years. Independent prognostic factors of pCR included small tumor size, HER2(+), high Ki-67 index,high signal enhancement ratio (SER), low minimum value of apparent diffusion coefficient (ADCmin), and significantly decreased BPE after NAT(change of BPE). The nomogram, which incorporates the above parameters, demonstrated excellent predictive performance in both the development and external validation cohorts, with AUC values of 0.900 and 0.850, respectively. Additionally, the nomogram showed excellent calibration capacities, as indicated by Hosmer-Lemeshow test p values of 0.508 and 0.423 in the two cohorts. Furthermore, the nomogram provided greater net benefits compared to the default simple schemes in both cohorts.

CONCLUSION

A nomogram constructed using tumor size, HER2 status, Ki-67 index, SER, ADCmin, and changes in pre- and post-NAT BPE demonstrated strong predictive performance, calibration ability, and greater net benefits for predicting pCR in patients with BC after NAT. This suggests that the user-friendly nomogram could be a valuable imaging biomarker for identifying suitable candidates for NAT.

Photoacoustic Imaging Radiomics to Identify Breast Cancer in BI-RADS 4 or 5 Lesions

OBJECTIVES

To develop a nomogram based on photoacoustic imaging (PAI) radiomics and BI-RADs to identify breast cancer (BC) in BI-RADS 4 or 5 lesions detected by ultrasound (US).

METHODS

In this retrospective study, 119 females with 119 breast lesions at US and PAI examination were included (January 2022 to December 2022). Patients were divided into the training set (n = 83) or testing set (n = 36) to develop a nomogram to identify BC in BI-RADS 4 or 5 lesions. Relevant factors at clinic, BI-RADS category, and PAI were reviewed. Univariate and multivariate regression was used to evaluate factors for associations with BC. To evaluate the diagnostic performance of nomogram, the area under the curve (AUC) of receiver operating characteristic curve, accuracy, specificity and sensitivity was employed.

RESULTS

The nomogram that included BI-RADS category and PAI radiomics score demonstrated a high AUC of 0.925 (95%CI: 0.8467-0.9712) in the training set and 0.926 (95%CI: 0.846-1.000) in the test set. The nomogram also showed significantly better discrimination than the radiomics score (P = .048) or BI-RADS category (P = .009) in the training set. These significant differences were demonstrated in the testing set, outperform the radiomics score (P = .038) and BI-RADS category (P = .013).

CONCLUSIONS

The nomogram developed with BI-RADS and PAI radiomics score can effectively identify BC in BI-RADS 4 or 5 lesions. This technique has the potential to further improve early diagnostic accuracy for BC.

Association between programmed cell death ligand-1 expression in patients with cervical cancer and apparent diffusion coefficient values: a promising tool for patient´s immunotherapy selection

OBJECTIVE

To investigate the associations between apparent diffusion coefficient (ADC) values extracted from three different region of interest (ROI) position approaches and programmed cell death ligand-1 (PD-L1) expression, and evaluate the performance of the nomogram established based on ADC values and clinicopathological parameters in predicting PD-L1 expression in cervical cancer (CC) patients.

METHODS

Through retrospective recruitment, a training cohort of 683 CC patients was created, and a validation cohort of 332 CC patients was prospectively recruited. ROIs were delineated using three different methods to measure the mean ADC (ADCmean), single-section ADC (ADCss), and the minimum ADC of tumors (ADCmin). Logistic regression was employed to identify independent factors related to PD-L1 expression. A nomogram was drawn based on ADC values combined with clinicopathological features, its discrimination and calibration performances were estimated using the area under the curve (AUC) of receiver operating characteristic and calibration curve. The clinical benefits were evaluated by decision curve analysis.

RESULTS

The ADCmin independently correlated with PD-L1 expression. The nomogram constructed with ADCmin and other independent clinicopathological-related factors: FIGO staging, pathological grade, parametrial invasion, and lymph node status demonstrated excellent diagnostic performance (AUC = 0.912 and 0.903, respectively), good calibration capacities, and greater net benefits compared to the clinicopathological model in both the training and validation cohorts.

CONCLUSION

ADCmin independently correlated PD-L1 expression, and the nomogram established with ADCmin and clinicopathological independent prognostic factors had a strong predictive performance for PD-L1 expression, thereby serving as a promising tool for selecting cases eligible for immunotherapy.

CLINICAL RELEVANCE STATEMENT

The minimum ADC can serve as a reliable imaging biomarker related to PD-L1 expression; the established nomogram combines the minimum ADC and clinicopathological factors that can assist clinical immunotherapy decisions.

Automated rating of background parenchymal enhancement in MRI of extremely dense breasts without compromising the association with breast cancer in the DENSE trial

OBJECTIVES

Background parenchymal enhancement (BPE) on dynamic contrast-enhanced MRI (DCE-MRI) as rated by radiologists is subject to inter- and intrareader variability. We aim to automate BPE category from DCE-MRI.

METHODS

This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. 4553 women with extremely dense breasts who received supplemental breast MRI screening in eight hospitals were included. Minimal, mild, moderate and marked BPE rated by radiologists were used as reference. Fifteen quantitative MRI features of the fibroglandular tissue were extracted to predict BPE using Random Forest, Naïve Bayes, and KNN classifiers. Majority voting was used to combine the predictions. Internal-external validation was used for training and validation. The inverse-variance weighted mean accuracy was used to express mean performance across the eight hospitals. Cox regression was used to verify non inferiority of the association between automated rating and breast cancer occurrence compared to the association for manual rating.

RESULTS

The accuracy of majority voting ranged between 0.56 and 0.84 across the eight hospitals. The weighted mean prediction accuracy for the four BPE categories was 0.76. The hazard ratio (HR) of BPE for breast cancer occurrence was comparable between automated rating and manual rating (HR = 2.12 versus HR = 1.97, P = 0.65 for mild/moderate/marked BPE relative to minimal BPE).

CONCLUSION

It is feasible to rate BPE automatically in DCE-MRI of women with extremely dense breasts without compromising the underlying association between BPE and breast cancer occurrence. The accuracy for minimal BPE is superior to that for other BPE categories.

MRI-based tumor shrinkage patterns after early neoadjuvant therapy in breast cancer: correlation with molecular subtypes and pathological response after therapy

BACKGROUND

MRI-based tumor shrinkage patterns (TSP) after neoadjuvant therapy (NAT) have been associated with pathological response. However, the understanding of TSP after early NAT remains limited. We aimed to analyze the relationship between TSP after early NAT and pathological response after therapy in different molecular subtypes.

METHODS

We prospectively enrolled participants with invasive ductal breast cancers who received NAT and performed pretreatment DCE-MRI from September 2020 to August 2022. Early-stage MRIs were performed after the first (1st-MRI) and/or second (2nd-MRI) cycle of NAT. Tumor shrinkage patterns were categorized into four groups: concentric shrinkage, diffuse decrease (DD), decrease of intensity only (DIO), and stable disease (SD). Logistic regression analysis was performed to identify independent variables associated with pathologic complete response (pCR), and stratified analysis according to tumor hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) disease subtype.

RESULTS

344 participants (mean age: 50 years, 113/345 [33%] pCR) with 345 tumors (1 bilateral) had evaluable 1st-MRI or 2nd-MRI to comprise the primary analysis cohort, of which 244 participants with 245 tumors had evaluable 1st-MRI (82/245 [33%] pCR) and 206 participants with 207 tumors had evaluable 2nd-MRI (69/207 [33%] pCR) to comprise the 1st- and 2nd-timepoint subgroup analysis cohorts, respectively. In the primary analysis, multivariate analysis showed that early DD pattern (OR = 12.08; 95% CI 3.34-43.75; p < 0.001) predicted pCR independently of the change in tumor size (OR = 1.37; 95% CI 0.94-2.01; p = 0.106) in HR+/HER2- subtype, and the change in tumor size was a strong pCR predictor in HER2+ (OR = 1.61; 95% CI 1.22-2.13; p = 0.001) and triple-negative breast cancer (TNBC, OR = 1.61; 95% CI 1.22-2.11; p = 0.001). Compared with the change in tumor size, the SD pattern achieved a higher negative predictive value in HER2+ and TNBC. The statistical significance of complete 1st-timepoint subgroup analysis was consistent with the primary analysis.

CONCLUSION

The diffuse decrease pattern in HR+/HER2- subtype and stable disease in HER2+ and TNBC after early NAT could serve as additional straightforward and comprehensible indicators of treatment response.

TRIAL REGISTRATION

Trial registration at https://www.chictr.org.cn/ .

REGISTRATION NUMBER

ChiCTR2000038578, registered September 24, 2020.

Deep learning-based predictive model for pathological complete response to neoadjuvant chemotherapy in breast cancer from biopsy pathological images: a multicenter study

Introduction: Early predictive pathological complete response (pCR) is beneficial for optimizing neoadjuvant chemotherapy (NAC) strategies for breast cancer. The hematoxylin and eosin (HE)-stained slices of biopsy tissues contain a large amount of information on tumor epithelial cells and stromal. The fusion of pathological image features and clinicopathological features is expected to build a model to predict pCR of NAC in breast cancer. Methods: We retrospectively collected a total of 440 breast cancer patients from three hospitals who underwent NAC. HE-stained slices of biopsy tissues were scanned to form whole-slide images (WSIs), and pathological images of representative regions of interest (ROI) of each WSI were selected at different magnifications. Based on several different deep learning models, we propose a novel feature extraction method on pathological images with different magnifications. Further, fused with clinicopathological features, a multimodal breast cancer NAC pCR prediction model based on a support vector machine (SVM) classifier was developed and validated with two additional validation cohorts (VCs). Results: Through experimental validation of several different deep learning models, we found that the breast cancer pCR prediction model based on the SVM classifier, which uses the VGG16 model for feature extraction of pathological images at ×20 magnification, has the best prediction efficacy. The area under the curve (AUC) of deep learning pathological model (DPM) were 0.79, 0.73, and 0.71 for TC, VC1, and VC2, respectively, all of which exceeded 0.70. The AUCs of clinical model (CM), a clinical prediction model established by using clinicopathological features, were 0.79 for TC, 0.73 for VC1, and 0.71 for VC2, respectively. The multimodal deep learning clinicopathological model (DPCM) established by fusing pathological images and clinicopathological features improved the AUC of TC from 0.79 to 0.84. The AUC of VC2 improved from 0.71 to 0.78. Conclusion: Our study reveals that pathological images of HE-stained slices of pre-NAC biopsy tissues can be used to build a pCR prediction model. Combining pathological images and clinicopathological features can further enhance the predictive efficacy of the model.

Prediction of pathological complete response of breast cancer patients who received neoadjuvant chemotherapy with a nomogram based on clinicopathologic variables, ultrasound, and MRI

OBJECTIVE

To establish a nomogram for predicting the pathologic complete response (pCR) in breast cancer (BC) patients after NAC by applying magnetic resonance imaging (MRI) and ultrasound (US).

METHODS

A total of 607 LABC women who underwent NAC before surgery between January 2016 and June 2022 were retrospectively enrolled, and then were randomly divided into the training (n = 425) and test set (n = 182) with the ratio of 7:3. MRI and US variables were collected before and after NAC, as well as the clinicopathologic features. Univariate and multivariate logistic regression analyses were applied to confirm the potentially associated predictors of pCR. Finally, a nomogram was developed in the training set with its performance evaluated by the area under the receiver operating characteristics curve (ROC) and validated in the test set.

RESULTS

Of the 607 patients, 108 (25.4%) achieved pCR. Hormone receptor negativity (odds ratio [OR], 0.3; P < .001), human epidermal growth factor receptor 2 positivity (OR, 2.7; P = .001), small tumour size at post-NAC US (OR, 1.0; P = .031), tumour size reduction ≥50% at MRI (OR, 9.8; P < .001), absence of enhancement in the tumour bed at post-NAC MRI (OR, 8.1; P = .003), and the increase of ADC value after NAC (OR, 0.3; P = .035) were all significantly associated with pCR. Incorporating the above variables, the nomogram showed a satisfactory performance with an AUC of 0.884.

CONCLUSION

A nomogram including clinicopathologic variables and MRI and US characteristics shows preferable performance in predicting pCR.

ADVANCES IN KNOWLEDGE

A nomogram incorporating MRI and US with clinicopathologic variables was developed to provide a brief and concise approach in predicting pCR to assist clinicians in making treatment decisions early.

Development and Validation of a Nomogram for Predicting Survival Based on Ferritin and Transferrin Ratio in Breast Cancer Patients

BACKGROUND

Our objective is to develop a predictive model utilizing the ferritin and transferrin ratio (FTR) and clinical factors to forecast overall survival (OS) in breast cancer (BC) patients.

METHODS

We conducted a retrospective analysis of clinical data from 2858 BC patients diagnosed between 2013 and 2021. Subsequently, the cohort of 2858 BC patients underwent random assignment into distinct subsets: a training cohort comprising 2002 patients and a validation cohort comprising 856 patients, maintaining a proportional ratio of 7:3. Employing multivariable Cox regression analysis within the training cohort, we derived a prognostic nomogram. The predictive performance was assessed using calibration curves, C-index, and decision curve analysis.

RESULTS

The final prognostic model included the TNM stage, subtype, hemoglobin levels, and the ferritin-transferrin ratio. The nomogram achieved a C-index of .794 (95% CI: .777-.810). The nomogram demonstrated superior predictive accuracy for OS at 3, 5, and 7 years for BC, with area under the time-dependent curves of .812, .782, and .773, respectively. These values notably outperformed those of the conventional TNM stage. Decision curve analysis reaffirmed the greater net benefit of our nomogram compared to the TNM stage. These findings were subsequently validated in the independent validation cohort.

CONCLUSION

The FTR-based prognostic model may predict a patient's OS better than the TNM stage in a clinical setting. The nomogram can provide an early, affordable, and reliable tool for survival prediction, as well as aid clinicians in treatment option-making and prognosis evaluation. However, further multi-center prospective trials are required to confirm the reliability of the existing nomogram.

Treatment of residual disease following neoadjuvant therapy in breast cancer

Substantial advances have been made in the systemic treatment of breast cancer with residual disease following neoadjuvant therapy. We reviewed recent and ongoing studies informing the standard clinical management of residual disease by subtype: HER2+, TNBC, and HR+/HER2-, as well as strategies for BRCA+ disease. We conclude with a discussion of ongoing clinical trials and current controversies regarding the treatment of residual disease in breast cancer.

Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy

OBJECTIVES

To evaluate the association between pretreatment MRI descriptors and breast cancer (BC) pathological complete response (pCR) to neoadjuvant chemotherapy (NAC).

MATERIALS AND METHODS

Patients with BC treated by NAC with a breast MRI between 2016 and 2020 were included in this retrospective observational single-center study. MR studies were described using the standardized BI-RADS and breast edema score on T2-weighted MRI. Univariable and multivariable logistic regression analyses were performed to assess variables association with pCR according to residual cancer burden. Random forest classifiers were trained to predict pCR on a random split including 70% of the database and were validated on the remaining cases.

RESULTS

Among 129 BC, 59 (46%) achieved pCR after NAC (luminal (n = 7/37, 19%), triple negative (n = 30/55, 55%), HER2 + (n = 22/37, 59%)). Clinical and biological items associated with pCR were BC subtype (p < 0.001), T stage 0/I/II (p = 0.008), higher Ki67 (p = 0.005), and higher tumor-infiltrating lymphocytes levels (p = 0.016). Univariate analysis showed that the following MRI features, oval or round shape (p = 0.047), unifocality (p = 0.026), non-spiculated margins (p = 0.018), no associated non-mass enhancement (p = 0.024), and a lower MRI size (p = 0.031), were significantly associated with pCR. Unifocality and non-spiculated margins remained independently associated with pCR at multivariable analysis. Adding significant MRI features to clinicobiological variables in random forest classifiers significantly increased sensitivity (0.67 versus 0.62), specificity (0.69 versus 0.67), and precision (0.71 versus 0.67) for pCR prediction.

CONCLUSION

Non-spiculated margins and unifocality are independently associated with pCR and can increase models performance to predict BC response to NAC.

CLINICAL RELEVANCE STATEMENT

A multimodal approach integrating pretreatment MRI features with clinicobiological predictors, including tumor-infiltrating lymphocytes, could be employed to develop machine learning models for identifying patients at risk of non-response. This may enable consideration of alternative therapeutic strategies to optimize treatment outcomes.

KEY POINTS

• Unifocality and non-spiculated margins are independently associated with pCR at multivariable logistic regression analysis. • Breast edema score is associated with MR tumor size and TIL expression, not only in TN BC as previously reported, but also in luminal BC. • Adding significant MRI features to clinicobiological variables in machine learning classifiers significantly increased sensitivity, specificity, and precision for pCR prediction.

Systematic Review of Nomograms Used for Predicting Pathological Complete Response in Early Breast Cancer

Pathological complete response (pCR) is an important surrogate outcome to assess the effects of neoadjuvant chemotherapy (NAC). Nomograms to predict pCR have been developed with local data to better select patients who are likely to benefit from NAC; however, they were never critically reviewed regarding their internal and external validity. The purpose of this systematic review was to critically appraise nomograms published in the last 20 years (2010-2022). Articles about nomograms were searched in databases, such as PubMed/MEDLINE, Embase and Cochrane. A total of 1120 hits were found, and seven studies were included for analyses. No meta-analysis could be performed due to heterogeneous reports on outcomes, including the definition of pCR and subtypes. Most nomograms were developed in Asian centers, and nonrandomized retrospective cohorts were the most common sources of data. The most common subtype included in the studies was triple negative (50%). There were articles that included HER2+ (>80%). In one study, scholars performed additional validation of the nomogram using DFS and OS as outcomes; however, there was a lack of clarity on how such endpoints were measured. Nomograms to predict pCR cannot be extrapolated to other settings due to local preferences/availability of NAC. The main gaps identified in this review are also opportunities for future nomogram research and development.

Accuracy of Preoperative Contrast-enhanced Cone Beam Breast CT in Assessment of Residual Tumor after Neoadjuvant Chemotherapy: A Comparative Study with Breast MRI

RATIONALE AND OBJECTIVES

To compare the accuracy of preoperative contrast-enhanced cone beam breast CT (CE-CBBCT) and MRI in assessment of residual tumor after neoadjuvant chemotherapy (NAC).

MATERIALS AND METHODS

Residual tumor assessments in 91 female patients were performed on preoperative CE-CBBCT and MRI images after NAC. The agreements of tumor size between imaging and pathology were tested by Intraclass Correlation Coefficient (ICC). Subgroup analyses were set according to ductal carcinoma in situ (DCIS), calcifications and molecular subtypes. Correlated-samples Wilcoxon Signed-rank test was used to analyze the difference between imaging and pathology in total and subgroups. AUC, sensitivity, specificity, PPV, and NPV were calculated to compare the performance of CE-CBBCT and MRI in predicting pathological complete response (pCR).

RESULTS

Comparing with pathology, the agreement on CE-CBBCT was good (ICC = 0.64, 95% CI, 0.35-0.78), whereas on MRI was moderate (ICC = 0.59, 95% CI, 0.36-0.77), and overestimation on CE-CBBCT was less than that on MRI (median (interquartile range, IQR): 0.24 [0.00, 1.31] cm vs. 0.67 [0.00, 1.81] cm; p = 0.000). In subgroup analysis, CE-CBBCT showed superior accuracy than MRI when residual DCIS (p = 0.000) and calcifications (p = 0.000) contained, as well as luminal A (p = 0.043) and luminal B (p = 0.009) breast cancer. CE-CBBCT and MRI performed comparable in predicting pCR, AUCs were 0.749 and 0.733 respectively (p > 0.05).

CONCLUSION

CE-CBBCT showed superior accuracy in assessment of residual tumor compared with MRI, especially when residual DCIS or calcifications contained and luminal subtype. The performance of preoperative CE-CBBCT in predicting pCR was comparable to MRI. CE-CBBCT could be an alternative method used for preoperative assessment after NAC.

Use of Pretreatment Multiparametric MRI to Predict Tumor Regression Pattern to Neoadjuvant Chemotherapy in Breast Cancer

RATIONALE AND OBJECTIVES

To develop an easy-to-use model by combining pretreatment MRI and clinicopathologic features for early prediction of tumor regression pattern to neoadjuvant chemotherapy (NAC) in breast cancer.

MATERIALS AND METHODS

We retrospectively analyzed 420 patients who received NAC and underwent definitive surgery in our hospital from February 2012 to August 2020. Pathologic findings of surgical specimens were used as the gold standard to classify tumor regression patterns into concentric and non-concentric shrinkage. Morphologic and kinetic MRI features were both analyzed. Univariable and multivariable analyses were performed to select the key clinicopathologic and MRI features for pretreatment prediction of regression pattern. Logistic regression and six machine learning methods were used to construct prediction models, and their performance were evaluated with receiver operating characteristic curve.

RESULTS

Two clinicopathologic variables and three MRI features were selected as independent predictors to construct prediction models. The apparent area under the curve (AUC) of seven prediction models were in the range of 0.669-0.740. The logistic regression model yielded an AUC of 0.708 (95% confidence interval [CI]: 0.658-0.759), and the decision tree model achieved the highest AUC of 0.740 (95% CI: 0.691-0.787). For internal validation, the optimism-corrected AUCs of seven models were in the range of 0.592-0.684. There was no significant difference between the AUCs of the logistic regression model and that of each machine learning model.

CONCLUSION

Prediction models combining pretreatment MRI and clinicopathologic features are useful for predicting tumor regression pattern in breast cancer, which can assist to select patients who can benefit from NAC for de-escalation of breast surgery and modify treatment strategy.

Development and External Validation of a Machine Learning Model to Predict Pathological Complete Response After Neoadjuvant Chemotherapy in Breast Cancer

PURPOSE

Several predictive models have been developed to predict the pathological complete response (pCR) after neoadjuvant chemotherapy (NAC); however, few are broadly applicable owing to radiologic complexity and institution-specific clinical variables, and none have been externally validated. This study aimed to develop and externally validate a machine learning model that predicts pCR after NAC in patients with breast cancer using routinely collected clinical and demographic variables.

METHODS

The electronic medical records of patients with advanced breast cancer who underwent NAC before surgical resection between January 2017 and December 2020 were reviewed. Patient data from Seoul National University Bundang Hospital were divided into training and internal validation cohorts. Five machine learning techniques, including gradient boosting machine (GBM), support vector machine, random forest, decision tree, and neural network, were used to build predictive models, and the area under the receiver operating characteristic curve (AUC) was compared to select the best model. Finally, the model was validated using an independent cohort from Seoul National University Hospital.

RESULTS

A total of 1,003 patients were included in the study: 287, 71, and 645 in the training, internal validation, and external validation cohorts, respectively. Overall, 36.3% of the patients achieved pCR. Among the five machine learning models, the GBM showed the highest AUC for pCR prediction (AUC, 0.903; 95% confidence interval [CI], 0.833-0.972). External validation confirmed an AUC of 0.833 (95% CI, 0.800-0.865).

CONCLUSION

Commonly available clinical and demographic variables were used to develop a machine learning model for predicting pCR following NAC. External validation of the model demonstrated good discrimination power, indicating that routinely collected variables were sufficient to build a good prediction model.

Prediction of Tumor Progression During Neoadjuvant Chemotherapy and Survival Outcome in Patients With Triple-Negative Breast Cancer

OBJECTIVE

To investigate the association of clinical, pathologic, and magnetic resonance imaging (MRI) variables with progressive disease (PD) during neoadjuvant chemotherapy (NAC) and distant metastasis-free survival (DMFS) in patients with triple-negative breast cancer (TNBC).

MATERIALS AND METHODS

This single-center retrospective study included 252 women with TNBC who underwent NAC between 2010 and 2019. Clinical, pathologic, and treatment data were collected. Two radiologists analyzed the pre-NAC MRI. After random allocation to the development and validation sets in a 2:1 ratio, we developed models to predict PD and DMFS using logistic regression and Cox proportional hazard regression, respectively, and validated them.

RESULTS

Among the 252 patients (age, 48.3 ± 10.7 years; 168 in the development set; 84 in the validation set), PD was occurred in 17 patients and 9 patients in the development and validation sets, respectively. In the clinical-pathologic-MRI model, the metaplastic histology (odds ratio [OR], 8.0; P = 0.032), Ki-67 index (OR, 1.02; P = 0.044), and subcutaneous edema (OR, 30.6; P = 0.004) were independently associated with PD in the development set. The clinical-pathologic-MRI model showed a higher area under the receiver-operating characteristic curve (AUC) than the clinical-pathologic model (AUC: 0.69 vs. 0.54; P = 0.017) for predicting PD in the validation set. Distant metastases occurred in 49 patients and 18 patients in the development and validation sets, respectively. Residual disease in both the breast and lymph nodes (hazard ratio [HR], 6.0; P = 0.005) and the presence of lymphovascular invasion (HR, 3.3; P < 0.001) were independently associated with DMFS. The model consisting of these pathologic variables showed a Harrell's C-index of 0.86 in the validation set.

CONCLUSION

The clinical-pathologic-MRI model, which considered subcutaneous edema observed using MRI, performed better than the clinical-pathologic model for predicting PD. However, MRI did not independently contribute to the prediction of DMFS.

Evaluation of Multiparametric MRI Radiomics-Based Nomogram in Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Two-Center study

INTRODUCTION

This study evaluated the performance of primary foci of breast cancer on multiparametric magnetic resonance imaging (MRI) contributing to establish and validate radiomics-based nomograms for predicting the different pathological outcome of breast cancer patients after neoadjuvant chemotherapy (NAC).

MATERIALS AND METHODS

Retrospectively collected 387 patients with locally advanced breast cancer, all treated with NAC and received breast dynamic contrast-enhanced MRI (DCE-MRI) before NAC. Radiomics signatures were extracted from region of interest (ROI) on multiparametric MRI to build rad score. Clinical-pathologic data and radiological features established the clinical model. The comprehensive model featured rad-score, predictive clinical-pathologic data and radiological features, which was ultimately displayed as a nomogram. Patients were grouped in 2 different ways in accordance with the Miller-Payne (MP) grading of surgical specimens. The first grouping method: 181 patients with pathological reaction grades Ⅳ∼Ⅴ were included in the significant remission group, while 206 patients with pathological reaction grades Ⅰ∼Ⅲ were included in the nonsignificant remission group. The second grouping method: 117 patients with pathological complete response (pCR) were assigned to the pCR group, and 270 patients who failed to meet pCR were assigned to in the non-pCR group. Two combined nomograms are created from 2 grouped data for predicting different pathological responses to NAC. The area under the curves (AUC) of the receiver operating characteristic curves (ROC) were used to evaluate the performance of each model. While decision curve analysis (DCA) and calibration curves were used for estimating the clinical application value of the nomogram.

RESULTS

Two combined nomograms embodying rad score and clinical-pathologic data outperformed, showing good calibrations for predicting response to NAC. The combined nomogram predicting pCR showed the best performance with the AUC values of 0.97, 0.90 and 0.86 in the training, testing, and external validation cohorts respectively. The AUC values of another combined nomogram predicting significant remission: 0.98, 0.88 0.80 in the training, testing and external validation cohorts. DCA showed the comprehensive model nomogram obtained the most clinical benefit.

CONCLUSIONS

The combined nomogram could preoperatively predict significant remission or even pCR to NAC in breast cancer based on multiparametric MRI and clinical-pathologic data.

Correlation of Mammographic Microcalcifications with Final Surgical Pathology After Neoadjuvant Chemotherapy for Breast Cancer

INTRODUCTION

Imaging guidelines for post-neoadjuvant chemotherapy (NAC) breast cancer patients lack specificity on appropriateness and utility of individual modalities for surgical planning. Microcalcifications confound mammographic interpretation. We examined the correlation between the mammographic extent of microcalcifications present post-NAC, corresponding magnetic resonance imaging (MRI) lesions, and definitive surgical pathology.

METHODS

In this retrospective cohort study, patients with calcifications on mammography were collected from a database of consecutive breast cancer patients receiving NAC. The primary objective was to determine the correlation between maximum dimension of post-NAC calcifications with surgical pathology (invasive disease, tumor bed, and ductal carcinoma in situ [DCIS]), stratified by tumor receptor subgroup. Secondarily, we examined the correlation of residual disease with MRI mass enhancement (ME) and non-ME (NME). Pearson's correlation coefficient was used to evaluate statistical significance (strong: R² ≥70%; moderate: R²=25-70%; weak: R² ≤25%).

RESULTS

Overall, 186 patients met the inclusion criteria. Mammographic calcifications correlated poorly with invasive disease (R² = 10.8%), overestimating by 57%. In patients with calcifications on mammography, MRI ME and NME correlated weakly with the maximum dimension of invasive disease and DCIS. In triple-negative breast cancer (TNBC) patients, invasive disease correlated strongly with the maximum dimension of calcifications (R² = 83%) and moderately with ME (R² = 37.7%) and NME (R² = 28.4%).

CONCLUSION

Overall, current imaging techniques correlate poorly and overestimate final surgical pathology. This poor correlation may lead to uncertainty in the extent of required surgical excision and the exclusion of potential candidates for non-surgical management in ongoing trials. TNBCs would be good candidates for these trials given the stronger observed correlations between pathology and imaging.

Survival Nomogram for Patients With Locally Advanced Breast Cancer Undergoing Immediate Breast Reconstruction: A SEER Population-Based Study

INTRODUCTION/BACKGROUND

This study aimed to construct a nomogram to provide prognostic references for patients with locally advanced breast cancer (LABC) to receive immediate breast reconstruction (IBR).

MATERIALS AND METHODS

All data were obtained from the Surveillance, Epidemiology and End Results (SEER) database. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and best subset regression (BSR), separately followed by backward stepwise multivariable Cox, were used to construct the nomogram. Risk stratification was established after validation.

RESULTS

A total of 6,285 patients were enrolled to generate the training group (n = 3,466) and the test group (n = 2,819) by geographical split. Age, marital status, grade, T staging, N staging, radiotherapy, chemotherapy, estrogen receptor status (ER), progesterone receptor status (PR) and human epidermal growth factor receptor type 2 status (HER2) were used to fit the nomogram. The overall Harrell's concordance index (C-index) was 0.772 in the training group and 0.762 in the test group. The area under the receiver operator characteristic curves (AUC) at 3-year and 5-year were respectively 0.824 and 0.720 in the training group, 0.792 and 0.733 in the test group. The calibration curves showed great consistency in both groups. A dynamic nomogram (https://dcpanfromsh.shinyapps.io/NomforLABCafterIBR/) was developed.

CONCLUSION

A nomogram was developed and validated that predicts prognosis more accurately than the AJCC 7th stage and can be used as a reference for decision-making in LABC patients receiving IBR.

The Role of Nomogram Based on the Combination of Ultrasound Parameters and Clinical Indicators in the Degree of Pathological Remission of Breast Cancer

Background

The mortality rate of breast cancer (BC) ranks first among female tumors worldwide and presents a trend of younger age, which poses a great threat to women's health and life. Neoadjuvant chemotherapy (NAC) for breast cancer is defined as the first step of treatment for breast cancer patients without distant metastasis before planned surgical treatment or local treatment with surgery and radiotherapy. According to the current NCCN guidelines, patients with different molecular types of BC should receive neoadjuvant chemotherapy (NAC), which can not only achieve tumor downstaging, increase the chance of surgery, and improve the breast-conserving rate. In addition, it can identify new genetic pathways and drugs related to cancer, improve patient survival rate, and make new progress in breast cancer management.

Objective

To explore the role of the nomogram established by the combination of ultrasound parameters and clinical indicators in the degree of pathological remission of breast cancer.

Methods

A total of 147 breast cancer patients who received neoadjuvant chemotherapy and elective surgery in the Department of Ultrasound, Nantong Cancer Hospital, from May 2014 to August 2021 were retrospectively included. Postoperative pathological remission was divided into two groups according to Miller-Payne classification: no significant remission group (NMHR group, n = 93) and significant remission group (MHR group, n = 54). Clinical characteristics of patients were recorded and collected. The multivariate logistic regression model was used to screen the information features related to the MHR group, and then, a nomogram model was constructed; ROC curve area, consistency index (C-index, CI), calibration curve, and H-L test were used to evaluate the model. And the decision curve is used to compare the net income of the single model and composite model.

Results

Among 147 breast cancer patients, 54 (36.7%) had pathological remission. Multivariate logistic regression showed that ER, reduction/disappearance of strong echo halo, Adler classification after NAC, PR + CR, and morphological changes were independent risk factors for pathological remission (P < 0.05). Based on these factors, the nomogram was constructed and verified. The area under the curve (AUC) and CI were 0.966, the sensitivity and specificity were 96.15% and 92.31%, and the positive predictive value (PPV) and negative predictive value (NPV) were 87.72% and 97.15%, respectively. The mean absolute error of the agreement between the predicted value and the real value is 0.026, and the predicted risk is close to the actual risk. In the range of HRT of about 0.0∼0.9, the net benefit of the composite evaluation model is higher than that of the single model. H-L test results showed that χ ² = 8.430, P=0.393 > 0.05.

Conclusion

The nomogram model established by combining the changes of ultrasound parameters and clinical indicators is a practical and convenient prediction model, which has a certain value in predicting the degree of pathological remission after neoadjuvant chemotherapy.

Resolution of Nonmass Enhancement Extension to the Nipple at Breast MRI after Neoadjuvant Chemotherapy: Pathologic Response and Feasibility for Nipple-sparing Mastectomy

Background Nipple-sparing mastectomy (NSM) is usually contraindicated in patients with nonmass enhancement (NME) extension to the nipple at breast MRI. However, little is known about the feasibility of NSM when NME extension to the nipple resolves after neoadjuvant chemotherapy (NAC). Purpose To evaluate whether NSM is an appropriate surgical procedure for patients in whom NME extension to the nipple resolves after NAC. Materials and Methods This retrospective study included 383 women with NME at baseline MRI who underwent NAC followed by mastectomy between January 2007 and March 2022 at a single institution. NME extension to the nipple was assessed using breast MRI before NAC (hereafter, pre-NAC) and after NAC (hereafter, post-NAC). In 326 women who underwent mastectomy with removal of the nipple-areolar complex, the rate of pathologic analysis-confirmed tumor invasion of the nipple compared with NME extension to the nipple at post-NAC breast MRI was evaluated. Tumor involvement of the nipple was also assessed in those with complete pathologic response at posttreatment MRI. Furthermore, the outcomes in 57 women undergoing NSM were investigated, particularly in patients with NME extension to the nipple at initial diagnosis. Results Of the 326 women who underwent mastectomy with removal of the nipple-areolar complex (mean age, 49 years ± 9.4 [SD]), 217 patients (67%) showed NME extension to the nipple on pre-NAC MRI scans. Among the 153 women (70%) in whom the NME extension to the nipple resolved after NAC, the rate of pathologic analysis-confirmed tumor invasion of the nipple was 2.6% (four of 153 women; 95% CI: 0, 6.5). No pathologic analysis-confirmed tumor invasion of the nipple was detected in 31 women with complete response at MRI. Of the 57 women who underwent NSM, 12 (21%) with resolution of NME extension to the nipple after NAC had no relapse during the median follow-up of 31 months (range, 11-80 months). Conclusion Pathologic analysis-confirmed tumor invasion of the nipple was rare in women with resolution of nonmass enhancement extension to the nipple after neoadjuvant chemotherapy (NAC). Therefore, nipple-sparing mastectomy could be feasible in this population, especially in those with complete MRI response to NAC. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Lee in this issue.

Multiparametric MRI radiomics fusion for predicting the response and shrinkage pattern to neoadjuvant chemotherapy in breast cancer

Purpose

During neoadjuvant chemotherapy (NACT), breast tumor morphological and vascular characteristics are usually changed. This study aimed to evaluate the tumor shrinkage pattern and response to NACT by preoperative multiparametric magnetic resonance imaging (MRI), including dynamic contrast-enhanced MRI (DCE-MRI), diffuse weighted imaging (DWI) and T2 weighted imaging (T2WI).

Method

In this retrospective analysis, female patients with unilateral unifocal primary breast cancer were included for predicting tumor pathologic/clinical response to NACT (n=216, development set, n=151 and validation set, n=65) and for discriminating the tumor concentric shrinkage (CS) pattern from the others (n=193; development set, n=135 and validation set, n=58). Radiomic features (n=102) of first-order statistical, morphological and textural features were calculated on tumors from the multiparametric MRI. Single- and multiparametric image-based features were assessed separately and were further combined to feed into a random forest-based predictive model. The predictive model was trained in the testing set and assessed on the testing dataset with an area under the curve (AUC). Molecular subtype information and radiomic features were fused to enhance the predictive performance.

Results

The DCE-MRI-based model showed higher performance (AUCs of 0.919, 0.830 and 0.825 for tumor pathologic response, clinical response and tumor shrinkage patterns, respectively) than either the T2WI or the ADC image-based model. An increased prediction performance was achieved by a model with multiparametric MRI radiomic feature fusion.

Conclusions

All these results demonstrated that multiparametric MRI features and their information fusion could be of important clinical value for the preoperative prediction of treatment response and shrinkage pattern.

Survival analysis and prognosis of patients with breast cancer with pleural metastasis

Background

Breast cancer (BC) is the most common malignant cancer. The prognosis of patients differs according to the location of distant metastasis, with pleura being a common metastatic site in BC. Nonetheless, clinical data of patients with pleural metastasis (PM) as the only distant metastatic site at initial diagnosis of metastatic BC (MBC) are limited.

Patient cohort and methods

The medical records of patients who were hospitalized in Shandong Cancer Hospital between January 1, 2012 and December 31, 2021 were reviewed, and patients eligible for the study were selected. Survival analysis was conducted using Kaplan-Meier (KM) method. Univariate and multivariate Cox proportional-hazards models were used to identify prognostic factors. Finally, based on these selected factors, a nomogram was constructed and validated.

Results

In total, 182 patients were included; 58 (group A), 81 (group B), and 43 (group C) patients presented with only PM, only lung metastasis (LM), and PM combined with LM, respectively. The KM curves revealed no significant difference in overall survival (OS) among the three groups. However, in terms of survival after distant metastasis (M-OS), the difference was significant: patients with only PM exhibited the best prognosis, whereas those with PM combined with LM exhibited the worst prognosis (median M-OS: 65.9, 40.5, and 32.4 months, respectively; P = 0.0067). For patients with LM in groups A and C, those with malignant pleural effusion (MPE) exhibited significantly worse M-OS than those without MPE. Univariate and multivariate analyses indicated that primary cancer site, T stage, N stage, location of PM, and MPE were independent prognostic factors for patients with PM without other distant metastasis. A nomogram prediction model incorporating these variables was created. According to the C-index (0.776), the AUC values of the 3-, 5-, and 8-year M-OS (0.86, 0.86, and 0.90, respectively), and calibration curves, the predicted and actual M-OS were in good agreement.

Conclusion

BC patients with PM only at the first diagnosis of MBC exhibited a better prognosis than those with LM only or PM combined with LM. We identified five independent prognostic factors associated with M-OS in this subset of patients, and a nomogram model with good predictive efficacy was established.

Potential Impact of Preoperative Circulating Biomarkers on Individual Escalating/de-Escalating Strategies in Early Breast Cancer

The research on non-invasive circulating biomarkers to guide clinical decision is in wide expansion, including the earliest disease settings. Several new intensification/de-intensification strategies are approaching clinical practice, personalizing the treatment for each patient. Moreover, liquid biopsy is revealing its potential with multiple techniques and studies available on circulating biomarkers in the preoperative phase. Inflammatory circulating cells, circulating tumor cells (CTCs), cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), and other biological biomarkers are improving the armamentarium for treatment selection. Defining the escalation and de-escalation of treatments is a mainstay of personalized medicine in early breast cancer. In this review, we delineate the studies investigating the possible application of these non-invasive tools to give a more enlightened approach to escalating/de-escalating strategies in early breast cancer.

Risk prediction model of polypharmacy for community-dwelling elderly patients: An assessment tool for early detection

Background: Polypharmacy has become a major and growing public health issue, with significant implications for health outcomes and expenditure on healthcare resources. In this study, a risk prediction model of polypharmacy represented by a nomogram for community-dwelling elderly patients based on the Chinese population was constructed.

Methods: A cross-sectional study was conducted in Shanghai, China. The variables data affecting polypharmacy were fetched from the information system database of health government departments in Shanghai. The Least Absolute Shrinkage Selection Operator (LASSO) regression analysis was used to select the predictor variables, and multivariate logistic regression was used to establish the prediction model. A visual tool of the nomogram was established for predicting the risk of polypharmacy in the elderly population. In addition, the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to estimate the performance of the model.

Results: A total of 80,012 elderly patients were included in this study. Eight variables, containing age, residential area, preferred medical institutions, number of visits to tertiary hospitals, number of visits to secondary hospitals, number of visits to community health centers, number of diagnoses, and main types of disease, were included in the risk prediction model of nomogram. The area under the curve (AUC) of the nomogram was 0.782 in both sets, demonstrating that the model has a good discriminant ability. The calibration chart shows that the prediction model fits well with the validation set. DCA results displayed that the threshold probabilities of the two sets in the prediction model reached up to 90%, implying that the model had a preferable application value.

Conclusion: This study explored the risk factors for polypharmacy among the elderly in Shanghai, China, and applied the nomogram to establish a predictive model via eight variables, which provided an effective tool for early screening and timely prevention of polypharmacy. Family physicians or pharmacists could scientifically use the tool to closely observe community-dwelling elderly patients, decreasing the adverse health effects caused by medication for the elderly.

Development and validation of novel risk prediction models of breast cancer based on stanniocalcin‐1 level

PURPOSE

The function of stanniocalcin-1 (STC-1) in the oncogenesis and progression of tumors has been extensively studied. The purpose of this study was to investigate the relationship between secreted STC-1 and prognosis in patients with breast cancer (BC) and to determine whether STC-1 could be a key prognostic factor in BC.

METHODS

The STC-1 level was measured by ELISA and clinical data from 1210 female patients with BC were used to develop and validate nomograms. We then verified the models through the plotting of ROC curves and calibration curves, calculating the C-index, and performing decision curve analyses (DCA).

RESULTS

The level of STC-1 in the peripheral plasma was significantly correlated with the T stage, N stage, clinical stage, grade, hormone receptors, HER-2 status, and tumor subtype. Cox regression analyses revealed that estrogen receptor(ER) status, N stage, and STC-1 level were risk factors for overall survival (OS), whereas T stage, N stage, and STC-1 level were independent prognostic factors for distant disease-free survival (DDFS) and disease-free survival (DFS). Both the ROC curve and the C-index confirmed the high resolution of these models, while the DCA identified the feasibility of their practical application. In addition, the calibration curves indicated good consistency between the predicted and actual survival rates.

CONCLUSION

Nomograms were created based on STC-1 levels for 3-, 5-, and 7-year OS, DDFS, and DFS of patients with BC respectively. As a key prognostic factor for BC, peripheral blood STC-1 level can be used clinically as a liquid biopsy indicator.

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study

INTRODUCTION

Predicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs.

MATERIALS AND METHODS

We retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs.

RESULTS

The WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80).

CONCLUSION

Our study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.

Prediction model based on preoperative CT findings for carotid artery invasion in patients with head and neck masses

Objectives

To investigate the performance of a model in predicting carotid artery (CA) invasion in patients with head and neck masses using computed tomography (CT).

Methods

This retrospective study included patients with head and neck masses who underwent CT and surgery between January 2013 and July 2021. Patient characteristics and ten CT features were assessed by two radiologists. The patients were randomly allocated to a training cohort (n=106) and a validation cohort (n=109). Independent risk factors for CA invasion were assessed by univariate and multivariate logistic regression analyses. The predictive model was established as a nomogram using the training cohort. In addition, the calibration, discrimination, reclassification, and clinical application of the model were assessed in the validation cohort.

Results

A total of 215 patients were evaluated, including 54 patients with CA invasion. Vascular wall deformation (odds ratio [OR], 7.17; p=0.02) and the extent of encasement to the CA (OR, 1.02; p&lt;0.001) were independent predictors of CA invasion in the multivariable analysis in the training cohort. The performance of the model was similar between the training and validation cohort, with an area under the receiver operating characteristic curve of 0.93 (95% confidence intervals [CI], 0.88-0.98) and 0.88 (95% CI, 0.80-0.96) (p=0.07), respectively. The calibration curve showed a good agreement between the predicted and actual probabilities.

Conclusion

A predictive model for carotid artery invasion can be defined based on features that come from patient characteristics and CT data to help in improve surgical planning and invasion evaluation.

Nomogram for the prediction of triple-negative breast cancer histological heterogeneity based on multiparameter MRI features: A preliminary study including metaplastic carcinoma and non- metaplastic carcinoma

Objectives

Triple-negative breast cancer (TNBC) is a heterogeneous disease, and different histological subtypes of TNBC have different clinicopathological features and prognoses. Therefore, this study aimed to establish a nomogram model to predict the histological heterogeneity of TNBC: including Metaplastic Carcinoma (MC) and Non-Metaplastic Carcinoma (NMC).

Methods

We evaluated 117 patients who had pathologically confirmed TNBC between November 2016 and December 2020 and collected preoperative multiparameter MRI and clinicopathological data. The patients were randomly assigned to a training set and a validation set at a ratio of 3:1. Based on logistic regression analysis, we established a nomogram model to predict the histopathological subtype of TNBC. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve. According to the follow-up information, disease-free survival (DFS) survival curve was estimated using the Kaplan-Meier product-limit method.

Results

Of the 117 TNBC patients, 29 patients had TNBC-MC (age range, 29–65 years; median age, 48.0 years), and 88 had TNBC-NMC (age range, 28–88 years; median age, 44.5 years). Multivariate logistic regression analysis demonstrated that lesion type (p = 0.001) and internal enhancement pattern (p = 0.001) were significantly predictive of TNBC subtypes in the training set. The nomogram incorporating these variables showed excellent discrimination power with an AUC of 0.849 (95% CI: 0.750−0.949) in the training set and 0.819 (95% CI: 0.693−0.946) in the validation set. Up to the cutoff date for this analysis, a total of 66 patients were enrolled in the prognostic analysis. Six of 14 TNBC-MC patients experienced recurrence, while 7 of 52 TNBC-NMC patients experienced recurrence. The DFS of the two subtypes was significantly different (p=0.035).

Conclusions

In conclusion, we developed a nomogram consisting of lesion type and internal enhancement pattern, which showed good discrimination ability in predicting TNBC-MC and TNBC-NMC.

A Novel Combined Nomogram Model for Predicting the Pathological Complete Response to Neoadjuvant Chemotherapy in Invasive Breast Carcinoma of No Specific Type: Real-World Study

Objective

To explore the value of a predictive model combining the multiparametric magnetic resonance imaging (mpMRI) radiomics score (RAD-score), clinicopathologic features, and morphologic features for the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in invasive breast carcinoma of no specific type (IBC-NST).

Methods

We enrolled, retrospectively and consecutively, 206 women with IBC-NST who underwent surgery after NAC and obtained pathological results from August 2018 to October 2021. Four RAD-scores were constructed for predicting the pCR based on fat-suppression T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), contrast-enhanced T1-weighted imaging (T1WI+C) and their combination, which was called mpMRI. The best RAD-score was combined with clinicopathologic and morphologic features to establish a nomogram model through binary logistic regression. The predictive performance of the nomogram was evaluated using the area under receiver operator characteristic (ROC) curve (AUC) and calibration curve. The clinical net benefit of the model was evaluated using decision curve analysis (DCA).

Results

The mpMRI RAD-score had the highest diagnostic performance, with AUC of 0.848 among the four RAD-scores. T stage, human epidermal growth factor receptor-2 (HER2) status, RAD-score, and roundness were independent factors for predicting the pCR (P < 0.05 for all). The combined nomogram model based on these factors achieved AUCs of 0.930 and 0.895 in the training cohort and validation cohort, respectively, higher than other models (P < 0.05 for all). The calibration curve showed that the predicted probabilities of the nomogram were in good agreement with the actual probabilities, and DCA indicated that it provided more net benefit than the treat-none or treat-all scheme by decision curve analysis in both training and validation datasets.

Conclusion

The combined nomogram model based on the mpMRI RAD-score combined with clinicopathologic and morphologic features may improve the predictive performance for the pCR of NAC in patients with IBC-NST.

Development and Validation of Nomograms to Predict Cancer-Specific Survival and Overall Survival in Elderly Patients With Prostate Cancer: A Population-Based Study

Objective

Prostate cancer (PC) is the most common non-cutaneous malignancy in men worldwide. Accurate predicting the survival of elderly PC patients can help reduce mortality in patients. We aimed to construct nomograms to predict cancer-specific survival (CSS) and overall survival (OS) in elderly PC patients.

Methods

Information on PC patients aged 65 years and older was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to determine independent risk factors for PC patients. Nomograms were developed to predict the CSS and OS of elderly PC patients based on a multivariate Cox regression model. The accuracy and discrimination of the prediction model were tested by the consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve. Decision curve analysis (DCA) was used to test the clinical value of the nomograms compared with the TNM staging system and D’Amico risk stratification system.

Results

135183 elderly PC patients in 2010-2018 were included. All patients were randomly assigned to the training set (N=94764) and the validation set (N=40419). Univariate and multivariate Cox regression model analysis revealed that age, race, marriage, histological grade, TNM stage, surgery, chemotherapy, radiotherapy, biopsy Gleason score (GS), and prostate-specific antigen (PSA) were independent risk factors for predicting CSS and OS in elderly patients with PC. The C-index of the training set and the validation set for predicting CSS was 0.883(95%CI:0.877-0.889) and 0.887(95%CI:0.877-0.897), respectively. The C-index of the training set and the validation set for predicting OS was 0.77(95%CI:0.766-0.774)and 0.767(95%CI:0.759-0.775), respectively. It showed that the proposed model has excellent discriminative ability. The AUC and the calibration curves also showed good accuracy and discriminability. The DCA showed that the nomograms for CSS and OS have good clinical potential value.

Conclusions

We developed new nomograms to predict CSS and OS in elderly PC patients. The models have been internally validated with good accuracy and reliability and can help doctors and patients to make better clinical decisions.

Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using radiomics of pretreatment dynamic contrast-enhanced MRI

PURPOSE

To investigate if the pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based radiomics machine learning predicts the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients.

METHODS

Seventy-eight breast cancer patients who underwent DCE-MRI before NAC and confirmed as pCR or non-pCR were enrolled. Early enhancement mapping images of pretreatment DCE-MRI were created using subtraction formula as follows: Early enhancement mapping = (Signal _{1 min} - Signal _pre)/Signal _pre. Images of the whole tumors were manually segmented and radiomics features extracted. Five prediction models were built using five scenarios that included clinical information, subjective radiological findings, first order texture features, second order texture features, and their combinations. In texture analysis workflow, the corresponding variables were identified by mutual information for feature selection and random forest was used for model prediction. In five models, the area under the receiver operating characteristic curves (AUC) to predict the pCR and several metrics for model evaluation were analyzed.

RESULTS

The best diagnostic performance based on F-score was achieved when both first and second order texture features with clinical information and subjective radiological findings were used (AUC = 0.77). The second best diagnostic performance was achieved with an AUC of 0.76 for first order texture features followed by an AUC of 0.76 for first and second order texture features.

CONCLUSIONS

Pretreatment DCE-MRI can improve the prediction of pCR in breast cancer patients when all texture features with clinical information and subjective radiological findings are input to build the prediction model.

Microcalcifications and Peritumoral Edema Predict Survival Outcome in Luminal Breast Cancer Treated with Neoadjuvant Chemotherapy

Background Little is known regarding findings at imaging associated with survival in patients with luminal breast cancer treated with neoadjuvant chemotherapy (NAC). Purpose To determine the relationship between imaging (MRI, US, and mammography) and clinical-pathologic variables in predicting distant metastasis-free survival (DMFS) and overall survival (OS) in patients with luminal breast cancer treated with NAC. Materials and Methods In this retrospective study, consecutive women with luminal breast cancer who underwent NAC followed by surgery were identified from the breast cancer registries of two hospitals. Women from one hospital between January 2003 and July 2015 were classified into the development cohort, and women from the other hospital between January 2007 and July 2015 were classified into the validation cohort. MRI scans, US scans, and mammograms before and after NAC (hereafter, referred to as pre- and post-NAC, respectively) and clinical-pathologic data were reviewed. Peritumoral edema was defined as the water-like high signal intensity surrounding the tumor on T2-weighted MRI scans. The prediction model was developed in the development cohort by using Cox regression and then tested in the validation cohort. Results The development cohort consisted of 318 women (68 distant metastases, 54 deaths) and the validation cohort consisted of 165 women (37 distant metastases, 14 deaths) (median age, 46 years in both cohorts). Post-NAC MRI peritumoral edema, age younger than 40 years, clinical N2 or N3, and lymphovascular invasion were associated with worse DMFS (all, P < .05). Pre-NAC mammographic microcalcifications, post-NAC MRI peritumoral edema, age older than 60 years, and clinical T3 or T4 were associated with worse OS (all, P < .05). The prediction model showed good discrimination ability (C index, 0.67-0.75 for DMFS and 0.70-0.77 for OS) and stratified prognosis into low-risk and high-risk groups (10-year DMFS rates, 79% vs 21%, respectively; and 10-year OS rates, 95%-96% vs 63%-67%, respectively) in the validation cohort. Conclusion MRI features and clinical-pathologic variables were identified that were associated with prolonged survival of patients with luminal breast cancer treated with neoadjuvant chemotherapy. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kataoka in this issue.

Development and validation of a nomogram based on pretreatment dynamic contrast-enhanced MRI for the prediction of pathologic response after neoadjuvant chemotherapy for triple-negative breast cancer

OBJECTIVES

To develop a nomogram based on pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with triple-negative breast cancer (TNBC).

METHODS

A total of 108 female patients with TNBC treated with neoadjuvant chemotherapy followed by surgery between January 2017 and October 2020 were enrolled. The patients were randomly divided into the primary cohort (n = 87) and validation cohort (n = 21) at a ratio of 4:1. The pretreatment DCE-MRI and clinicopathological features were reviewed and recorded. Univariate analysis and multivariate logistic regression analyses were used to determine the independent predictors of pCR in the primary cohort. A nomogram was developed based on the predictors, and the predictive performance of the nomogram was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The validation cohort was used to test the predictive model.

RESULTS

Tumor volume measured on DCE-MRI, time to peak (TTP), and androgen receptor (AR) status were identified as independent predictors of pCR. The AUCs of the nomogram were 0.84 (95% CI: 0.75-0.93) and 0.79 (95% CI: 0.59-0.99) in the primary cohort and validation cohort, respectively.

CONCLUSIONS

Pretreatment DCE-MRI could predict pCR after NAC in patients with TNBC. The nomogram can be used to predict the probability of pCR and may help individualize treatment.

KEY POINTS

• Pretreatment DCE-MRI findings can predict pathologic complete response (pCR) after neoadjuvant chemotherapy in patients with triple-negative breast cancer. • A nomogram based on the independent predictors of tumor volume measured on DCE-MRI, time to peak, and androgen receptor status could help personalized cancer treatment in TNBC patients.

An integrated deep learning model for the prediction of pathological complete response to neoadjuvant chemotherapy with serial ultrasonography in breast cancer patients: a multicentre, retrospective study

BACKGROUND

The biological phenotype of tumours evolves during neoadjuvant chemotherapy (NAC). Accurate prediction of pathological complete response (pCR) to NAC in the early-stage or posttreatment can optimize treatment strategies or improve the breast-conserving rate. This study aimed to develop and validate an autosegmentation-based serial ultrasonography assessment system (SUAS) that incorporated serial ultrasonographic features throughout the NAC of breast cancer to predict pCR.

METHODS

A total of 801 patients with biopsy-proven breast cancer were retrospectively enrolled from three institutions and were split into a training cohort (242 patients), an internal validation cohort (197 patients), and two external test cohorts (212 and 150 patients). Three imaging signatures were constructed from the serial ultrasonographic features before (pretreatment signature), during the first-second cycle of (early-stage treatment signature), and after (posttreatment signature) NAC based on autosegmentation by U-net. The SUAS was constructed by subsequently integrating the pre, early-stage, and posttreatment signatures, and the incremental performance was analysed.

RESULTS

The SUAS yielded a favourable performance in predicting pCR, with areas under the receiver operating characteristic curve (AUCs) of 0.927 [95% confidence interval (CI) 0.891-0.963] and 0.914 (95% CI 0.853-0.976), compared with those of the clinicopathological prediction model [0.734 (95% CI 0.665-0.804) and 0.610 (95% CI 0.504-0.716)], and radiologist interpretation [0.632 (95% CI 0.570-0.693) and 0.724 (95% CI 0.644-0.804)] in the external test cohorts. Furthermore, similar results were also observed in the early-stage treatment of NAC [AUC 0.874 (0.793-0.955)-0.897 (0.851-0.943) in the external test cohorts].

CONCLUSIONS

We demonstrate that autosegmentation-based SAUS integrating serial ultrasonographic features throughout NAC can predict pCR with favourable performance, which can facilitate individualized treatment strategies.

A Practical Predictive Model Based on Ultrasound Imaging and Clinical Indices for Estimation of Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer

Purpose

Clinical responses of neoadjuvant chemotherapy (NACT) are associated with prognosis in patients with breast cancer. The selection of suitable variables for the prediction of clinical responses remains controversial. Herein, we developed a predictive model based on ultrasound imaging and clinical indices to identify patients most likely to benefit from NACT.

Patients and Methods

We recruited a total of 225 consecutive patients who underwent NACT followed by surgery and axillary lymph node dissection at the Sixth Hospital of Ning Bo City of Zhe Jiang Province between January 1, 2018, and March 31, 2021. All patients had been diagnosed with breast cancer following the clinical examination. First, we created a training cohort of patients who underwent NACT+surgery (N=180) to develop a nomogram. We then validated the performance of the nomogram in a validation cohort of patients who underwent NACT+ surgery (N=45). Multivariate logistic regression was then used to identify independent risk factors that were associated with the response to NACT; these were then incorporated into the nomogram.

Results

Multivariate logistic regression analysis identified several significant differences as to clinical responses of NACT, including neutrophil–lymphocyte ratio (NLR), body mass index (BMI), pulsatility index (PI), resistance index (RI), blood flow, Ki67, histological type, molecular subtyping, and tumor size. The performance of the nomogram score exhibited a robust C-index of 0.89 (95% confidence interval [CI]: 0.83 to 0.95) in the training cohort and a high C-index of 0.87 (95% CI: 0.81 to 0.93) in the validation cohort. Clinical impact curves showed that the nomogram had a good predictive ability.

Conclusion

We successfully established an accurate and optimized nomogram incorporated ultrasound imaging and clinical indices that could be used preoperatively to predict clinical responses of NACT. This model can be used to evaluate the risk of clinical responses to NACT and therefore facilitate the choice of personalized therapy.

Background Parenchymal Enhancement on Breast MRI: Assessment and Clinical Implications

Purpose of Review

To present recent literature regarding the assessment and clinical implications of background parenchymal enhancement on breast MRI.

Recent Findings

The qualitative assessment of BPE remains variable within the literature, as well as in clinical practice. Several different quantitative approaches have been investigated in recent years, most commonly region of interest-based and segmentation-based assessments. However, quantitative assessment has not become standard in clinical practice to date. Numerous studies have demonstrated a clear association between higher BPE and future breast cancer risk. While higher BPE does not appear to significantly impact cancer detection, it may result in a higher abnormal interpretation rate. BPE is also likely a marker of pathologic complete response after neoadjuvant chemotherapy, with decreases in BPE during and after neoadjuvant chemotherapy correlated with pCR. In contrast, pre-treatment BPE does not appear to be predictive of pCR. The association between BPE and prognosis is less clear, with heterogeneous results in the literature.

Summary

Assessment of BPE continues to evolve, with heterogeneity in approaches to both qualitative and quantitative assessment. The level of BPE has important clinical implications, with associations with future breast cancer risk and treatment response. BPE may also be an imaging marker of prognosis, but future research is needed on this topic.

Construction of a risk prediction model for Alzheimer's disease in the elderly population

BACKGROUND

Dementia is one of the greatest global health and social care challenges of the twenty-first century. The etiology and pathogenesis of Alzheimer's disease (AD) as the most common type of dementia remain unknown. In this study, a simple nomogram was drawn to predict the risk of AD in the elderly population.

METHODS

Nine variables affecting the risk of AD were obtained from 1099 elderly people through clinical data and questionnaires. Least Absolute Shrinkage Selection Operator (LASSO) regression analysis was used to select the best predictor variables, and multivariate logistic regression analysis was used to construct the prediction model. In this study, a graphic tool including 9 predictor variables (nomogram-see precise definition in the text) was drawn to predict the risk of AD in the elderly population. In addition, calibration diagram, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to verify the model.

RESULTS

Six predictors namely sex, age, economic status, health status, lifestyle and genetic risk were identified by LASSO regression analysis of nine variables (body mass index, marital status and education level were excluded). The area under the ROC curve in the training set was 0.822, while that in the validation set was 0.801, suggesting that the model built with these 6 predictors showed moderate predictive ability. The DCA curve indicated that a nomogram could be applied clinically if the risk threshold was between 30 and 40% (30 to 42% in the validation set).

CONCLUSION

The inclusion of sex, age, economic status, health status, lifestyle and genetic risk into the risk prediction nomogram could improve the ability of the prediction model to predict AD risk in the elderly patients.