The influences of peritumoral lymphatic invasion and vascular invasion on the survival and recurrence according to the molecular subtypes of breast cancer

A LASSO Cox Regression Predictive Model for Patients Undergoing Surgery for Pancreatic Body and Tail Adenocarcinoma Patients: Comparative Long-Term Survival Analysis of Radical Antegrade Modular Pancreatosplenectomy (RAMPS) and Standard Retrograde Pancreatosplenectomy (SPRS)

PURPOSE

This study was designed to assess the advantages of radical antegrade modular pancreatosplenectomy (RAMPS) over standard retrograde pancreatosplenectomy (SPRS) in terms of disease-free survival (DFS) by comparing clinical outcomes.

METHODS

Clinical data from 154 patients who underwent distal pancreatectomy at Tianjin Medical University Cancer Institute and Hospital between January 2015 and August 2018 were collected. We compared the preoperative conditions, postoperative complications, and survival outcomes of patients who underwent two different surgical procedures. By creating a LASSO-Cox model, we determined the parameters affecting DFS and the risk ratios of the two surgical procedures on DFS.

RESULTS

The R0 resection rate (85.23% vs. 68.18%, P = 0.003), negative posterior margin rate (96.59% vs. 75.76%, P < 0.001), and tumor bed recurrence rate (15.29% vs. 40.00%, P = 0.001) significantly differed between the RAMPS and SPRS groups. The 1-, 3-, and 5-year survival and DFS rates of the RAMPS group were significantly better than those of the SPRS group (P < 0.05). Disease-free survival analysis based on Kaplan-Meier curves revealed that RAMPS was superior to SPRS (P < 0.001).

CONCLUSIONS

We recommend RAMPS as the preferred procedure for treating ductal adenocarcinoma of the pancreatic body and tail due to its enhanced lymph node repair capacity and visualization of posterior pancreatic sections, which can increase DFS in patients.

Simultaneous integrated boost intensity-modulated radiotherapy post breast-conserving surgery: clinical efficacy, adverse effects, and cosmetic outcomes in breast cancer patients

BACKGROUND

Simultaneous integrated boost intensity-modulated radiotherapy (SIB-IMRT) is an innovative technique delivering a higher dose to the tumor bed while irradiating the entire breast. This study aims to assess the clinical outcomes, adverse effects, and cosmetic results of SIB-IMRT following breast-conserving surgery in breast cancer patients.

METHODS

We conducted a retrospective analysis of 308 patients with stage 0-III breast cancer who underwent breast-conserving surgery and SIB-IMRT from January 2016 to December 2020. The prescribed doses included 1.85 Gy/27 fractions to the whole breast and 2.22 Gy/27 fractions or 2.20 Gy/27 fractions to the tumor bed. Primary endpoints included overall survival (OS), local-regional control (LRC), distant metastasis-free survival (DMFS), acute and late toxicities, and cosmetic outcomes.

RESULTS

The median follow-up time was 36 months. The 3-year OS, LRC, and DMFS rates were 100%, 99.6%, and 99.2%, respectively. Five patients (1.8%) experienced local recurrence or distant metastasis, and one patient succumbed to distant metastasis. The most common acute toxicity was grade 1-2 skin reactions (91.6%). The most common late toxicity was grade 0-1 skin and subcutaneous tissue reactions (96.7%). Five patients (1.8%) developed grade 1-2 upper limb lymphedema, and three patients (1.1%) had grade 1 radiation pneumonitis. Among the 262 patients evaluated for cosmetic outcomes at least 2 years post-radiotherapy, 96.9% achieved excellent or good results, while 3.1% had fair or poor outcomes.

CONCLUSIONS

SIB-IMRT after breast-conserving surgery in breast cancer patients demonstrated excellent clinical efficacy, mild acute and late toxicities, and satisfactory cosmetic outcomes in our study. SIB-IMRT appears to be a feasible and effective option for breast cancer patients suitable for breast-conserving surgery.

Negative Survival Impact of Occult Lymph Node Involvement in Small HER2-Positive Early Breast Cancer Treated by Up-Front Surgery

(1) Background: The independent negative prognostic value of isolated tumor cells or micro-metastases in axillary lymph nodes has been established in triple-negative breast cancers (BC). However, the prognostic significance of pN0(i+) or pN1mi in HER2-positive BCs treated by primary surgery remains unexplored. Therefore, our objective was to investigate the impact of pN0(i+) or pN1mi in HER2-positive BC patients undergoing up-front surgery on their outcomes. (2) Methods: We retrospectively analyzed 23,650 patients treated in 13 French cancer centers from 1991 to 2013. pN status was categorized as pN0, pN0(i+), pN1mi, and pNmacro. The effect of pN0(i+) or pN1mi on outcomes was investigated both in the entire cohort of patients and in pT1a-b tumors. (3) Results: Of 1771 HER2-positive BC patients included, pN status distributed as follows: 1047 pN0 (59.1%), 60 pN0(i+) (3.4%), 118 pN1mi (6.7%), and 546 pN1 macro-metastases (30.8%). pN status was significantly associated with sentinel lymph node biopsy, axillary lymph node dissection, age, ER status, tumor grade, and size, lymphovascular invasion, adjuvant systemic therapy (ACt), and radiation therapy. With 61 months median follow-up (mean 63.2; CI 95% 61.5-64.9), only pN1 with macro-metastases was independently associated with a negative impact on overall, disease-free, recurrence-free, and metastasis-free survivals in multivariate analysis. In the pT1a-b subgroup including 474 patients, RFS was significantly decreased in multivariate analysis for pT1b BC without ACt (HR 2.365, 1.04-5.36, p = 0.039) and for pN0(i+)/pN1mi patients (HR 2.518, 1.03-6.14, p = 0.042). (4) Conclusions: Survival outcomes were not adversely affected by pN0(i+) and pN1mi in patients with HER2-positive BC. However, in the case of pT1a-b HER2-positive BC, a negative impact on RFS was observed specifically for patients with pN0(i+) and pN1mi diseases, particularly among those with pT1b tumors without ACt. Our findings highlight the importance of considering the pN0(i+) and pN1mi status in the decision-making process when discussing trastuzumab-based ACt for these patients.

Survival and clinicopathological significance of blood vessel invasion in operable breast cancer: a systematic review and meta-analysis

BACKGROUND

Lymphovascular invasion, including lymphatic-vessel invasion and blood-vessel invasion, plays an important role in distant metastases. The metastatic pattern of blood-vessel invasion may differ from that of lymphatic-vessel invasion. However, its prognostic significance in breast cancer remains controversial. We evaluated the role of blood-vessel invasion in the prognosis of operable breast-cancer patients and its association with clinicopathological characteristics.

METHODS

We systematically searched EMBASE, PubMed, the Cochrane Library and Web of Science for studies in English through December 2020. Disease-free survival, overall survival and cancer-specific survival were the primary outcomes. Pooled hazard ratios and 95% confidence intervals were assessed using a random-effects model.

RESULTS

Twenty-seven studies involving 7954 patients were included. Blood-vessel invasion occurred in 20.4% of tumor samples. Pooled results showed significant associations of blood-vessel invasion with worse disease-free survival (hazard ratio = 1.82; 95% confidence interval = 1.43-2.31) and overall survival (hazard ratio = 1.86; 95% confidence interval = 1.16-2.99) in multivariate analyses. The results of the univariate analyses were similar. Among the clinicopathological factors, blood-vessel invasion was associated with larger tumor size, lymph-node metastasis, nonspecific invasive type, higher histological grade, estrogen receptor-negative breast cancer, human epidermal growth factor receptor 2-positive breast cancer and lymphatic-vessel invasion. In the lymph-node-negative subgroup analyses, the presence of blood-vessel invasion led to poorer disease-free survival (hazard ratio = 2.46; 95%confidence interval = 1.64-3.70) and overall survival (hazard ratio = 2.94; 95%confidence interval = 1.80-4.80).

CONCLUSIONS

We concluded that blood-vessel invasion is an independent predictor of poor prognosis in operable breast cancer and is associated with aggressive clinicopathological features. Breast-cancer patients with blood-vessel invasion require more aggressive treatments after surgery.

Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging to predict lymphovascular invasion and survival outcome in breast cancer

BACKGROUND

Lymphovascular invasion (LVI) predicts a poor outcome of breast cancer (BC), but LVI can only be postoperatively diagnosed by histopathology. We aimed to determine whether quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can preoperatively predict LVI and clinical outcome of BC patients.

METHODS

A total of 189 consecutive BC patients who underwent multiparametric MRI scans were retrospectively evaluated. Quantitative (K^trans, V_e, K_ep) and semiquantitative DCE-MRI parameters (W_- in, W_- out, TTP), and clinicopathological features were compared between LVI-positive and LVI-negative groups. All variables were calculated by using univariate logistic regression analysis to determine the predictors for LVI. Multivariate logistic regression was used to build a combined-predicted model for LVI-positive status. Receiver operating characteristic (ROC) curves evaluated the diagnostic efficiency of the model and Kaplan-Meier curves showed the relationships with the clinical outcomes. Multivariate analyses with a Cox proportional hazard model were used to analyze the hazard ratio (HR) for recurrence-free survival (RFS) and overall survival (OS).

RESULTS

LVI-positive patients had a higher K_ep value than LVI-negative patients (0.92 ± 0.30 vs. 0.81 ± 0.23, P = 0.012). N2 stage [odds ratio (OR) = 3.75, P = 0.018], N3 stage (OR = 4.28, P = 0.044), and K_ep value (OR = 5.52, P = 0.016) were associated with LVI positivity. The combined-predicted LVI model that incorporated the N stage and K_ep yielded an accuracy of 0.735 and a specificity of 0.801. The median RFS was significantly different between the LVI-positive and LVI-negative groups (31.5 vs. 34.0 months, P = 0.010) and between the combined-predicted LVI-positive and LVI-negative groups (31.8 vs. 32.0 months, P = 0.007). The median OS was not significantly different between the LVI-positive and LVI-negative groups (41.5 vs. 44.0 months, P = 0.270) and between the combined-predicted LVI-positive and LVI-negative groups (42.8 vs. 43.5 months, P = 0.970). LVI status (HR = 2.40), N2 (HR = 3.35), and the combined-predicted LVI model (HR = 1.61) were independently associated with disease recurrence.

CONCLUSION

The quantitative parameter of K_ep could predict LVI. LVI status, N stage, and the combined-predicted LVI model were predictors of a poor RFS but not OS.

MRI-Based Radiomics for Preoperative Prediction of Lymphovascular Invasion in Patients With Invasive Breast Cancer

Objective

Preoperative identification of lymphovascular invasion (LVI) in patients with invasive breast cancer is challenging due to absence of reliable biomarkers or tools in clinical settings. We aimed to establish and validate multiparametric magnetic resonance imaging (MRI)-based radiomic models to predict the risk of lymphovascular invasion (LVI) in patients with invasive breast cancer.

Methods

This retrospective study included a total of 175 patients with confirmed invasive breast cancer who had known LVI status and preoperative MRI from two tertiary centers. The patients from center 1 was randomly divided into a training set (n=99) and a validation set (n = 26), while the patients from center 2 was used as a test set (n=50). A total of 1409 radiomic features were extracted from the T2-weighted imaging (T2WI), dynamic contrast-enhanced (DCE) imaging, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC), respectively. A three-step feature selection including SelectKBest, interclass correlation coefficients (ICC), and least absolute shrinkage and selection operator (LASSO) was performed to identify the features most associated with LVI. Subsequently, a Support Vector Machine (SVM) classifier was trained to develop single-layer radiomic models and fusion radiomic models. Model performance was evaluated and compared by the area under the curve (AUC), sensitivity, and specificity.

Results

Based on one feature of wavelet-HLH_gldm_GrayLevelVariance, the ADC radiomic model achieved an AUC of 0.87 (95% confidence interval [CI]: 0.80–0.94) in the training set, 0.87 (0.70-1.00) in the validation set, and 0.77 (95%CI: 0.64-0.86) in the test set. However, the combination of radiomic features derived from other MR sequences failed to yield incremental value.

Conclusions

ADC-based radiomic model demonstrated a favorable performance in predicting LVI prior to surgery in patients with invasive breast cancer. Such model holds the potential for improving clinical decision-making regarding treatment for breast cancer.

Lymphovascular invasion has a significant prognostic impact in patients with early breast cancer, results from a large, national, multicenter, retrospective cohort study

Background

We determined the prognostic impact of lymphovascular invasion (LVI) in a large, national, multicenter, retrospective cohort of patients with early breast cancer (BC) according to numerous factors.

Patients and methods

We collected data on 17 322 early BC patients treated in 13 French cancer centers from 1991 to 2013. Survival functions were calculated using the Kaplan–Meier method and multivariate survival analyses were carried out using the Cox proportional hazards regression model adjusted for significant variables associated with LVI or not. Two propensity score-based matching approaches were used to balance differences in known prognostic variables associated with LVI status and to assess the impact of adjuvant chemotherapy (AC) in LVI-positive luminal A-like patients.

Results

LVI was present in 24.3% (4205) of patients. LVI was significantly and independently associated with all clinical and pathological characteristics analyzed in the entire population and according to endocrine receptor (ER) status except for the time period in binary logistic regression. According to multivariate analyses including ER status, AC, grade, and tumor subtypes, the presence of LVI was significantly associated with a negative prognostic impact on overall (OS), disease-free (DFS), and metastasis-free survival (MFS) in all patients [hazard ratio (HR) = 1.345, HR = 1.312, and HR = 1.415, respectively; P < 0.0001], which was also observed in the propensity score-based analysis in addition to the association of AC with a significant increase in both OS and DFS in LVI-positive luminal A-like patients. LVI did not have a significant impact in either patients with ER-positive grade 3 tumors or those with AC-treated luminal A-like tumors.

Conclusion

The presence of LVI has an independent negative prognostic impact on OS, DFS, and MFS in early BC patients, except in ER-positive grade 3 tumors and in those with luminal A-like tumors treated with AC. Therefore, LVI may indicate the existence of a subset of luminal A-like patients who may still benefit from adjuvant therapy.

•

In a study of 17 322 early BC patients, LVI had a significant independent negative prognostic impact on survival.

•

LVI negatively impacted survival in almost every patient category and cancer subtype, with and without AC.

•

LVI did not have a negative survival impact in patients with ER+ grade 3 or with luminal A-like tumors with chemotherapy.

•

Results suggest a possible benefit of AC in LVI-positive luminal A-like patients.

Radiomics Nomogram Based on Radiomics Score from Multiregional Diffusion-Weighted MRI and Clinical Factors for Evaluating HER-2 2+ Status of Breast Cancer

This study aimed to establish and validate a radiomics nomogram using the radiomics score (rad-score) based on multiregional diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) features combined with clinical factors for evaluating HER-2 2+ status of breast cancer. A total of 223 patients were retrospectively included. Radiomic features were extracted from multiregional DWI and ADC images. Based on the intratumoral, peritumoral, and combined regions, three rad-scores were calculated using the logistic regression model. Independent parameters were selected among clinical factors and combined rad-score (com-rad-score) using multivariate logistic analysis and used to construct a radiomics nomogram. The performance of the nomogram was evaluated using calibration, discrimination, and clinical usefulness. The areas under the receiver operator characteristic curve (AUCs) of intratumoral and peritumoral rad-scores were 0.824/0.763 and 0.794/0.731 in the training and validation cohorts, respectively. Com-rad-score achieved the highest AUC (0.860/0.790) among three rad-scores. ER status and com-rad-score were selected to establish the nomogram, which yielded good discrimination (AUC: 0.883/0.848) and calibration. Decision curve analysis demonstrated the clinical value of the nomogram in the validation cohort. In conclusion, radiomics nomogram, including clinical factors and com-rad-score, showed favorable performance for evaluating HER-2 2+ status in breast cancer.

Establishment of a model for predicting sentinel lymph node metastasis in early breast cancer based on contrast-enhanced ultrasound and clinicopathological features

Background

Sentinel lymph node (SLN) biopsy (SLNB) is the standard procedure for axillary staging in clinically node-negative (cN0) breast cancer patients. However, the positive rate of SLNs among cN0 stage patients is 26-35%. The identification of appropriate candidates for SLNB is quite challenging. This study aimed to establish and verify a predictive model of SLN metastasis using contrast-enhanced ultrasound (CEUS) and other clinicopathological indicators.

Methods

The clinicopathological data of 224 patients who had undergone SLNB at the Affiliated Cancer Hospital of Zhengzhou University from June 2018 to July 2019 were analyzed retrospectively. The risk prediction model of SLN metastasis was established by logistic regression analysis. According to the β value of each variable in the model, a risk score system of SLN metastasis was established and verified using the internal population. The predictive model was prospectively applied to 73 patients from July 2019 to September 2019 to evaluate the clinical value of the model in patients with early breast cancer.

Results

Multivariate analysis confirmed that body mass index (BMI), SLN aspect ratio of CEUS mode, SLN aspect ratio of mammography, lympho-vascular invasion, and cytokeratin (CK)5/6 were independent risk factors for SLN metastasis. A scoring system was established according to the above risk factors, and a receiver operating characteristic (ROC) curve was drawn. After internal- and external verification, a corrected ROC curve was drawn, respectively. The ROC curve of the modeling group, internal verification group, and external verification group was 0.9075 (95% CI: 0.8616-0.9534), 0.8766 (95% CI: 0.8192-0.9341), and 0.8505 (95% CI: 0.7333-0.9676), respectively.

Conclusions

We constructed and verified a prediction model of SLN metastasis in early breast cancer. The model has a specific predictive value for preoperative evaluation of SLN status.

Prognostic Nutritional Index (PNI) in Patients With Breast Cancer Treated With Neoadjuvant Chemotherapy as a Useful Prognostic Indicator

Objective

Prognostic nutritional index (PNI), calculated as serum albumin (ALB) (g/L) + 5 × total lymphocyte count (10⁹/L), is initially used to evaluate nutritional status in patients undergoing surgery and may evaluate the therapeutic effects and predict the survival of various solid tumors. The present study aimed to evaluate the potential prognostic significance of PNI in breast cancer patients receiving neoadjuvant chemotherapy (NACT).

Methods

A total of 785 breast cancer patients treated with neoadjuvant chemotherapy were enrolled in this retrospective study. The optimal cutoff value of PNI by receiver operating characteristic curve stratified patients into a low-PNI group (<51) and a high PNI group (≥51). The associations between breast cancer and clinicopathological variables by PNI were determined by chi-square test or Fisher’s exact test. Kaplan–Meier plots and log-rank test were used to evaluate the clinical outcomes of disease-free survival (DFS) and overall survival (OS). The prognostic value of PNI was analyzed by univariate and multivariate Cox proportional hazards regression models. The toxicity of NACT was accessed by the National Cancer Institute Common Toxicity Criteria (NCI-CTC).

Results

The results indicated that PNI had prognostic significance by an optimal cutoff value of 51 on DFS and OS in univariate and multivariate Cox regression survival analyses. Breast cancer patients with a high PNI value had longer DFS and OS than those with a low PNI value [47.64 vs. 36.60 months, P < 0.0001, hazard ratio (HR) = 0.264, 95%CI = 0.160–0.435; 73.61 vs. 64.97 months, P < 0.0001, HR = 0.319, 95%CI = 0.207–0.491, respectively]. Furthermore, the results indicated that patients with high PNI had longer DFS and OS than those with low PNI in early stage and advanced breast cancer, especially in advanced breast cancer. The mean DFS and OS times for breast cancer patients with high PNI by the log-rank test were longer than in those with low PNI in different molecular subtypes. Moreover, the mean DFS and OS times in patients with high PNI by the log-rank test were longer than in those patients with low PNI without or with lymph vessel invasion. The common toxicities after neoadjuvant chemotherapy were hematologic and gastrointestinal reaction, and the PNI had no significance on the toxicities of all enrolled patients, except in anemia, leukopenia, and myelosuppression.

Conclusion

Pretreatment PNI with the advantages of being convenient, noninvasive, and reproducible was a useful prognostic indicator for breast cancer patients receiving neoadjuvant chemotherapy and is a promising biomarker for breast cancer on treatment strategy decisions.

Clinical Databases for Breast Cancer Research

Clinical database is a collection of clinical data related to patients, which can be used for analysis and research. Clinical data can be classified into several categories: patient-related, tumor-related, diagnostics-related, treatment-related, outcome-related, administration-related, and other clinical data. Clinical databases can be classified according to the data types of clinical databases, ranges of institutes, and accessibility to data. The numbers of papers and clinical trials are rapidly increasing. Recently, more than 9000 papers related to breast cancer have been published annually, and more than 7000 papers related to human breast cancer are published annually. The speed of increase is expected to be faster and faster in future. Now, almost 8000 clinical trials are registered world widely. Main research areas of breast cancer can be classified into followings; epidemiology, screening and prevention, diagnosis, treatment, and prognosis. Clinical databases that are available for breast cancer research are also introduced in this chapter. The analysis of big data is expected to be the mainstream of breast cancer research using clinical databases. As the technology of artificial intelligence (AI) is rapidly evolving, the technology of deep learning starts to be applied for breast cancer research. In near future, AI technology is predicted to penetrate deeply the field of breast cancer research.

[Survival rate of 10 years among women with breast cancer: a historic cohort from 2000-2014]

Breast cancer is a public health problem due to its high incidence, morbidity and mortality. The analysis of survival for this disease contributes to the description of behavior and prognostic factors. The scope of this article is to investigate survival for 10 years after diagnosis and the prognostic factors of women with breast cancer admitted to the Unified Health System Mastology Service in Joinville, State of Santa Catarina, between 2000 and 2014. A historical cohort study with data from the medical records and death certificates of 1,321 women, of whom 471 were considered eligible under the minimum follow-up criteria of 10 years (n = 288), and/or death before this period (n = 183). Survival analysis using the Kaplan-Meier model, the Log-Rank test and the Cox regression model was conducted. Overall survival at 10 years was 41% (CI 95%, 36.1%-45.0%). The risk of 10-year mortality stratified by tumor staging was higher among women with lymphatic invasion and staging II, and staging III, at 60 years or older. The findings suggest that the presence of lymphatic invasion, advanced age and intermediate/advanced staging of the disease can be considered indicators of a worse prognosis for breast cancer.

Why is pancreatic cancer so deadly? The pathologist's view

The remarkable aggressiveness of pancreatic cancer has never been fully explained. Although clearly multifactorial, we postulate that venous invasion, a finding seen in most pancreatic cancers but not in most cancers of other organs, may be a significant, underappreciated contributor to the aggressiveness of this disease. Copyright © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

A nomogram for individual prediction of vascular invasion in primary breast cancer

OBJECTIVES

To explore the feasibility of preoperative prediction of vascular invasion (VI) in breast cancer patients using nomogram based on multiparametric MRI and pathological reports.

METHODS

We retrospectively collected 200 patients with confirmed breast cancer between January 2016 and January 2018. All patients underwent MRI examinations before the surgery. VI was identified by postoperative pathology. The 200 patients were randomly divided into training (n = 100) and validation datasets (n = 100) at a ratio of 1:1. Least absolute shrinkage and selection operator (LASSO) regression was used to select predictors most associated with VI of breast cancer. A nomogram was constructed to calculate the area under the curve (AUC) of receiver operating characteristics, sensitivity, specificity, accuracy, positive prediction value (PPV) and negative prediction value (NPV). We bootstrapped the data for 2000 times without setting the random seed to obtain corrected results.

RESULTS

VI was observed in 79 patients (39.5%). LASSO selected 10 predictors associated with VI. In the training dataset, the AUC for nomogram was 0.94 (95% confidence interval [CI]: 0.89-0.99, the sensitivity was 78.9% (95%CI: 72.4%-89.1%), the specificity was 95.3% (95%CI: 89.1%-100.0%), the accuracy was 86.0% (95%CI: 82.0%-92.0%), the PPV was 95.7% (95%CI: 90.0%-100.0%), and the NPV was 77.4% (95%CI: 67.8%-87.0%). In the validation dataset, the AUC for nomogram was 0.89 (95%CI: 0.83-0.95), the sensitivity was 70.3% (95%CI: 60.7%-79.2%), the specificity was 88.9% (95%CI: 80.0%-97.1%), the accuracy was 77.0% (95%CI: 70.0%-83.0%), the PPV was 91.8% (95%CI: 85.3%-98.0%), and the NPV was 62.7% (95%CI: 51.7%-74.0%). The nomogram calibration curve shows good agreement between the predicted probability and the actual probability.

CONCLUSION

The proposed nomogram could be used to predict VI in breast cancer patients, which was helpful for clinical decision-making.