Nomogram predicting clinical outcomes in breast cancer patients treated with neoadjuvant chemotherapy

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.

Survival analysis and individualized prediction of survival benefit for pancreatic signet ring cell carcinoma: a population study based on the SEER database

OBJECTIVES

This study aimed to compare the incidence, clinicopathological characteristics and survival results of pancreatic signet ring cell carcinoma (PSRCC) and pancreatic adenocarcinomas (PDAC), as well as to analyze the clinical characteristics related to the overall survival (OS) of PSRCC, and to establish an effective prognostic nomogram to predict the risks associated with patient outcomes.

METHODS

A total of 85,288 eligible patients including 425 PSRCC and 84,863 PDAC cases were retrieved from the Surveillance, Epidemiology, and End Results database. The survival curve was calculated using the Kaplan-Meier method and differences in them were measured by Log-rank tests. The Cox proportional hazards regression model was used to identify independent predictors of OS in patients with PSRCC. A nomogram was constructed to predict 1-, 3-, and 5-year OS. The performance of the nomogram was measured by C-index, receiver operating characteristic (ROC) curve, decision curve analysis (DCA).

RESULTS

The incidence of PSRCC is much lower than that of PDAC (10.798 V.S. 0.349 per millions). PSRCC is an independent predictor of pancreatic cancer with a poorer histological grade, a higher rate of lymph node and distant metastasis, and a poorer prognosis. We identified four independent prognostic factors including grade, American Joint Committee on Cancer Tumor-Node-Metastasis (TNM) stage, surgery and chemotherapy based on the Cox regression model. The C-index and DCA curves showed better performance of the nomogram than TNM stage. ROC curve analysis also showed that the nomogram had good discrimination, with an area under the curve of 0.840, 0.896, and 0.923 for 1-, 3-, and 5-year survival. The calibration curves showed good agreement between the prediction by the nomogram and actual observations.

CONCLUSION

PSRCC is a rare but fatal subtype of pancreatic cancer. The constructed nomogram in this study accurately predicted the prognosis of PSRCC, performed better than the TNM stage.

Development and Assessment of a Novel Core Biopsy-Based Prediction Model for Pathological Complete Response to Neoadjuvant Chemotherapy in Women with Breast Cancer

Purpose: Pathological complete response (pCR), the goal of NAC, is considered a surrogate for favorable outcomes in breast cancer (BC) patients administrated neoadjuvant chemotherapy (NAC). This study aimed to develop and assess a novel nomogram model for predicting the probability of pCR based on the core biopsy. Methods: This was a retrospective study involving 920 BC patients administered NAC between January 2012 and December 2018. The patients were divided into a primary cohort (769 patients from January 2012 to December 2017) and a validation cohort (151 patients from January 2017 to December 2018). After converting continuous variables to categorical variables, variables entering the model were sequentially identified via univariate analysis, a multicollinearity test, and binary logistic regression analysis, and then, a nomogram model was developed. The performance of the model was assessed concerning its discrimination, accuracy, and clinical utility. Results: The optimal predictive threshold for estrogen receptor (ER), Ki67, and p53 were 22.5%, 32.5%, and 37.5%, respectively (all p < 0.001). Five variables were selected to develop the model: clinical T staging (cT), clinical nodal (cN) status, ER status, Ki67 status, and p53 status (all p ≤ 0.001). The nomogram showed good discrimination with the area under the curve (AUC) of 0.804 and 0.774 for the primary and validation cohorts, respectively, and good calibration. Decision curve analysis (DCA) showed that the model had practical clinical value. Conclusions: This study constructed a novel nomogram model based on cT, cN, ER status, Ki67 status, and p53 status, which could be applied to personalize the prediction of pCR in BC patients treated with NAC.

Establishment and validation of an ultrasound-based nomogram with risk stratification for short disease-free survival in breast cancer

PURPOSE

This retrospective study aimed to develop and validate an Ultrasound (US)-based nomogram to predict short disease-free survival (short-DFS, less than 120 months DFS) in breast cancer (BC).

METHODS

Nomogram was established based on a training data of 311 BC patients by multivariable logistic regression, and were assessed by discrimination, calibration, and clinical usefulness. Risk stratification was performed by X-tile. An independent testing data of 200 patients with BC was used for external validation.

RESULTS

Nine predictors including three US features and six clinical parameters were screened into the nomogram by Lasso (log λ = -3.594) in training data. Better performance was obtained in the training data (C-index: 0.942) and testing data (C-index: 0.914). Calibration analysis indicated optimal agreement between nomogram predictions and actual observations (p = 0.67). Decision curve analysis showed a great clinical benefit (Youden index: 0.634). Three risk levels are low-risk (<184.0), moderate-risk (184.0-345.3) and high-risk (>345.3). Our nomograms had larger area under the receiver operating characteristic (ROC) curves compared with Magee Equation and Nottingham Prognostic models (0.942 vs. 0.824, 0.790).

CONCLUSION

The US-based nomogram and the practical score system facilitate individualized prediction of short-DFS to optimize clinical decisions and improve prognosis in patients with BC.

MEME KANSERİ HASTALARI İÇİN İZOTONİK ORANTILI TEHLİKE MODELİ

Amaç: Onkolojik çalışmalarda çok sayıda sağkalım analiz yöntemi kullanılmaktadır. Farklı veri yapıları için farklı modeller tercih edilir. İzotonik orantılı tehlike modeli bu yöntemlerden birisidir. Olayın gerçekleştiği zamana kadar geçen süre ile monotonik olarak artan ortak değişken arasındaki ilişkiyi değerlendirmek için izotonik modellerin kullanılması önerilmektedir. Bu çalışmanın amacı, izotonik modellerin teorik özelliklerini ve kullanım alanlarını açıklamak ve uygun bir veri seti üzerinde uygulamasını göstermektir. Yöntemler: İzotonik modellerin uygulanması için Seul Ulusal Üniversite Hastanesinde tedavi edilen meme kanseri hastalarına ait veriler kullanıldı. Tekrarlama riskinin modellenmesinde ER, PR, HER2 ve Ki-67 değişkenleri bağımsız risk faktörleri olarak kabul edildi. Tümör boyutu, modele ortak değişken olarak dahil edildi. Modellemede izotonik orantılı tehlike modeli kullanıldı. Model katsayılarının tahmini kısmi olabilirlik yöntemi ile yapıldı. Bulgular: Nüksetme üzerinde tümör boyutunun monoton artırıcı etkisi olduğu gözlendi. Ki-67 değeri arttıkça tekrarlama riskinin 1.008 kat arttığı belirlendi. ER ve PR negatifliğinin riski artırdığı, HER2 pozitifliğinin ise riski artırdığı gözlemlendi. Sonuç: Ortak değişkenin hastalık sürecini monoton artırıcı etkisinin olduğu durumlarda, ortak değişken ve olay arasındaki ilişkiler dikkate alınarak izotonik regresyon modeli kullanılabilir. Böylece, ortak değişken ile olay arasındaki monoton ilişkiyi hesaba katan fonksiyon, hayatta kalma analizine dahil edilecektir.

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.

Lymph Node Ratio after Neoadjuvant Chemotherapy for Stage II/III Breast Cancer: Prognostic Value Measured with Gini's Mean Difference of Restricted Mean Survival Times

Restricted mean survival time (RMST), recommended for reporting survival, lacks a tool to evaluate multilevel factors. The potential of the Gini’s mean difference of RMSTs (Δ) is explored in a comparison of a lymph node ratio-based classification (LNRc) versus a number-based classification (ypN) applied to stage II/III breast cancer patients who received neoadjuvant chemotherapy and underwent axillary dissection. Number of positive nodes (npos) classified patients into ypN0, npos = 0, ypN1, npos = [1,3], ypN2, npos = [4,9], and ypN3, npos ⩾ 10. Ratio npos/(number of nodes examined) of 0, (0,0.20], (0.20,0.65], and >0.65, classified patients into Lnr0 to Lnr3, respectively. Unadjusted and Cox-adjusted RMSTs were computed for the ypN and LNRc’s. At a follow-up time horizon of 72 months for 114 node-negative and 254 node-positive patients, unadjusted ypN0-ypN3 RMSTs were 62.4-41.4 months, Δ = 11.9 months (95%CI: 7.4-16.9), and Lnr0-Lnr3 62.4 to 36.3 months, Δ = 14.0 months (95%CI: 10.1-18.1). Cox models’ ypN1-ypN3 hazard ratios were 1.81-3.30, and Lnr1-Lnr3 1.52-4.39. Δ from Cox-fitted survival were ypN 8.1 months (95%CI: 5.9-10.5), LNRc 10.5 months (95%CI: 8.4-12.8). In conclusion, Gini’s mean difference is applicable to well established data in keeping with the literature on LNRc. It provides an alternative view on the improvement gained with a lymph node ratio-classification over using a number-classification.

Development, verification, and comparison of a risk stratification model integrating residual cancer burden to predict individual prognosis in early-stage breast cancer treated with neoadjuvant therapy

Background

A favorable model for predicting disease-free survival (DFS) and stratifying prognostic risk in breast cancer (BC) treated with neoadjuvant chemotherapy (NAC) is lacking. The aim of the current study was to formulate an excellent model specially for predicting prognosis in these patients.

Patients and methods

Between January 2012 and December 2015, 749 early-stage BC patients who received NAC in Xijing hospital were included. Patients were randomly assigned to a training cohort (n = 563) and an independent cohort (n = 186). A prognostic model was created and subsequently validated. Predictive performance and discrimination were further measured and compared with other models.

Results

Clinical American Joint Committee on Cancer stage, grade, estrogen receptor expression, human epidermal growth factor receptor 2 (HER2) status and treatment, Ki-67 expression, lymphovascular invasion, and residual cancer burden were identified as independent prognostic variables for BC treated with NAC. The C-index of the model consistently outperformed other available models as well as single independent factors with 0.78, 0.80, 0.75, 0.82, and 0.77 in the training cohort, independent cohort, luminal BC, HER2-positive BC, and triple-negative BC, respectively. With the optimal cut-off values (280 and 360) selected by X-tile, patients were categorized as low-risk (total points ≤280), moderate-risk (280 < total points ≤ 360), and high-risk (total points >360) groups presenting significantly different 5-year DFS of 89.9%, 56.9%, and 27.7%, respectively.

Conclusions

In patients with BC, the first model including residual cancer burden index was demonstrated to predict the survival of individuals with favorable performance and discrimination. Furthermore, the risk stratification generated by it could determine the risk level of recurrence in whole early-stage BC cohort and subtype-specific cohorts, help tailor personalized intensive treatment, and select comparable study cohort in clinical trials.

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Establishing the first risk stratification nomogram for BC treated with NAC and validate its performance in BC cohorts.

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Incorporating residual cancer burden index into predictive nomogram for the first time.

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Predictive model can be utilized to predict DFS for all early-stage BC treated with NAC.

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Performing a continuous rather than categorized model to predict individual survival.

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The risk stratification can be used to select comparable population in trial design.

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

Background There is an increasing need to develop a more accurate prediction model for pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer. Purpose To develop a nomogram based on MRI and clinical-pathologic variables to predict pCR. Materials and Methods In this single-center retrospective study, consecutive women with stage II-III breast cancer who underwent NAC followed by surgery between January 2011 and December 2017 were considered for inclusion. The women were divided into a development cohort between January 2011 and September 2015 and a validation cohort between October 2015 and December 2017. Clinical-pathologic data were collected, and mammograms and MRI scans obtained before and after NAC were analyzed. Logistic regression analyses were performed to identify independent variables associated with pCR in the development cohort from which the nomogram was created. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC) and calibration slope. Results A total of 359 women (mean age, 49 years ± 10 [standard deviation]) were in the development cohort and 351 (49 years ± 10) in the validation cohort. Hormone receptor negativity (odds ratio [OR], 3.1; 95% CI: 1.4, 7.1; P = .006), high Ki-67 index (OR, 1.05; 95% CI: 1.03, 1.07; P < .001), and post-NAC MRI variables, including small tumor size (OR, 0.6; 95% CI: 0.4, 0.9; P = .03), low lesion-to-background parenchymal signal enhancement ratio (OR, 0.2; 95% CI: 0.1, 0.6; P = .004), and absence of enhancement in the tumor bed (OR, 3.8; 95% CI: 1.4, 10.5; P = .009) were independently associated with pCR. The nomogram incorporating these variables showed good discrimination (AUC, 0.90; 95% CI: 0.86, 0.94) and calibration abilities (calibration slope, 0.91; 95% CI: 0.69, 1.13) in the independent validation cohort. Conclusion A nomogram incorporating hormone receptor status, Ki-67 index, and MRI variables showed good discrimination and calibration abilities in predicting pathologic complete response. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Imbriaco and Ponsiglione in this issue.

Development and Validation of a Nomogram for Individually Predicting Pathologic Complete Remission After Preoperative Chemotherapy in Chinese Breast Cancer: A Population-Based Study

PURPOSE

To explore the independent predictors of pathologic complete remission response (pCR) for Chinese patients with breast cancer (BC) after preoperative chemotherapy and to develop an individualized nomogram for predicting the probability of pCR.

PATIENTS AND METHODS

The clinicopathologic data of clinical stage I-III BC patients who received preoperative chemotherapy in Xijing Hospital were retrospectively analyzed. A total of 689 BC patients diagnosed in 2015-2017 were included in the training set to develop a nomogram. A separate cohort of 357 patients in the same center was regarded as a validation set for externally examining the performance of the model. The area under the receiver operating characteristic curve and calibration curve were used to verify the predictive performance of the nomogram.

RESULTS

Multivariate logistic regression analysis showed that independent predictors of pCR were menopause status at diagnosis, family history of BC, initial tumor size, estrogen receptor status, HER2/neu (human epidermal growth factor receptor 2) status, and Ki-67 expression. On the basis of these factors, a nomogram was developed using R software. Our nomogram had good discrimination in the training and validation set (area under the receiver operating characteristic curve, 0.762 and 0.768, respectively). The calibration curves further confirmed that the model performs well.

CONCLUSION

Menopause status and family history of BC were independent predictors of pCR after preoperative chemotherapy for the first time. The nomogram can accurately predict pCR rate in BC, which may provide some guidelines for breast surgery options and patient counseling.

Nomogram for prediction of pathologic complete remission using biomarker expression and endoscopic finding after preoperative chemoradiotherapy in rectal cancer

Objective

The aim of this study is to develop a nomogram for prediction of pathologic complete remission (pCR) after preoperative chemoradiotherapy (CRT) for rectal cancer.

Methods

mRNA expression levels of seven molecular markers [p53, p21, Ki-67, vascular endothelial growth factor (VEGF), CD133, CD24, CD44] were measured by reverse transcriptase polymerase chain reaction (RT-PCR) in 120 rectal cancers. Endoscopic findings of clinical complete remission (cCR) and biologic variables were used to construct nomogram in the training group (n=80), which was validated in the validation group (n=40).

Results

mRNA expression levels of four markers (p53, p21, Ki67, CD133) correlated with pCR (24/80, 30.0%) in the training group. Low expression of p53 and/or high expression of p21, Ki67 and CD133 showed greater pCR rate. pCR was shown in 18 (69.2%) of 26 cases showing endoscopic cCR in the training group. Higher pCR rate was demonstrated in lower tumor location than middle tumor (19/49, 38.8% vs. 5/31, 16.1%). A nomogram for prediction of pCR was developed from the multivariate prediction model using these six variables, which showed good discrimination ability in the training group [area under the curve (AUC)=0.945] and validation group (AUC=0.922). The calibration plot showed good agreement between actual and predicted pCR in both patient groups.

Conclusions

Nomogram for assessment of pCR can be useful for making treatment decisions after CRT according to predicted responses.

Nomograms to predict survival of stage IV tongue squamous cell carcinoma after surgery

To develop clinical nomograms for prediction of overall survival (OS) and cancer-specific survival (CSS) in patients with stage IV tongue squamous cell carcinoma (TSCC) after surgery based on the Surveillance, Epidemiology, and End Results (SEER) program database.We collected data of resected stage IV TSCC patients from the SEER database, and divided them into the training set and validation set by 7:3 randomly. Kaplan-Meier analysis and Cox regression analysis were adopted to distinguish independent risk factors for OS and CSS. Clinical nomograms were constructed to predict the 3-year and 5-year probabilities of OS and CSS for individual patients. Calibration curves and Harrell C-indices were used for internal and external validation.A total of 1550 patients with resected stage IV TSCC were identified. No statistical differences were detected between the training and validation sets. Age, race, marital status, tumor site, AJCC T/N/M status, and radiotherapy were recognized as independent prognostic factors associated with OS as well as CSS. Then nomograms were developed based on these variables. The calibration curves displayed a good agreement between the predicted and actual values of 3-year and 5-year probabilities for OS and CSS. The C-indices predicting OS were corrected as 0.705 in the training set, and 0.664 in the validation set. As for CSS, corrected C-indices were 0.708 in the training set and 0.663 in the validation set.The established nomograms in this study exhibited good accuracy and effectiveness to predict 3-year and 5-year probabilities of OS and CSS in resected stage IV TSCC patients. They are useful tools to evaluate survival outcomes and helped choose appropriate treatment strategies.

Use of clinical nomograms for predicting survival outcomes in young women with breast cancer

Early-onset breast cancer (BC) has been recognized to be more aggressive compared with its later counterparts. Survival models of BC in young patients have rarely been reported in previous studies. The current study aimed to establish and validate prediction models with clinicopathological variables for visceral metastasis-free survival (VFS), disease-free-survival (DFS) and overall survival (OS) time in young patients with BC. Clinicopathological data were obtained for 351 patients with primary breast tumors who were ≤40 years old. Univariate and multivariate analyses were performed and nomograms were established to screen and illustrate the prognostic factors. Risk scores were calculated based on coefficients from the Cox regression analysis. Internal validation of the prediction models was conducted by predicting the prognosis of cases randomly sampled from the cohort used in the current study. Multivariate analysis demonstrated that N stage (P=0.004), molecular subtype (P=0.007) and age (P=0.005) were significant independent prognostic factors for VFS. Similarly, N stage (P=0.002) and molecular subtype (P=0.001) were significantly associated with DFS. In addition, N stage (P=0.006), molecular subtype (P=0.006) and the presence of an initially inoperable tumor (P=0.005) were significant independent prognostic factors for OS. According to the Cox regression analysis, nomograms were generated to illustrate the effect of independent prognostic factors on VFS, DFS and OS. Risk scores were calculated and internal validation demonstrated the reliability of the prediction models. In conclusion, N stage and molecular subtype were identified as predictors for VFS, DFS and OS in early-onset BC. Furthermore, an age of <35 years at diagnosis was revealed to be unfavorable for VFS and the presence of an initially inoperable tumor was identified to reduce OS time.

Development and validation of a nomogram to individually predict survival of young patients with nonmetastatic gastric cancer: A retrospective cohort study

BACKGROUND/AIMS

Evidence regarding gastric cancer (GC) patients <40 years old is limited. The aim of the study was to identify risk factors affecting overall survival (OS) of young patients with nonmetastatic GC and to establish a nomogram for prognostic prediction using data from the Surveillance, Epidemiology and End Results (SEER) database. Furthermore, this study sought to externally validate this nomogram in an independent patient cohort.

PATIENTS AND METHODS

In this retrospective cohort study, the records of patients aged <40 years with nonmetastatic GC (n = 559), from the SEER database, between 2006 and 2015, were examined. The nomogram was established based on the Cox proportional hazards regression model using the SEER dataset. Patients with nonmetastatic GC (n = 201) in our department between 2009 and 2015 were selected as an external validation set. Discrimination and calibration were performed in both cohorts.

RESULTS

The multivariate Cox model identified race, tumor subsites, tumor size, depth of invasion, lymph node metastasis, number of examined lymph nodes, and surgery as independent covariates associated with OS. The nomogram exhibited superior discriminative power than the eighth tumor, node, metastasis (TNM) staging system in both the training set [Harrell's concordance index (C index): 0.762 vs. 0.635,P < 0.001] and validation set (C index: 0.805 vs. 0.712,P= 0.176). Calibration of the nomogram was good in both cohorts.

CONCLUSIONS

We developed a nomogram predicting 3- and 5-year OS rates in young patients with nonmetastatic GC. Both the training set and validation set showed good discrimination and calibration, suggesting good clinical applicability.

Establishment and external validation of a prognostic model for predicting disease-free survival and risk stratification in breast cancer patients treated with neoadjuvant chemotherapy

Background

The eighth edition of the American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system for survival prediction and risk stratification in breast cancer (BC) patients after neoadjuvant chemotherapy (NCT) is of limited efficacy. This study aimed to establish a novel prognostic nomogram for predicting disease-free survival (DFS) in BC patients after NCT.

Patients and methods

A total of 567 BC patients treated with NCT, from two independent centers, were included in this study. Cox proportional-hazards regression (CPHR) analysis was conducted to identify the independent prognostic factors for DFS, in order to develop a model. Subsequently, the discrimination and calibration ability of the prognostic model were assessed in terms of its concordance index (C-index), risk group stratification, and calibration curve. The performance of the nomogram was compared with that of the eighth edition of the AJCC TNM staging system via C-index.

Results

Based on the CPHR model, eight prognostic predictors were screened and entered into the nomogram. The prognostic model showed better performance (p<0.01) in terms of DFS prediction (C-index: 0.738; 95% CI: 0.698-0.779) than the eighth edition of the AJCC TNM staging system (C-index: 0.644; 95% CI: 0.604-0.684). Stratification into three risk groups highlighted significant differences between the survival curves in the training cohort and those in the validation cohort. The calibration curves for likelihood of 3- and 5-year DFS indicated optimal agreement between nomogram predictions and actual observations.

Conclusion

We constructed and externally validated a novel nomogram scoring system for individualized DFS estimation in BC patients treated with NCT. This user-friendly predictive tool may help oncologists to make optimal clinical decisions.

Nomogram based on systemic inflammatory response markers predicting the survival of patients with resectable gastric cancer after D2 gastrectomy

This study aimed to construct a nomogram to predict survival of patients with resectable gastric cancer (RGC) based on both clinicopathology characteristics and systemic inflammatory response markers (SIRMs). Of 3,452 RGC patients after D2 gastrectomy at the Sun Yat-sen University Cancer Center, 1058 patients who met the inclusion criterion were analyzed. The patients operated on from January 1, 2005 to December 31, 2009 were assigned to the training set (817 patients) to establish a nomogram, and the rest (241 patients) were selected as validation set. Based on the training set, seven independent risk factors were selected in the nomogram. The calibration curves for probability of 1-year, 3-year and 5-year overall survival (OS) showed satisfactory accordance between nomogram prediction and actual observation. When the metastatic lymph node stage (mLNS) is replaced by metastasis lymph node ratio (mLNR) in validation set, the C-index in predicting OS rise from 0.77 to 0.79, higher than that of 7th American Joint Committee on Cancer 7th (AJCC) staging system (0.70; p<0.001). In conclusions, the proposed nomogram which including mLNR and routine detected SIRMs resulted in optimal survival prediction for RGC patients after D2 gastrectomy.

Development and external validation of a prognostic nomogram for gastric cancer using the national cancer registry

A nomogram based on both western and eastern populations to estimate the Disease Specific Survival (DSS) of resectable gastric cancer (RGC) has not been established. In current study, we retrospectively analyzed 4,379 RGC patients who underwent curative resection from the Surveillance, Epidemiology, and End Results (SEER) database. Patients diagnosed between 1998 and 2009 were assigned as training set (n= 2,770), and the rest were selected as SEER validation set (n= 1,609). An external validation was performed by a set of independent 1,358 RGC patients after D2 resection from Sun Yat–sen University Cancer Center (SYSUCC) in China. The nomogram was constructed based on the training set. The multivariate analysis identified that patient's age at diagnosis, race, tumor location, grade, depth of invasion, metastatic lymph node stage (mLNS) and total number of examined lymph node (TLN) were associated with patient's DSS. The discrimination of this nomogram was superior to that of the 7th edition of AJCC staging system in SEER validation set and SYSUCC validation set (0.73 versus 0.70, p=0.005; 0.76 versus 0.72, p=0.005; respectively). Calibration plots of the nomogram showed that the probability of DSS corresponded to actual observation closely. In conclusion, our nomogram resulted in more–reliable prognostic prediction for RGC patients in general population.

[Predictive factors of conservative breast surgery after neoadjuvant chemotherapy for breast cancer]

OBJECTIVES

The aim of our study was to evaluate the existence of predictive factors of conservative breast surgery after neoadjuvant chemotherapy (NAC) for breast cancer.

METHODS

We included all women with invasive breast cancer who received NAC and underwent breast surgery between January 2007 and December 2013 in our institution. Univariable and multivariable analyses were performed to determine the association between clinical and histological factors and conservative breast surgery.

RESULTS

During the study period, 229 women were included of whom 73 had breast conservative surgery (32%). At univariable analysis, significant predictive factors were age (OR 0.97 [CI 95% 0.95-0.99], P=0.02), radiological size (OR 0.97 [CI 95% 0.96-0.99], P<0.001), multifocality (OR 0.53 [CI 95% 0.27-1.05], P=0.06), breast inflammation (OR 0.15 [CI 95% 0.07-0.32], P<0.001) and the type of hormone receptors (P=0.12). In multivariable analysis, all these factors but age were significant factors and thus considered as independent predictive factors.

CONCLUSION

This work permitted to identify independent predictive factors of breast conservative surgery after NAC for breast cancer that will be included in a risk scoring system that we aim to evaluate prospectively.

A nomogram for predicting pathological complete response in patients with human epidermal growth factor receptor 2 negative breast cancer

Background

The response to neoadjuvant chemotherapy has been proven to predict long-term clinical benefits for patients. Our research is to construct a nomogram to predict pathological complete response of human epidermal growth factor receptor 2 negative breast cancer patients.

Methods

We enrolled 815 patients who received neoadjuvant chemotherapy from 2003 to 2015 and divided them into a training set and a validation set. Univariate logistic regression was performed to screen for predictors and construct the nomogram; multivariate logistic regression was performed to identify independent predictors.

Results

After performing the univariate logistic regression analysis in the training set, tumor size, hormone receptor status, regimens of neoadjuvant chemotherapy and cycles of neoadjuvant chemotherapy were the final predictors for the construction of the nomogram. The multivariate logistic regression analysis demonstrated that T4 status, hormone receptor status and receiving regimen of paclitaxel and carboplatin were independent predictors of pathological complete response. The area under the receiver operating characteristic curve of the training set and the validation set was 0.779 and 0.701, respectively.

Conclusions

We constructed and validated a nomogram to predict pathological complete response in human epidermal growth factor receptor 2 negative breast cancer patients. We also identified tumor size, hormone receptor status and paclitaxel and carboplatin regimen as independent predictors of pathological complete response.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2652-z) contains supplementary material, which is available to authorized users.

New Perspectives on Predictive Biomarkers of Tumor Response and Their Clinical Application in Preoperative Chemoradiation Therapy for Rectal Cancer

Preoperative chemoradiation therapy (CRT) is the standard treatment for patients with locally advanced rectal cancer (LARC) and can improve local control and survival outcomes. However, the responses of individual tumors to CRT are not uniform and vary widely, from complete response to disease progression. Patients with resistant tumors can be exposed to irradiation and chemotherapy that are both expensive and at times toxic without benefit. In contrast, about 60% of tumors show tumor regression and T and N down-staging. Furthermore, a pathologic complete response (pCR), which is characterized by sterilization of all tumor cells, leads to an excellent prognosis and is observed in approximately 10-30% of cases. This variety in tumor response has lead to an increased need to develop a model predictive of responses to CRT in order to identify patients who will benefit from this multimodal treatment. Endoscopy, magnetic resonance imaging, positron emission tomography, serum carcinoembryonic antigen, and molecular biomarkers analyzed using immunohistochemistry and gene expression profiling are the most commonly used predictive models in preoperative CRT. Such modalities guide clinicians in choosing the best possible treatment options and the extent of surgery for each individual patient. However, there are still controversies regarding study outcomes, and a nomogram of combined models of future trends is needed to better predict patient response. The aim of this article was to review currently available tools for predicting tumor response after preoperative CRT in rectal cancer and to explore their applicability in clinical practice for tailored treatment.

Nomogram for predicting breast conservation after neoadjuvant chemotherapy

PURPOSE

The ability to accurately predict the likelihood of achieving breast conservation surgery (BCS) after neoadjuvant chemotherapy (NCT) is important in deciding whether NCT or surgery should be the first-line treatment in patients with operable breast cancers.

MATERIALS AND METHODS

We reviewed the data of 513 women, who had stage II or III breast cancer and received NCT and surgery from a single institution. The ability of various clinicopathologic factors to predict the achievement of BCS and tumor size reduction to ≤ 3 cm was assessed. Nomograms were built and validated in an independent cohort.

RESULTS

BCS was performed in 50.1% of patients, with 42.2% of tumors reduced to ≤ 3 cm after NCT. A multivariate logistic regression analysis showed that smaller initial tumor size, longer distance between the lesion and the nipple, absence of suspicious calcifications on mammography, and a single tumor were associated with BCS rather than mastectomy (p < 0.05). Negative estrogen receptor, smaller initial tumor size, higher Ki-67 level, and absence of in situ component were associated with residual tumor size ≤ 3 cm (p < 0.05). Two nomograms were developed using these factors. The areas under the receiver operating characteristic curves for nomograms predicting BCS and residual tumor ≤ 3 cm were 0.800 and 0.777, respectively. The calibration plots showed good agreement between the predicted and actual probabilities.

CONCLUSION

We have established a model with novel factors that predicts BCS and residual tumor size after NCT. This model can help in making treatment decisions for patients who are candidates for NCT.

Pathologic response prediction to neoadjuvant chemotherapy utilizing pretreatment near-infrared imaging parameters and tumor pathologic criteria

Introduction

The purpose of this study is to develop a prediction model utilizing tumor hemoglobin parameters measured by ultrasound-guided near-infrared optical tomography (US-NIR) in conjunction with standard pathologic tumor characteristics to predict pathologic response before neoadjuvant chemotherapy (NAC) is given.

Methods

Thirty-four patients’ data were retrospectively analyzed using a multiple logistic regression model to predict response. These patients were split into 30 groups of training (24 tumors) and testing (12 tumors) for cross validation. Tumor vascularity was assessed using US-NIR measurements of total hemoglobin (tHb), oxygenated (oxyHb) and deoxygenated hemoglobin (deoxyHb) concentrations acquired before treatment. Tumor pathologic variables of tumor type, Nottingham score, mitotic index, the estrogen and progesterone receptors and human epidermal growth factor receptor 2 acquired before NAC in biopsy specimens were also used in the prediction model. The patients’ pathologic response was graded based on the Miller-Payne system. The overall performance of the prediction models was evaluated using receiver operating characteristic (ROC) curves. The quantitative measures were sensitivity, specificity, positive and negative predictive values (PPV and NPV) and the area under the ROC curve (AUC).

Results

Utilizing tumor pathologic variables alone, average sensitivity of 56.8%, average specificity of 88.9%, average PPV of 84.8%, average NPV of 70.9% and average AUC of 84.0% were obtained from the testing data. Among the hemoglobin predictors with and without tumor pathological variables, the best predictor was tHb combined with tumor pathological variables, followed by oxyHb with pathological variables. When tHb was included with tumor pathological variables as an additional predictor, the corresponding measures improved to 79%, 94%, 90%, 86% and 92.4%, respectively. When oxyHb was included with tumor variables as an additional predictor, these measures improved to 77%, 85%, 83%, 83% and 90.6%, respectively. The addition of tHb or oxyHb significantly improved the prediction sensitivity, NPV and AUC compared with using tumor pathological variables alone.

Conclusions

These initial findings indicate that combining widely used tumor pathologic variables with hemoglobin parameters determined by US-NIR may provide a powerful tool for predicting patient pathologic response to NAC before the start of treatment.

Trial registration

ClincalTrials.gov ID: NCT00908609 (registered 22 May 2009)

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-014-0456-0) contains supplementary material, which is available to authorized users.

Nomogram Predicting Clinical Outcomes in Non-small Cell Lung Cancer Patients Treated with Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors

Purpose

The aim of this study was to develop a pragmatic nomogram for prediction of progressionfree survival (PFS) for the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) in EGFR mutant non-small cell lung cancer (NSCLC).

Materials and Methods

A total of 306 recurred or metastatic NSCLC patients with EGFR mutation, who received EGFR TKIs, were enrolled in this study. We developed the nomogram, using a Cox proportional hazard regression model for PFS.

Results

The median PFS was 11.2 months. Response rate to EGFR TKI was 71.9%. Multivariate Cox model identified disease status, performance status, chemotherapy line, response to EGFR TKI, and bone metastasis as independent prognostic factors, and the nomogram for PFS was developed, based on these covariates. The concordance index for a nomogram was 0.708, and the calibration was also good.

Conclusion

We developed a nomogram, based on clinical characteristics, for prediction of the PFS to EGFR TKI in NSCLC patients with EGFR mutation.

Different methods of pretreatment Ki-67 labeling index evaluation in core biopsies of breast cancer patients treated with neoadjuvant chemotherapy and their relation to response to therapy

Increased proliferation activity of breast cancer cells evaluated by Ki-67 immunohistochemistry, i.e. a high Ki-67 labeling index (Ki-67 LI), may predict better tumor regression in case of neoadjuvant chemotherapy. Despite recommendations for the evaluation of Ki-67 LI, there are variations in methodology. We assessed the effect of different evaluation methods on the Ki-67 LI in patients with different response to neoadjuvant chemotherapy. Thirty pretreatment core-biopsy samples of patients receiving neoadjuvant docetaxel-epirubicin chemotherapy with or without capecitabine were evaluated for their Ki-67 LI. Pathologic regression was categorized as no regression, partial regression and complete regression, with 10 cases in each category. Three antibodies (MIB1, B56, SP6), 4 observers and 4 methods (counting or estimating on glass slides and counting or estimating on representative digital images) were compared. The Kruskal-Wallis test and analyses of variance were performed to investigate the differences in Ki-67 LIs between different clinical outcomes (tumor regression categories). Breast carcinomas with pathological complete regression had a higher mean Ki-67 LI than tumors not achieving complete regression with any methods, observers and antibodies investigated, although there was a variation between different evaluations in what may represent high proliferation. Estimating the Ki-67 LI on digital images representing the highest proliferation in the core biopsy seemed the best in separating complete responders from non-responders. High Ki-67 LI values were more likely associated with pathological complete regression independently of the method of evaluation used, although the definition of high proliferation is problematic. Estimating the Ki-67 LI may be an adequate method of evaluation.

Validation of a Web-Based Tool to Predict the Ipsilateral Breast Tumor Recurrence (IBTR! 2.0) after Breast-Conserving Therapy for Korean Patients

Purpose

IBTR! 2.0 is a web-based nomogram that predicts the 10-year ipsilateral breast tumor recurrence (IBTR) rate after breast-conserving therapy. We validated this nomogram in Korean patients.

Methods

The nomogram was tested for 520 Korean patients, who underwent breast-conserving surgery followed by radiation therapy. Predicted and observed 10-year outcomes were compared for the entire cohort and for each group, predefined by nomogram-predicted risks: group 1, <3%; group 2, 3% to 5%; group 3, 5% to 10%; group 4, >10%.

Results

In overall patients, the overall 10 year predicted and observed estimates of IBTR were 5.22% and 5.70% (p=0.68). In group 1, (n=124), the predicted and observed estimates were 2.25% and 1.80% (p=0.73), in group 2 (n=177), 3.95% and 3.90% (p=0.97), in group 3 (n=181), 7.14% and 8.80% (p=0.42), and in group 4 (n=38), 11.66% and 14.90% (p=0.73), respectively.

Conclusion

In a previous validation of this nomogram based on American patients, nomogram-predicted IBTR rates were overestimated in the high-risk subgroup. However, our results based on Korean patients showed that the observed IBTR was higher than the predicted estimates in groups 3 and 4. This difference may arise from ethnic differences, as well as from the methods used to detect IBTR and the healthcare environment. IBTR! 2.0 may be considered as an acceptable nomogram in Korean patients with low- to moderate-risk of in-breast recurrence. Before widespread use of this nomogram, the IBTR! 2.0 needs a larger validation study and continuous modification.

Clinical usefulness of AJCC response criteria for neoadjuvant chemotherapy in breast cancer

PURPOSE

Recently, the American Joint Committee on Cancer (AJCC) 7th edition proposed new response criteria for neoadjuvant chemotherapy (NAC) in breast cancer. The purpose of this study was to evaluate the clinical usefulness of AJCC response criteria.

METHODS

A total of 398 consecutive stage II or III breast cancer patients who received NAC were enrolled in this study. AJCC response criteria were as follows: (1) complete response (CR)-absence of invasive carcinoma in the breast and node; (2) partial response (PR)-decrease in either or both T or N stage; (3) no response (NR)-no change or increase in either or both T or N stage.

RESULTS

Complete response, PR, and NR by AJCC criteria were 9.8, 59.3, and 30.7 %, respectively. Among the 398 patients, 337 patients were available for both paired pre- and post- breast MRI and chest CT. AJCC response criteria were significantly associated with RECIST criteria (P < 0.001). AJCC response was significantly associated with relapse-free survival (RFS) and overall survival (OS). The 5-year RFS rates were 89.6 % in CR, 74.1 % in PR, and 62.6 % in NR (P = 0.002). The 5-year OS rates were 97.4 % in CR, 88.6 % in PR, and 78.3 % in NR (P = 0.012). When adjusting potential prognostic factors, AJCC response was independently associated with RFS and OS.

CONCLUSIONS

AJCC response criteria for NAC in breast cancer have clinical usefulness in evaluating response of NAC, as well as predicting survival. AJCC response criteria can discriminate among patient subgroups with respect to survival.

Nomogram predicting long-term survival after d2 gastrectomy for gastric cancer

PURPOSE

The aim of this study was to combine clinicopathologic variables associated with overall survival after gastric resection with D2 lymphadenectomy (D2 gastrectomy) for gastric cancer into a prediction nomogram.

PATIENTS AND METHODS

We retrospectively analyzed 7,954 patients who underwent D2 gastrectomy for gastric cancer at Seoul National University Hospital (SNUH) in Seoul, Korea. Two thirds of the patients were randomly assigned to the training set (n = 5,300), and one third were assigned to the validation set (n = 2,654). Multivariate analysis by Cox proportional hazards regression was performed using the training set, and the nomogram was constructed. Discrimination and calibration were performed using the SNUH validation set. Additional external validation was performed using the data set (n = 2,500) from Cancer Institute Ariake Hospital (CIAH) in Tokyo, Japan.

RESULTS

The multivariate Cox model identified age at diagnosis, sex, location, depth of invasion, number of metastatic lymph nodes, and number of examined lymph nodes as covariates associated with survival. In the SNUH validation set, the nomogram exhibited superior discrimination power compared with the seventh American Joint Committee on Cancer TNM classification (Harrell's C-index, 0.78 v 0.69, respectively; P < .001). Calibration of the nomogram predicted survival corresponding closely with the actual survival. In the CIAH validation set, discrimination was good (C-index, 0.79), and the predicted survival was within a 10% margin of ideal nomogram.

CONCLUSION

We developed a nomogram predicting 5- and 10-year overall survival after D2 gastrectomy for gastric cancer. Validation using the SNUH and CIAH data sets revealed good discrimination and calibration, suggesting good clinical utility. The nomogram improved individualized predictions of survival.

Predicting survival in women with breast cancer and brain metastasis: a nomogram outperforms current survival prediction models

BACKGROUND

Brain metastases (BMs) are a common occurrence in patients with breast cancer, and accurately predicting survival in these patients is critical to appropriate management. A survival nomogram for breast cancer patients with BM was constructed, and its performance is compared to current predictive models of survival.

METHODS

A Cox proportional hazards regression with a nomogram representation was used to model survival in a population of 261 women with breast cancer and BMs treated from 1999 to 2008. The model was validated internally by 10-fold cross-validation and bootstrapping, and concordance (c) indices were calculated. The predictive performance of the nomogram described here is compared to current prognostic models, including recursive partitioning analysis, graded prognostic assessment, and diagnosis-specific graded prognostic assessment.

RESULTS

The c-index for the model described here was 0.67. It outperformed recursive partitioning analysis, graded prognostic assessment, and diagnosis-specific graded prognostic assessment, based on c-index comparisons.

CONCLUSIONS

The nomogram described here outperformed current strategies for survival prediction in breast cancer patients with BMs. Two additional advantages of this nomogram are its ability to predict individualized, 1-, 3-, and 5-year survival for novel patients and its straightforward representations of the relative effects of each of 9 covariates on neurologic survival. This represents a potentially valuable alternative to current models of survival prediction in this patient population.