Nomogram to predict the benefit of radiation for older patients with breast cancer treated with conservative surgery

Nomograms to predict lung metastasis in malignant primary osseous spinal neoplasms and cancer-specific survival in lung metastasis subgroup

Purpose

To construct and validate nomograms for predicting lung metastasis probability in patients with malignant primary osseous spinal neoplasms (MPOSN) at initial diagnosis and predicting cancer-specific survival (CSS) in the lung metastasis subgroup.

Methods

A total of 1,298 patients with spinal primary osteosarcoma, chondrosarcoma, Ewing sarcoma, and chordoma were retrospectively collected. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic analysis were used to identify the predictors for lung metastasis. LASSO and multivariate Cox analysis were used to identify the prognostic factors for 3- and 5-year CSS in the lung metastasis subgroup. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA) were used to estimate the accuracy and net benefits of nomograms.

Results

Histologic type, grade, lymph node involvement, tumor size, tumor extension, and other site metastasis were identified as predictors for lung metastasis. The area under the curve (AUC) for the training and validating cohorts were 0.825 and 0.827, respectively. Age, histologic type, surgery at primary site, and grade were identified as the prognostic factors for the CSS. The AUC for the 3- and 5-year CSS were 0.790 and 0.740, respectively. Calibration curves revealed good agreements, and the Hosmer and Lemeshow test identified the models to be well fitted. DCA curves demonstrated that nomograms were clinically useful.

Conclusion

The nomograms constructed and validated by us could provide clinicians with a rapid and user-friendly tool to predict lung metastasis probability in patients with MPOSN at initial diagnosis and make a personalized CSS evaluation for the lung metastasis subgroup.

Prognostic model of ER-positive, HER2-negative breast cancer predicted by clinically relevant indicators

PURPOSE

To study the clinicopathological variables connected with disease-free survival (DFS) as well as overall survival (OS) in patients who are ER-positive or HER2-negative and to propose nomograms for predicting individual risk.

METHODS

In this investigation, we examined 585 (development cohort) and 291 (external validation) ER-positive, HER2-negative breast cancer patients from January 2010 to January 2014. From January 2010 to December 2014, we retrospectively reviewed and analyzed 291 (external validation) and 585 (development cohort) HER2-negative, ER-positive breast cancer patients. Cox regression analysis, both multivariate and univariate, confirmed the independence indicators for OS and DFS.

RESULTS

Using cox regression analysis, both multivariate and univariate, the following variables were combined to predict the DFS of development cohort: pathological stage (HR = 1.391; 95% CI = 1.043-1.855; P value = 0.025), luminal parting (HR = 1.836; 95% CI = 1.142-2.952; P value = .012), and clinical stage (HR = 1.879; 95% CI = 1.102-3.203; P value = 0.021). Endocrine therapy (HR = 3.655; 95% CI = 1.084-12.324; P value = 0.037) and clinical stage (HR = 6.792; 95% CI = 1.672-28.345; P value = 0.009) were chosen as predictors of OS. Furthermore, we generated RS-OS and RS-DFS. According to the findings of Kaplan-Meier curves, patients who are classified as having a low risk have considerably longer DFS and OS durations than patients who are classified as having a high risk.

CONCLUSION

To generate nomograms that predicted DFS and OS, independent predictors of DFS in ER-positive/HER2-negative breast cancer patients were chosen. The nomograms successfully stratified patients into prognostic categories and worked well in both internal validation and external validation.

Establishment and validation of a competitive risk model for predicting cancer-specific survival in patients with osteosarcoma: a population-based study

BACKGROUND

Osteosarcoma is the most common primary bone tumor with a poor prognosis. The aim of this study was to establish a competitive risk model nomogram to predict cancer-specific survival in patients with osteosarcoma.

METHODS

Patient data was obtained from the Surveillance, Epidemiology, and End Results database in the United States. A sub-distribution proportional hazards model was used to analyze independent risk factors affecting cancer-specific mortality (CSM) in osteosarcoma patients. Based on these risk factors, a competitive risk model was constructed to predict 1-year, 3-year, and 5-year cancer-specific survival (CSS) in osteosarcoma patients. The reliability and accuracy of the nomogram were evaluated using the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), and calibration curves.

RESULTS

A total of 2900 osteosarcoma patients were included. The analysis showed that age, primary tumor site, M stage, surgery, chemotherapy, and median household income were independent risk factors influencing CSM in patients. The competitive risk model was constructed to predict CSS in osteosarcoma patients. In the training and validation sets, the C-index of the model was 0.756 (95% CI 0.725-0.787) and 0.737 (95% CI 0.717-0.757), respectively, and the AUC was greater than 0.7 for both. The calibration curves also demonstrated a high consistency between the predicted survival rates and the actual survival rates, confirming the accuracy and reliability of the model.

CONCLUSION

We established a competitive risk model to predict 1-year, 3-year, and 5-year CSS in osteosarcoma patients. The model demonstrated good predictive performance and can assist clinicians and patients in making clinical decisions and formulating follow-up strategies.

What Role Does Radiotherapy Play in the Molecular Era for Intrahepatic Cholangiocarcinoma?

ABSTRACT

Intrahepatic cholangiocarcinoma is a rare disease, yet with rising incidence globally. Most patients are not eligible for potentially curative surgical resection, and many patients with unresectable disease die within 12 months of diagnosis, primarily due to liver failure from the primary tumor. Recent prospective and retrospective studies indicate that local control of the primary tumor can be achieved with hypofractionated radiotherapy in patients with unresectable disease, translating into prolonged survival of these patients. During the time that these encouraging reports for radiotherapy have been published, numerous concurrent studies have also shown that intrahepatic cholangiocarcinoma is a molecularly diverse disease with multiple targetable genetic alterations and a complex tumor microenvironment. These biological insights have translated into new drug approvals for subsets of patients. We review the current knowledge about the biology and targeted treatment of intrahepatic cholangiocarcinoma and describe these developments in the context of modern radiotherapy.

Prognostic Tools for Older Women with Breast Cancer: A Systematic Review

Background: Breast cancer is the most common cancer in women, and older patients comprise an increasing proportion of patients with this disease. The older breast cancer population is heterogenous with unique factors affecting clinical decision making. While many models have been developed and tested for breast cancer patients of all ages, tools specifically developed for older patients with breast cancer have not been recently reviewed. We systematically reviewed prognostic models developed and/or validated for older patients with breast cancer. Methods: We conducted a systematic search in 3 electronic databases. We identified original studies that were published prior to 8 November 2022 and presented the development and/or validation of models based mainly on clinico-pathological factors to predict response to treatment, recurrence, and/or mortality in older patients with breast cancer. The PROBAST was used to assess the ROB and applicability of each included tool. Results: We screened titles and abstracts of 7316 records. This generated 126 studies for a full text review. We identified 17 eligible articles, all of which presented tool development. The models were developed between 1996 and 2022, mostly using national registry data. The prognostic models were mainly developed in the United States (n = 7; 41%). For the derivation cohorts, the median sample size was 213 (interquartile range, 81-845). For the 17 included modes, the median number of predictive factors was 7 (4.5-10). Conclusions: There have been several studies focused on developing prognostic tools specifically for older patients with breast cancer, and the predictions made by these tools vary widely to include response to treatment, recurrence, and mortality. While external validation was rare, we found that it was typically concordant with interval validation results. Studies that were not validated or only internally validated still require external validation. However, most of the models presented in this review represent promising tools for clinical application in the care of older patients with breast cancer.

The effect of adjuvant radiotherapy after breast-conserving surgery in elderly women with T1-2N0 estrogen receptor-negative breast cancer

PURPOSE

To evaluate whether adjuvant radiotherapy (RT) following breast-conserving surgery (BCS) results in better survival among women ≥ 70 years with T1-2N0 estrogen receptor (ER)-negative breast cancer.

METHODS

In this retrospective cohort study, we included patients who met the inclusion criteria between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) program. Univariate and Multivariate Cox proportional analysis were used to identify the risk factors for overall survival (OS) and breast cancer-specific survival (BCSS). Kaplan-Meier survival analysis was used to compare the prognosis of patients with or without adjuvant RT. Propensity score matching (PSM) was applied to perform a 1:1 matched case-control analysis.

RESULTS

A total of 4201 women were included in this study, with a median follow-up time of 64 months (range: 0-107 months). Of these patients, 2811 (66.9%) received adjuvant RT, while 1390 (33.1%) did not. Patients who did not receive adjuvant RT were more likely to be aged ≥ 80 years old, have a single marital status, larger tumors, and HER2-positive status (p < 0.05). Multivariate Cox proportional analysis indicated that receiving adjuvant RT was an independent factor associated with better OS and BCSS before and after PSM (P < 0.001). The survival curves before and after PSM showed that patients achieved an improved OS and BCSS from adjuvant RT (P < 0.005). In the subgroup analysis, there was no survival benefit trend from adjuvant RT in patients who were ≥ 80 years, or those with T1mic+T1a, T1b tumors.

CONCLUSIONS

The use of RT following BCS in older women with T1-2N0 ER-negative breast cancer is associated with improve OS and BCSS. However, the potential benefit may be relatively limited for patients ≥ 80 years, or those with T1mic+T1a, T1b tumors.

Nomogram-Based Survival Predictions and Treatment Recommendations for Locally Advanced Esophageal Squamous Cell Carcinoma Treated with Upfront Surgery

Background and purpose: The aim of this study is to develop a prognostic nomogram, quantify survival benefit, and guide risk-dependent adjuvant therapy for locally advanced esophageal squamous cell carcinoma (LA-ESCC) after esophagectomy. Materials and methods: This was a single-center, retrospective study of consecutive LA-ESCCs treated by curative-intent esophagectomy with internal validation and independent external validation in a randomized controlled trial. After factor selection by the least absolute shrinkage and selection operator regression, a nomogram was developed to estimate 5-year overall survival (OS) based on the Cox proportional hazards model. The area under the curve (AUC) and calibration plot were used to determine its discriminative and predictive capacities, respectively. Survival improvement from adjuvant therapy was quantified and plotted corresponding to nomogram score. Results: A total of 1077, 718, and 118 patients were included for model development, internal validation, and external validation, respectively. The nomogram identified eight significant prognostic factors: gender, pathological T and N stages, differentiation, surgical margin, lymphovascular invasion, number of lymph node resection, and adjuvant therapy. The nomogram showed superior discriminative capacity than TNM stage (AUC: 0.76 vs. 0.72, p < 0.01), with significant survival differences among different risk stratifications. The calibration plot illustrated a good agreement between nomogram-predicated and actual 5-year OS. Consistent results were concluded after external validation. At least 10% 5-year OS improvement from adjuvant chemoradiotherapy and chemotherapy was expected in almost all patients (nomogram score 110 to 260) and patients mainly with high-intermediate risk (nomogram score 159 to 207), respectively. Conclusions: The clinicopathological nomogram predicting 5-year OS for LA-ESCC after esophagectomy was developed with high accuracy. The proposed nomogram showed better performance than TNM stage and provided risk-dependent and individualized adjuvant treatment recommendations.

A tool for predicting overall survival in patients with Ewing sarcoma: a multicenter retrospective study

OBJECTIVE

The aim of this study was to establish and validate a clinical prediction model for assessing the risk of metastasis and patient survival in Ewing's sarcoma (ES).

METHODS

Patients diagnosed with ES from the Surveillance, Epidemiology and End Results (SEER) database for the period 2010-2016 were extracted, and the data after exclusion of vacant terms was used as the training set (n=767). Prediction models predicting patients' overall survival (OS) at 1 and 3 years were created by cox regression analysis and visualized using Nomogram and web calculator. Multicenter data from four medical institutions were used as the validation set (n=51), and the model consistency was verified using calibration plots, and receiver operating characteristic (ROC) verified the predictive ability of the model. Finally, a clinical decision curve was used to demonstrate the clinical utility of the model.

RESULTS

The results of multivariate cox regression showed that age, , bone metastasis, tumor size, and chemotherapy were independent prognostic factors of ES patients. Internal and external validation results: calibration plots showed that the model had a good agreement for patient survival at 1 and 3 years; ROC showed that it possessed a good predictive ability and clinical decision curve proved that it possessed good clinical utility.

CONCLUSIONS

The tool built in this paper to predict 1- and 3-year survival in ES patients ( https://drwenleli0910.shinyapps.io/EwingApp/ ) has a good identification and predictive power.

A Novel Tool to Predict the Overall Survival of High-Grade Osteosarcoma Patients after Neoadjuvant Chemotherapy: A Large Population-Based Cohort Study

Background. The goal of this study was to discover clinical factors linked to overall survival in patients with high-grade osteosarcoma who had received neoadjuvant therapy and to develop a prognostic nomogram and risk classification system. Methods. A total of 762 patients with high-grade osteosarcoma were included in this study. In the training cohort, Cox regression analysis models were used to find prognostic variables that were independently linked with overall survival. To predict overall survival at 3, 5, and 8 years, a nomogram is created. In addition, in both the internal and external validation cohorts, receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA) were utilized to assess the prediction model’s performance. Results. The age, size of the tumor, and the stage of the disease are all important predictive variables for overall survival. The training and validation cohorts have C-indexes of 0.699 and 0.669, respectively. At the same time, the area under the curve values for both cohorts also showed that the nomogram had good discriminatory power. The calibration curve demonstrated the good performance and predictive accuracy of the model. The DCA results suggest that the nomogram has a wide range of therapeutic applications. Furthermore, a new risk classification system based on the nomogram was established, which allows all patients to be classified into three subgroups as high, middle, and low risk of death. Conclusion. The prognostic nomogram constructed in this study may provide a better precise prognostic prediction for patients with high-grade osteosarcoma after neoadjuvant chemotherapy.

Prognostic Factor Analysis and Model Construction of Triple-Negative Metaplastic Breast Carcinoma After Surgery

Objective

The study aimed to analyze the prognostic factors of patients with triple-negative (TN) metaplastic breast carcinoma (MpBC) after surgery and to construct a nomogram for forecasting the 3-, 5-, and 8-year overall survival (OS).

Methods

A total of 998 patients extracted from the Surveillance, Epidemiology, and End Results (SEER) database were assigned to either the training or validation group at random in a ratio of 7:3. The clinical characteristics of patients in the training and validation sets were compared, and multivariate Cox regression analysis was used to identify the independent risk variables for the OS of patients with TN MpBC after surgery. These selected parameters were estimated through the Kaplan–Meier (KM) curves using the log-rank test. The nomogram for predicting the OS was constructed and validated by performing the concordance index (C-index), receiver operating characteristics (ROC) curves with area under the receiver operating characteristic curves (AUC), calibration curves, and decision curve analyses (DCAs). Patients were then stratified as high-risk and low-risk, and KM curves were performed.

Results

Multivariate Cox regression analysis indicated that factors including age, marital status, clinical stage at diagnosis, chemotherapy, and regional node status were independent predictors of prognosis in patients with MpBC after surgery. Separate KM curves for the screened variables revealed the same statistical results as with Cox regression analysis. A prediction model was created and virtualized via nomogram based on these findings. For the training and validation cohorts, the C-index of the nomogram was 0.730 and 0.719, respectively. The AUC values of the 3-, 5-, and 8-year OS were 0.758, 0.757, and 0.785 in the training group, and 0.736, 0.735, and 0.736 for 3, 5, and 8 years in the validation group, respectively. The difference in the OS between the real observation and the forecast was quite constant according to the calibration curves. The generated clinical applicability of the nomogram was further demonstrated by the DCA analysis. In all the training and validation sets, the KM curves for the different risk subgroups revealed substantial differences in survival probabilities (P <0.001).

Conclusion

The study showed a nomogram that was built from a parametric survival model based on the SEER database, which can be used to make an accurate prediction of the prognosis of patients with TN MpBC after surgery.

Screening of Adverse Prognostic Factors and Construction of Prognostic Index in Previously Untreated Concurrent Follicular Lymphoma and Diffuse Large B-Cell Lymphoma

Objective

Concurrent follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL) (defined as FL/DLBCL) have been considered an important pathological feature in cell lymphoma. However, clinicopathological information and prognostic factors in these cases are scarce. The aim of this study was to construct a prediction index to compare with traditional prognostic models.

Methods

Retrospectively enrolled, previously untreated FL/DLBCL (n = 121) patients, as well as those with pure FL 1-3a (n = 471), were assessed. De novo DLBCL (n = 529) were used as controls. Kaplan-Meier curves were plotted to compare the outcomes among the three groups. Multivariate analysis identified risk factors associated with overall survival (OS) in FL/DLBCL patients. A clinicopathological prognosis index (CPPI) was developed to predict OS based on the Cox proportional hazards model.

Results

The outcomes of FL/DLBCL patients were intermediate between pure FL 1-3a and de novo DLBCL patients, with a 5-year PFS of 70%, 59%, and 48% (P < 0.05) and 5-year OS of 80%, 70% and 60% (P < 0.05), respectively. Cox regression analysis showed that the prognostic factors of OS for FL/DLBCL patients included FL grade, cell of origin, and Ann Arbor stage. A nomogram and clinicopathological prognostic index (CPPI) were developed to predict the OS for FL/DLBCL patients based on these factors. The area under the curve (AUC) of the CPPI for 3- and 5-year OS prediction was 0.782 and 0.860, respectively. This was superior to that of the International Prognostic Index (IPI), Follicular Lymphoma International Prognostic Index (FLIPI), and FLIPI2 in the 0.540-0.819 (P < 0.01) range.

Conclusions

A valid OS estimation in FL/DLBCL patients, using the recommended CPPI, may be useful in routine clinical practice.

A scoping review of interactive and personalized web-based clinical tools to support treatment decision making in breast cancer

The increasing attention on personalized breast cancer care has resulted in an explosion of new interactive, tailored, web-based clinical decision tools for guiding treatment decisions in clinical practice. The goal of this study was to review, compare, and discuss the clinical implications of current tools, and highlight future directions for tools aiming to improve personalized breast cancer care. We searched PubMed, Embase, PsychInfo, Cochrane Database of Systematic Reviews, Web of Science, and Scopus to identify web-based decision tools addressing breast cancer treatment decisions. There was a total of 17 articles associated with 21 unique tools supporting decisions related to surgery, radiation therapy, hormonal therapy, bisphosphonates, HER2-targeted therapy, and chemotherapy. The quality of the tools was assessed using the International Patient Decision Aid Standard instrument. Overall, the tools considered clinical (e.g., age) and tumor characteristics (e.g., grade) to provide personalized outcomes (e.g., survival) associated with various treatment options. Fewer tools provided the adverse effects of the selected treatment. Only one tool was field-tested with patients, and none were tested with healthcare providers. Future studies need to assess the feasibility, usability, acceptability, as well as the effects of personalized web-based decision tools on communication and decision making from the patient and clinician perspectives.

Semiparametric copula-based regression modeling of semi-competing risks data

Semi-competing risks data often arise in medical studies where the terminal event (e.g., death) censors the non-terminal event (e.g., cancer recurrence), but the non-terminal event does not prevent the subsequent occurrence of the terminal event. This article considers regression modeling of semi-competing risks data to assess the covariate effects on the respective non-terminal and terminal event times. We propose a copula-based framework for semi-competing risks regression with time-varying coefficients, where the dependence between the non-terminal and terminal event times is characterized by a copula and the time-varying covariate effects are imposed on two marginal regression models. We develop a two-stage inferential procedure for estimating the association parameter in the copula model and time-varying regression parameters. We evaluate the finite sample performance of the proposed method through simulation studies and illustrate the method through an application to Surveillance, Epidemiology, and End Results-Medicare data for elderly women diagnosed with early-stage breast cancer and initially treated with breast-conserving surgery.

Nomogram for Predicting the Prognoses of Patients With Pancreatic Head Cancer After Pancreaticoduodenectomy: A Population-Based Study on SEER Data

Objective

In this study, we retrieved the data available in the Surveillance, Epidemiology, and End Results database to identify the prognostic factors for patients with pancreatic head cancer who had undergone pancreaticoduodenectomy and developed a prediction model for clinical reference.

Methods

We screened the data between 1973 and 2015. Propensity score matching (PSM) was used to control for the confounding factors. Kaplan-Meier (log-rank test) curves were used to compare the survival rates. A nomogram was established using multifactorial Cox regression.

Results

In total, 4099 patients were identified. Their median survival was 22 months, with 74.2%, 36.5%, and 26.2% survival after 1, 3, and 5 years, respectively. The median cancer-specific survival was 24.0 months, with 71.1%, 32.6%, and 21.9% survival after 1, 3, and 5 years, respectively. The results of the Cox proportional risk regression showed that age, insurance status, gender, histological type, degree of tissue differentiation, T and N stages, tumor size, extent of regional lymph node dissection, and postoperative radiotherapy or chemotherapy are independent factors affecting prognosis. PSM was used twice to eliminate any bias from the unbalanced covariates in the raw data. After PSM, the patients who had received postoperative radiotherapy were found to have a better survival prognosis and disease-specific survival prognosis than those who had not received radiotherapy [HR = 0.809, 95% CI (0.731–0.894), P < 0.001 and HR = 0.814, 95% CI (0.732–0.904), P < 0.001; respectively]. A similar result was observed for the patients who had received postoperative chemotherapy versus those who had not [HR = 0.703, 95% CI (0.633–0.78), P < 0.001 and HR = 0.736, 95% CI (0.658–0.822), P < 0.001, for survival and disease-specific survival prognoses, respectively]. Finally, the β coefficients of the Cox proportional risk regression were used to establish a nomogram.

Conclusion

Age, insurance status, gender, histological type, degree of differentiation, T and N stages, tumor size, regional lymph node dissection, and postoperative radiotherapy or chemotherapy are factors affecting the prognosis in pancreatic head cancer after pancreaticoduodenectomy. Postoperative radiotherapy and chemotherapy can improve patient survival. These still need to be further validated in the future.

Development and Validation of a Novel Prognostic Nomogram for CD5-Positive Diffuse Large B-Cell Lymphoma: A Retrospective Multicenter Study in China

Background

CD5-positive diffuse large B-cell lymphoma (CD5+ DLBCL) is a rare subtype of DLBCL with invasive clinical features and poor prognosis. Current clinical variables based on prognostic systems for DLBCL are inadequate to accurately stratify the prognosis of CD5+ DLBCL.

Methods

A total of 195 CD5+ DLBCL patients were retrospectively recruited from nine centers in Huaihai Lymphoma Working Group. MaxStat analysis was used to identify optimal cutoff points for continuous variables; univariable and multivariable Cox analyses were used for variable selection; Kaplan–Meier curve was used to analyze the value of variables on prognosis; and C-index, Brier score, and decision curve analysis were measured for predicting model performance.

Results

The derivation and validation cohorts consisted of 131 and 64 patients. Of the whole cohort, median age at diagnosis was 61 years, of whom 100 (51.28%) were males and the 5‐year overall survival rate was 42.1%. MYC, BCL-2, and the coexpression of MYC/BCL-2 could distinguish the survival of CD5+ DLBCL. Multivariable analysis showed that age, IPI, red blood cell count, neutrophil count, MYC expression, and hepatosplenomegaly were independent predictors, and the prognostic nomogram was developed. The C‐index of the nomogram was 0.809 in the derivation and 0.770 in the validation cohort. Decision curve analysis proved that compared with IPI, the specific nomogram showed a better identification in CD5+ DLBCL.

Conclusion

The proposed nomogram provided a valuable tool for prognosis prediction in patients with CD5+ DLBCL.

A Nomogram Based on Aspartate Aminotransferase/Alanine Aminotransferase (AST/ALT) Ratio to Predict Prognosis After Surgery in Gastric Cancer Patients

INTRODUCTION

Using the TMN classification alone to predict survival in patients with gastric cancer has certain limitations, we conducted this study was to develop an effective nomogram based on aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio to predict overall survival (OS) in surgically treated gastric cancer.

METHODS

we retrospectively analyzed 190 cases of gastric cancer and used Cox regression analysis to identify the significant prognostic factors for OS in patients with resectable gastric cancer. The predictive accuracy of nomogram was assessed using a calibration plot, concordance index (C-index) and decision curve. This was then compared with a traditional TNM staging system. Based on the total points (TPS) by nomogram, we further divided patients into different risk groups.

RESULTS

multivariate analysis of the entire cohort revealed that independent risk factors for survival were age, clinical stage and AST/ALT ratio, which were entered then into the nomogram. The calibration curve for the probability of OS showed that the nomogram-based predictions were in good agreement with actual observations. Additionally, the C-index of the established nomogram for predicting OS had a superior discrimination power compared to the TNM staging system [0.794 (95% CI: 0.749-0.839) vs 0.730 (95% CI: 0.688-0.772), p < 0.05]. Decision curve also demonstrated that the nomogram was better than the TNM staging system. Based on TPS of the nomogram, we further subdivided the study cohort into 3 groups including low risk (TPS ≤ 158), middle risk (158 < TPS ≤ 188) and high risk (TPS > 188) categories. The differences in OS rate were significant among the groups.

CONCLUSION

the established nomogram is associated with a more accurate prognostic prediction for individual patients with resectable gastric cancer.

The Effect of Postmastectomy Radiotherapy on Breast Cancer Patients After Neoadjuvant Chemotherapy by Molecular Subtype

BACKGROUND

The effect of postmastectomy radiotherapy (PMRT) on patient outcomes after neoadjuvant chemotherapy (NAC) remains controversial. We aimed to establish a model to identify the subsets benefiting from PMRT and to examine the effect of PMRT according to molecular subtype.

PATIENTS AND METHODS

We retrospectively analyzed 1118 cT1-4cN0-3M0 breast cancer patients treated with NAC and mastectomy. A nomogram predicting locoregional recurrence (LRR) was established based on 418 unirradiated patients, and X-tile analysis was performed to divide the patients into two risk groups. The effect of PMRT on LRR, distant recurrence (DR), and breast cancer mortality (BCM) was estimated for patients with different molecular subtypes in two risk groups.

RESULTS

A nomogram predicting LRR was developed using six factors: histologic classification, lymphovascular invasion, ypT stage, ypN stage, estrogen receptor status, and Ki-67 expression. Our study found that PMRT correlated with lower 5-year LRR, DR, and BCM rates for the high-risk group; however, no significant improvement in these endpoints was observed in the low-risk group. Among patients with high risk, subgroup analysis showed that LRR control was improved after PMRT for the human epidermal growth factor receptor 2 (HER2)-negative/hormone receptor (HR)-positive (HER2-/HR+), HER2-positive (HER2+)/HR+, and HER2-/HR-negative (HR-) subtypes, with hazard ratios of 0.113 (95% confidence [CI] 0.034-0.379; p < 0.001), 0.159 (95% CI 0.038-0.671; p = 0.017), and 0.243 (95% CI 0.088-0.676; p = 0.007), respectively, but not for the HER2+/HR- subtype (p = 0.468).

CONCLUSIONS

We built a nomogram showing favorable risk quantification and patient stratification. Patients in the high-risk group benefited from PMRT, but patients in the low-risk group did not. PMRT may show different benefits for each molecular subtype.

Supraclavicular lymph node metastasis in elderly patients undergoing esophageal squamous cell carcinoma radical surgery: construction of risk and prognostic predictive nomograms

Background

Supraclavicular lymph node metastasis (SCLN) is an adverse prognostic determinant of esophageal cancer. However, lymphadenectomy for SCLN is a traumatic procedure, especially in elderly patients, which is associated with more postoperative complications. Currently, identification of risk factors of SCLN metastasis and avoidance of unnecessary lymphadenectomy for SCLN in esophageal squamous cell carcinoma (ESCC) patients has become an unmet clinical need.

Methods

A total of 90 elderly patients with ESCC between January 2008 and December 2013 was eligible for this analysis. Logistic regression was performed to determine risk factors for SCLN metastasis after ESCC radical surgery in elderly patients. A nomogram was constructed to individually predict the risk for SCLN metastasis. The Kaplan-Meier survival curve and cumulative risk curve were further analyzed to evaluate the effect of SCLN metastasis after ESCC radical surgery on survival prognosis and cumulative risk assessment in elderly patients. Finally, the SCLN metastasis group and the independent risk factor group were fitted by drawing a decision curve to evaluate the net benefit of the model.

Results

SCLN developed in 38 patients (42.2%). Postoperative lymph node metastasis (P<0.05), tumor thrombus (P<0.05) and tumor infiltration (P<0.05) were independent risk factors for SCLN metastasis. The influence of SCLN metastasis on postoperative survival in elderly ESCC patients was statistically significant (P=0.028, P <0.05); with the passage of time, the cumulative risk of SCLN metastasis increased, the survival probability decreased, and the survival time was shortened.

Conclusions

Postoperative lymph node metastasis, tumor thrombus and tumor infiltration are independent risk factors for recurrence and metastasis of SCLNs in elderly patients with esophageal squamous cell carcinoma. The nomogram model based on these factors provides a preliminary reference for individualized risk assessment, prognosis guidance and decision-making of SCLN metastasis in elderly patients with esophageal squamous cell carcinoma (ESCC).

Radiotherapy after breast-conserving surgery for elderly patients with early-stage breast cancer: A national registry-based study

Considerable controversies exist regarding whether elderly patients with early-stage breast cancer receiving breast-conserving surgery (BCS) should forgo radiotherapy. We utilized the National Cancer Database to analyze data of 115 516 women aged ≥70 years, treated with BCS for T1-2N0-1M0 breast cancer between 2004 and 2014. Multivariable Cox proportional hazards model was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for mortality 3, 5 and 10 years after 90 days of BCS associated with radiotherapy. Patients who received no radiotherapy had a higher mortality rate than those who received radiotherapy (5-year survival rate: 71.2% vs 83.8%), with multivariable-adjusted HRs of 1.65 (95% CI: 1.57-1.72) for 3-year mortality, 1.53 (1.47-1.58) for 5-year mortality and 1.43 (1.39-1.48) for 10-year mortality. The association held even for patients ≥90 years. This association was observed in all strata by reasons for radiotherapy omission, receipt of endocrine therapy or chemotherapy, calendar period and other clinical characteristics, with 40% to 65% increased 5-year mortality for patients without radiotherapy. This positive association persisted when analyses were restricted to patients with T1N0 and estrogen-receptor-positive disease who had received endocrine therapy (5-year mortality: HR 1.47 [1.39-1.57]) and in propensity score weighted analyses. Our study shows, in routine practice, elderly patients who received no post-BCS radiotherapy had higher total mortality than those who received radiotherapy. These findings suggest that the current recommendation of omission of post-BCS radiotherapy for elderly women with early-stage breast cancer may need to be reconsidered, particularly for those without contraindication.

Nomogram for Predicting Breast Cancer-Specific Mortality of Elderly Women with Breast Cancer

Background

The objectives of this study were to evaluate the cumulative incidence of breast cancer-specific death (BCSD) and other cause-specific death in elderly patients with breast cancer (BC) and to develop an individualized nomogram for estimating BCSD.

Material/Methods

Data were retrieved from the Surveillance, Epidemiology, and End Results program. A total of 25 241 patients older than 65 years with stage I–III BC diagnosed between 2004 and 2008 was included in the study cohort. We used the cumulative incidence function (CIF) to describe the cause-specific mortality and Gray’s test to compare the differences in CIF among the groups. Fine and Gray’s proportional subdistribution hazard model was applied to validate the independent prognostic factors, upon which the competing-risks nomogram and web-based calculator was built. The performance of the nomogram was assessed with the C-indexes and calibration plot diagrams.

Results

After data screening, 25 241 cases were included for statistical analysis. In the training cohort, the 5-, 8-, and 10-year cumulative incidence of BCSD was 5.7, 8.1, and 9.1%, respectively. Ten independent prognostic factors associated with BCSD were identified. The C-index of the nomogram was 0.818 (0.804–0.831) in the training cohort and 0.808 (0.783–0.833) in the validation cohort. Calibration plot diagrams showed near-ideal consistency between the predicted probabilities and actual observations.

Conclusions

We built a reliable dynamic nomogram for predicting BCSD in elderly patients, and this individualized predictive tool is favorable for risk classification and complex personalized treatment decision making in clinical practice.

Development and validation of a novel nomogram for predicting the prognosis of patients with resected pancreatic adenocarcinoma

The survival prediction for patients with resected pancreatic adenocarcinoma by using the Tumor-Node-Metastasis (TNM) staging system remains limited. A nomogram is a efficient tool that can be used to predict the outcome of patients with various types of malignancy. The present study aimed to develop and validate a nomogram for patients with resected pancreatic adenocarcinoma. A total of 368 patients (258 in the training set and 110 in the validation set) who underwent pancreatic adenocarcinoma resection at the China National Cancer Center between January 2008 and October 2018 were included in the present study. The nomogram was established according to the results from Cox multivariate analysis, which was validated by discrimination and calibration. The area under the receiver operating characteristic curve (AUC) was determined to assess the accuracy of survival predictions. The results from multivariate analysis in the training set demonstrated that blood transfusion, T-stage, N-stage, tumor grade, capsule invasion, carbohydrate antigen 199, neutrophil percentage and adjuvant therapy were independent prognostic factors for overall survival (OS; all P<0.05). Subsequently, a nomogram predicting the 1-year, 3-year and 5-year OS rates, with favorable calibration, was established based on the independent prognostic factors. The concordance indices of the nomogram were higher compared with the TNM staging system in both training and validation sets. Furthermore, a clear risk stratification system based on the nomogram was used to classify patients into the three following groups: Low-risk group (≤168), moderate-risk group (168–255) and high-risk group (>255). The risk stratification system demonstrated an improved ability in predicting the 1-year, 3-year and 5-year OS rates compared with the TNM system (AUC, 0.758, 0.709 and 0.672 vs. AUC, 0.614, 0.604 and 0.568; all P<0.05). The present study developed and validated a nomogram for patients with resected pancreatic adenocarcinoma by including additional independent prognostic factors, including tumor marker, immune index, surgical information, pathological data and adjuvant therapy. Taken together, the results from the present study indicated an improved performance of the nomogram in predicting the prognosis of patients with resected pancreatic adenocarcinoma compared with the TNM staging system.

Precision Medicine and the Role of Biomarkers of Radiotherapy Response in Breast Cancer

Radiotherapy remains an important treatment modality in nearly two thirds of all cancers, including the primary curative or palliative treatment of breast cancer. Unfortunately, largely due to tumor heterogeneity, tumor radiotherapy response rates can vary significantly, even between patients diagnosed with the same tumor type. Although in recent years significant technological advances have been made in the way radiation can be precisely delivered to tumors, it is proving more difficult to personalize radiotherapy regimens based on cancer biology. Biomarkers that provide prognostic or predictive information regarding a tumor's intrinsic radiosensitivity or its response to treatment could prove valuable in helping to personalize radiation dosing, enabling clinicians to make decisions between different treatment options whilst avoiding radiation-induced toxicity in patients unlikely to gain therapeutic benefit. Studies have investigated numerous ways in which both patient and tumor radiosensitivities can be assessed. Tumor molecular profiling has been used to develop radiosensitivity gene signatures, while the assessment of specific intracellular or secreted proteins, including circulating tumor cells, exosomes and DNA, has been performed to identify prognostic or predictive biomarkers of radiation response. Finally, the investigation of biomarkers related to radiation-induced toxicity could provide another means by which radiotherapy could become personalized. In this review, we discuss studies that have used these methods to identify or develop prognostic/predictive signatures of radiosensitivity, and how such assays could be used in the future as a means of providing personalized radiotherapy.

Clinical nomogram for predicting the survival of patients with cerebral anaplastic gliomas

The present study aimed to develop an effective nomogram for predicting the overall survival (OS) of patients with cerebral anaplastic glioma (AG).This study included 1939 patients diagnosed with AG between 1973 and 2013 who were identified using the Surveillance, Epidemiology, and End Results database. A multivariate Cox regression analysis revealed that age, histology, tumor site, marital status, radiotherapy, and surgery were independent prognostic factors and, thus, these factors were selected to build a clinical nomogram. Harrell's concordance index (C-index) and a calibration curve were formulated to evaluate the discrimination and calibration of the nomogram using bootstrapping.A nomogram was developed to predict 5- and 9-year OS rates based on 6 independent prognostic factors identified in the training set: age, tumor site, marital status, histology, radiotherapy, and surgery (P < .05). The Harrell's concordance index values of the training and validation sets were 0.776 (0.759-0.793) and 0.766 (0.739-0.792), respectively. The calibration curve exhibited good consistency with the actual observation curve in both sets.Although the prognostic value of the World Health Organization (WHO) classification has been validated, we developed a novel nomogram based on readily available clinical variables in terms of demographic data, therapeutic modalities, and tumor characteristics to predict the survival of AG patients. When used in combination with the WHO classification system, this clinical nomogram can aid clinicians in making individualized predictions of AG patient survival and improving treatment strategies.

Could a Personalized Strategy Using Accelerated Partial Breast Irradiation be an Advantage for Elderly Patients? A Systematic Review of the Literature and Multidisciplinary Opinion

Introduction. Elderly patients are underrepresented from a majority of clinical trials and the choice of the best treatment becomes a challenge. The optimal treatment should be personalized and based on a multidisciplinary approach that includes radiation oncologists, surgeons, geriatricians, medical oncologists, social workers, and support services. The global evaluation of the patients and the creation of nomograms may facilitate the definition of long-term treatment benefits minimizing the use of unnecessary therapy. Material and Method. A systematic research using PubMed, Scopus, and Cochrane library was performed to identify full articles analyzing the efficacy of APBI in elderly patients with breast cancer. ClinicalTrials.gov was searched for ongoing or recently completed trials, and PROSPERO was searched for ongoing or recently completed systematic reviews.

Improving prediction performance of colon cancer prognosis based on the integration of clinical and multi-omics data

BACKGROUND

Colon cancer is common worldwide and is the leading cause of cancer-related death. Multiple levels of omics data are available due to the development of sequencing technologies. In this study, we proposed an integrative prognostic model for colon cancer based on the integration of clinical and multi-omics data.

METHODS

In total, 344 patients were included in this study. Clinical, gene expression, DNA methylation and miRNA expression data were retrieved from The Cancer Genome Atlas (TCGA). To accommodate the high dimensionality of omics data, unsupervised clustering was used as dimension reduction method. The bias-corrected Harrell's concordance index was used to verify which clustering result provided the best prognostic performance. Finally, we proposed a prognostic prediction model based on the integration of clinical data and multi-omics data. Uno's concordance index with cross-validation was used to compare the discriminative performance of the prognostic model constructed with different covariates.

RESULTS

Combinations of clinical and multi-omics data can improve prognostic performance, as shown by the increase of the bias-corrected Harrell's concordance of the prognostic model from 0.7424 (clinical features only) to 0.7604 (clinical features and three types of omics features). Additionally, 2-year, 3-year and 5-year Uno's concordance statistics increased from 0.7329, 0.7043, and 0.7002 (clinical features only) to 0.7639, 0.7474 and 0.7597 (clinical features and three types of omics features), respectively.

CONCLUSION

In conclusion, this study successfully combined clinical and multi-omics data for better prediction of colon cancer prognosis.

A Risk Stratification Model for Predicting Overall Survival and Surgical Benefit in Triple-Negative Breast Cancer Patients With de novo Distant Metastasis

Background and Aims: This research aimed to construct a novel model for predicting overall survival (OS) and surgical benefit in triple-negative breast cancer (TNBC) patients with de novo distant metastasis. Methods: We collected data from the Surveillance, Epidemiology, and End Results (SEER) database for TNBC patients with distant metastasis between 2010 and 2016. Patients were excluded if the data regarding metastatic status, follow-up time, or clinicopathological information were incomplete. Univariate and multivariate analyses were applied to identify significant prognostic parameters. By integrating these variables, a predictive nomogram and risk stratification model were constructed and assessed with C-indexes and calibration curves. Results: A total of 1,737 patients were finally identified. Patients enrolled from 2010 to 2014 were randomly assigned to two cohorts, 918 patients in the training cohort and 306 patients in the validation cohort I, and 513 patients enrolled from 2015 to 2016 were assigned to validation cohort II. Seven clinicopathological factors were included as prognostic variables in the nomogram: age, marital status, T stage, bone metastasis, brain metastasis, liver metastasis, and lung metastasis. The C-indexes were 0.72 [95% confidence interval [CI] 0.68-0.76] in the training cohort, 0.71 (95% CI 0.68-0.74) in validation cohort I and 0.71 (95% CI 0.67-0.75) in validation cohort II. Calibration plots indicated that the nomogram-based predictive outcome had good consistency with the recoded prognosis. A risk stratification model was further generated to accurately differentiate patients into three prognostic groups. In all cohorts, the median overall survival time in the low-, intermediate- and high-risk groups was 17.0 months (95% CI 15.6-18.4), 11.0 months (95% CI 10.0-12.0), and 6.0 months (95% CI 4.7-7.3), respectively. Locoregional surgery improved prognosis in both the low-risk [hazard ratio [HR] 0.49, 95% CI 0.41-0.60, P < 0.0001] and intermediate-risk groups (HR 0.55, 95% CI 0.46-0.67, P < 0.0001), but not in high-risk group (HR 0.73, 95% CI 0.52-1.03, P = 0.068). All stratified groups could prognostically benefit from chemotherapy (low-risk group: HR 0.50, 95% CI 0.35-0.69, P < 0.0001; intermediate-risk group: HR 0.34, 95% CI 0.26-0.44, P < 0.0001; and high-risk group: HR 0.16, 95% CI 0.10-0.25, P < 0.0001). Conclusion: A predictive nomogram and risk stratification model were constructed to assess prognosis in TNBC patients with de novo distant metastasis; these methods may provide additional introspection, integration and improvement for therapeutic decisions and further studies.

Prognostic Nomogram and Predictive Factors in Refractory or Relapsed Diffuse Large B-Cell Lymphoma Patients Failing Front-Line R-CHOP Regimens

Background: The clinical course of relapsed or refractory (r/r) diffuse large B-cell lymphoma (DLBCL) is variable, with limited efficacy data of second-line treatment in a post-rituximab real-world context. Hence, we explored the predictors and constructed a nomogram for risk stratification in this population.

Patients and methods: Among 296 r/r DLBCL patients pretreated with R-CHOP (rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone) at the Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College between 2006 and 2017, 231 were included for nomogram construction. After randomization, we constructed the prognostic nomogram in the primary cohort (n=161) based on a multivariate analysis and confirmed it in the validation cohort (n=70). Additionally, we explored predictive factors for second-line therapy using a ordinal regression analysis.

Results: Four independent prognostic factors including rituximab in the second-line setting, initial Eastern Cooperative Oncology Group (ECOG) performance status (PS), response to front-line treatment, and invasion on progression/recurrence were used to construct the nomogram. The nomogram had a C index of 0.70 with AUC values of 0.73 and 0.71 for the primary and validation cohorts, respectively. Subsequently, three risk groups (low, intermediate, and high) were determined with median overall survival (OS) of 38.0, 25.0, and 7.0 months, respectively. Additionally, we conducted a multivariate ordinal regression analysis and identified prior response to front-line treatment (odds ratio=4.50, 95% CI: 1.84-11.27, p=0.001) and bulky disease at diagnosis (odds ratio=0.36, 95% CI: 0.182-0.702, p=0.003) were two independent factors for second-line treatment efficacy.

Conclusions: The established predictors for treatment efficacy and the novel nomogram for survival in r/r DLBCL patients could potentially be applied for risk stratification and treatment guidance in the post-rituximab era.

Controversies of radiation therapy omission in elderly women with early stage invasive breast cancer

Early stage invasive breast cancer is a disease that is prevalent in the elderly population. Data regarding radiation omission for elderly population is based on patients’ age. Given the increased life expectancy, data on individualizing treatment decisions based on multiple tumor and patient related factors other than age only is emerging. This review aims to analyze published data to provide clinicians with a general oversight on approaching this question.

"Radiotherapy for older women (ROW)": A risk calculator for women with early-stage breast cancer

OBJECTIVE

Among older adult women with early-stage breast cancer who undergo lumpectomy, the benefits of radiotherapy vary according to tumor characteristics and life expectancy. We aimed to develop a risk calculator to predict individualized probability of long-term survival and local recurrence, accounting for these factors.

METHODS

We developed a simulation model to estimate an individual patient's risk of local recurrence and all-cause mortality according to age, comorbidities, functional status, tumor characteristics, and radiotherapy status. We integrated two existing prediction models, the Early Breast Cancer Trialist's Collaborative Group prediction model for breast cancer specific outcomes and ePrognosis for life expectancy. An online risk calculator "Radiotherapy for Older Women (ROW)" was developed through an iterative multi-stage process, that included individual consultation and group meetings with an advisory committee (AC) comprised of patients, advocates, clinicians, and researchers.

RESULTS

We developed the tool over 40 months and had 15 group meetings. The risk calculator developed as a simulation model with 16 factors (5 tumor-related, 3 demographic, 4 comorbidities, and 4 functional statuses). Across 56,700 simulated scenarios, the benefit of RT in terms of absolute 10-year local recurrence reduction, ranged from 0% to 34%, depending on individual characteristics. Based on feedback from the AC, overall survival and local recurrence were chosen as the output for ROW, with these outcomes displayed numerically (percentages and natural frequencies) and graphically (pictographs).

CONCLUSIONS

This tool "ROW" could facilitate shared decision making regarding receipt of radiotherapy for older women with early breast cancer. Additional studies to examine usability testing are underway.

Accelerated partial breast irradiation in the elderly: 5-Year results of the single fraction elderly breast irradiation (SiFEBI) phase I/II trial

PURPOSE

To evaluate the clinical outcomes of a very-accelerated partial breast irradiation (vAPBI) in the elderly based on a single fraction of multicatheter interstitial high-dose rate brachytherapy (MIB). Mature results with a median follow-up of 5 years.

METHODS AND MATERIALS

From November 2012 to September 2014, 26 patients (pts) (≥70) with early breast cancer were enrolled in a prospective phase II trial (NCT01727011). After lumpectomy, intraoperative catheter implant was performed for postoperative APBI (single fraction 16 Gy). Surveillance was performed twice a year after APBI. Oncologic outcome (local [LRFS], metastasis-free survival, cancer-specific survival, and overall survival [OS]) as well as late toxicity and cosmetic outcome were investigated.

RESULTS

Median age was 77 years [69-89]. After a median follow-up of 63 months [60-68], 5-year LRFS, metastasis-free survival, cancer-specific survival, and overall survival rates were 100%, 95.5%, 100%, and 88.5%, respectively. Late toxicity was observed in 5 pts (19.2%) with a total of five events: 3 pts G1 (60%); and 2 pts G2 (40%). The observed late side effects were breast pain in 1 pt (G2 cytosteatonecrosis with occasional acetaminophen consumption), hypopigmentation (puncture site) in 2 pts (G1) and breast fibrosis in 2 pts (G1: 1 pt; G2: 1 pt). Cosmetic evaluation was excellent for 21 pts (81%) and good for 2 pts (19%).

CONCLUSION

For elderly with early breast cancer, a vAPBI using a single fraction of postoperative MIB (16 Gy) provides excellent oncologic results, mainly in terms of local control and cancer death. Late toxicity and cosmetic profile are acceptable.

Nomogram for predicting the survival of patients with malignant melanoma: A population analysis

The aim of the current study was to develop and validate a nomogram based on a large population to estimate the 3- and 5-year survival rates of patients with malignant melanoma (MM). Patients were selected from the Surveillance, Epidemiology and End Results database and randomly divided into the training and validation cohorts. A nomogram was developed, and was used to assess the accuracy of the model. Independent prognostic factors associated with overall survival (OS) rate were identified through multivariate analysis, and were included in the internal validation of the nomogram. The nomogram provided high C-indexes for the training cohort [area under the time-dependent receiver operating characteristic curve (AUC) of 0.877 for 3-year OS rate and 0.872 for 5-year OS rate] and the validation cohort (AUC of 0.880 for 3-year OS rate and 0.874 for 5-year OS rate), indicating that the model had good discrimination ability. Calibration plots showed that the predicted 3- and 5-year OS rates probabilities for the training and validation groups were almost identical to the actual observations. The 3- and 5-year decision curves indicated net benefits for both the training and validation cohorts. The nomogram may aid clinicians to provide more accurate prognosis prediction in patient consultations and more personalized postoperative management plans.

Prognostic Nomogram for Overall Survival in Patients with Diffuse Large B-Cell Lymphoma

PURPOSE

This study aimed to develop a prognostic nomogram in diffuse large B-cell lymphoma (DLBCL) and compare it with traditional prognostic systems.

MATERIALS AND METHODS

We included 1,070 consecutive and nonselected patients with DLBCL in the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, between 2006 and 2012. A nomogram based on the Cox proportional hazards model was developed.

RESULTS

The entire group were divided into the primary (n = 748) and validation (n = 322) cohorts. The 5-year overall survival (OS) rate was 64.1% for the entire group. Based on a multivariate analysis of the primary cohort, seven independent prognostic factors including age, Ann Arbor stage, Eastern Cooperative Oncology Group performance status score, lactate dehydrogenase, β2-microglobulin, CD5 expression, and Ki-67 index were identified and entered the nomogram. The calibration curve showed the optimal agreement between nomogram prediction and actual observation. In addition, the concordance index (C-index) of the nomogram for OS prediction was 0.77 (95% confidence interval [CI], 0.73-0.81) in the primary cohort and 0.76 (95% CI, 0.70-0.81) in the validation, superior to that of the international prognostic index (IPI), revised IPI (R-IPI), and National Comprehensive Cancer Network (NCCN)-IPI (range, 0.69-0.74, p<.0001). Moreover, in patients receiving rituximab plus CHOP (R-CHOP) or R-CHOP-like regimens, compared with IPI (C-index, 0.73; 95% CI, 0.69-0.77), R-IPI (C-index, 0.70; 95% CI, 0.66-0.74), or NCCN-IPI (C-index, 0.71; 95% CI, 0.66-0.75), the DLBCL-specific nomogram showed a better discrimination capability (p < .0001).

CONCLUSIONS

The proposed nomogram provided an accurate estimate of survival of patients with DLBCL, especially for those receiving R-CHOP or R-CHOP-like regimens, allowing clinicians to optimized treatment plan based on individualized risk prediction.

IMPLICATIONS FOR PRACTICE

A diffuse large B-cell lymphoma (DLBCL)-specific prognostic nomogram was developed based on Chinese patients with DLBCL. As a tertiary hospital, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences is the number 1 ranked cancer center in China, with more than 800,000 outpatients in 2018. Patients included in this study were nonselected and came from 29 different provinces, municipalities, and autonomous regions in China. Thus, the data is believed to be representative to an extent.

Semiparametric Model for Bivariate Survival Data Subject to Biased Sampling

To better understand the relationship between patient characteristics and their residual survival after an intermediate event such as the local cancer recurrence, it is of interest to identify patients with the intermediate event and then analyze their residual survival data. One challenge in analyzing such data is that the observed residual survival times tend to be longer than those in the target population, since patients who die before experiencing the intermediate event are excluded from the identified cohort. We propose to jointly model the ordered bivariate survival data using a copula model and appropriately adjusting for the sampling bias. We develop an estimating procedure to simultaneously estimate the parameters for the marginal survival functions and the association parameter in the copula model, and use a two-stage expectation-maximization algorithm. Using empirical process theory, we prove that the estimators have strong consistency and asymptotic normality. We conduct simulations studies to evaluate the finite sample performance of the proposed method. We apply the proposed method to two cohort studies to evaluate the association between patient characteristics and residual survival.

A nomogram to predict prognosis in Ewing sarcoma of bone

Objective

This study was designed to develop a nomogram for assessing the survival of patients with Ewing sarcoma (ES).

Methods

Data from patients diagnosed with ES between 2004 and 2013 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Based on patient registration, the primary cohort was divided into a training set (n = 479, data from 17 cancer registries) and a validation set (n = 137, data from 1 cancer registry). Then, the prognostic effects of variables were analyzed using Kaplan–Meier method and Cox proportional hazard model. Moreover, nomograms were established for estimating 3- and 5-year overall survival (OS) and cancer-special survival (CSS) based on Cox regression model. Last, nomogram was validated by training set and validation set.

Results

According to the multivariate analysis of training set, nomogram which combined age, race, stage, tumor site, tumor size and chemotherapy was identified. The internal bootstrap resampling approach suggested the nomogram had sufficient discriminatory power with the C-index of OS: 0.754 (95% CI, 0.705–0.802) and CSS: 0.759 (95% CI, 0.700–0.800). The calibration plots also demonstrated good consistence between the prediction and the observation.

Conclusion

Our nomogram is a reliable and powerful tool for distinguishing and predicting the survival of ES patients, thus helping to better select medical examinations and optimize treatment options in collaboration with medical oncologists and surgeons.

Prognostic nomograms for predicting overall and cancer-specific survival of high-grade osteosarcoma patients

Aim

The present study aimed to develop nomograms estimating survival for patients with high-grade osteosarcoma.

Methods

1990 patients with high-grade osteosarcoma between 1994 and 2013 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Data from 12 cancer registries (n = 1460) were used to conduct multivariate Cox analysis to identify independent prognostic factors. Nomograms which estimate 3- and 5-year overall survival (OS) and cancer-specific survival (CSS) were constructed. The nomograms were internally validated for calibration and were also externally validated with an independent patient cohort from 1 cancer registry (n = 530).

Results

Age, primary site, tumor size, use of surgery, and extent of disease were found to be independently associated with OS and CSS (p < 0.05). The nomograms estimating 3- and 5-year OS and CSS were developed based on these prognostic factors. The concordance indices were high in internal validation (0.726 for OS and 0.731 for CSS) and external validation (0.716 for OS and 0.724 for CSS). Internal and external calibration plots demonstrated a good agreement between nomogram prediction and actual observation.

Conclusions

We constructed nomograms that accurately predict OS and CSS of high-grade osteosarcoma patients. The nomograms can be used for counseling patients and establishing risk stratification.

A Prognostic Nomogram for Disease-Specific Survival in Patients with Pancreatic Ductal Adenocarcinoma of the Head of the Pancreas Following Pancreaticoduodenectomy

BACKGROUND This study developed and validated a nomogram to predict patient prognosis for pancreatic ductal adenocarcinoma (PDAC) of the head of the pancreas following pancreaticoduodenectomy. MATERIAL AND METHODS Retrospective data were obtained from 4,383 patients with PDAC of the head of the pancreas who underwent pancreaticoduodenectomy between 2004-2013 from 11 Registries Research Data of the Surveillance, Epidemiology,and End Results (SEER) database. Cox proportional hazards model was used to identify independent risk factors. The predictive accuracy of the nomogram was determined by the concordance index (C-index) and calibration curve. The results were externally validated by comparison with data from 1,743 patients from 7 other Registries Research Data. RESULTS Of the 4,383 patients in the training dataset, median disease-specific survival (DSS) was 17.0 months (range, 1.0-131 months), and postoperative 1-year, 3-year, and 5-year DSS rates were 70.3%, 26.1%, and 16.8%, respectively. Multivariate analysis showed that patient sex, age, tumor grade, regional lymph nodes examined, positive regional lymph nodes, tumor size, extent of local invasion, and tumor metastases were independent risk factors for DSS. The C-index of the internal validation dataset for prediction of DSS was 0.64 (95% CI, 0.63-0.65), which was superior to the American Joint Committee on Cancer (AJCC) staging, 0.57 (95% CI, 0.56-0.58) (P<0.001). The 5-year DSS rates and median DSS time for patients in the low-risk group were significantly greater compared with high-risk group (P<0.001). CONCLUSIONS A validated prognostic disease-specific nomogram for patient survival in PDAC of the head of the pancreas following pancreaticoduodenectomy was developed.

Nomograms for predicting disease progression in patients of Stage I non-small cell lung cancer treated with stereotactic body radiotherapy

Objective

Non-local progression is a major concern in non-small cell lung cancer (NSCLC) treated with stereotactic body radiotherapy (SBRT). Herein we aimed to create a pre-treatment prognostic nomogram for patients with Stage I NSCLC receiving SBRT.

Methods

We retrospectively studied 182 eligible patients. Patients were randomly divided into a model (70%) group and a validation (30%) group. In the model group, thirteen parameters consisting of patient, treatment, and tumor factors were studied and multivariate Cox proportional hazards regression was performed to identify independent predictors for survival outcome, based on which we developed clinical nomogram. The nomogram was externally validated in the validation group.

Results

Multivariate analysis showed that tumor size (P = 0.011) was the only factor correlated with 2-year overall survival, whereas 2-year locoregional control (LRC) was significantly related to tumor size (P = 0.024) and the maximum standardized uptake value (SUVmax) (P = 0.044), so does 2-year progression-free survival (PFS) (tumor size: P = 0.026; SUVmax: P = 0.038). Nomogram for 2-year LRC and 2-year PFS were created based on aforementioned results. The C-indexes for the nomograms to predict 2-year LRC and PFS were 0.816 and 0.804, respectively, in model group, and were 0.729 and 0.731, respectively, in the validation group. Calibration plots also showed that the model performed well.

Conclusions

Tumor of larger size and higher SUVmax predisposed patients to early onset of locoregional and distant progression. The nomogram developed in our study would be helpful in clinical decision-making and selection of patients who may benefit from more rigorous follow-up and aggressive systemic treatment plan.

A Novel Risk prediction Model for Patients with Combined Hepatocellular-Cholangiocarcinoma

Backgrounds: Regarding the difficulty of CHC diagnosis and potential adverse outcomes or misuse of clinical therapies, an increasing number of patients have undergone liver transplantation, transcatheter arterial chemoembolization (TACE) or other treatments.

Objective: To construct a convenient and reliable risk prediction model for identifying high-risk individuals with combined hepatocellular-cholangiocarcinoma (CHC).

Methods: 3369 patients who underwent surgical resection for liver cancer at Zhongshan Hospital were enrolled in this study. The epidemiological and clinical characteristics of the patients were collected at the time of tumor diagnosis. Variables (P <0.25 in the univariate analyses) were evaluated using backward stepwise method. A receiver operating characteristic (ROC) curve was used to assess model discrimination. Calibration was performed using the Hosmer-Lemeshow test and a calibration curve. Internal validation was performed using a bootstrapping approach.

Results: Among the entire study population, 250 patients (7.42%) were pathologically defined with CHC. Age, HBcAb, red blood cells (RBC), blood urea nitrogen (BUN), AFP, CEA and portal vein tumor thrombus (PVTT) were included in the final risk prediction model (area under the curve, 0.69; 95% confidence interval, 0.51-0.77). Bootstrapping validation presented negligible optimism. When the risk threshold of the prediction model was set at 20%, 2.73% of the patients diagnosed with liver cancer would be diagnosed definitely, which could identify CHC patients with 12.40% sensitivity, 98.04% specificity, and a positive predictive value of 33.70%.

Conclusions: Herein, the study established a risk prediction model which incorporates the clinical risk predictors and CT/MRI-presented PVTT status that could be adopted to facilitate the diagnosis of CHC patients preoperatively.

Nomogram to predict the benefit of additional induction chemotherapy to concurrent chemoradiotherapy in locoregionally advanced nasopharyngeal carcinoma: Analysis of a multicenter, phase III randomized trial

BACKGROUND AND PURPOSE

Recent clinical trials and network meta-analysis have suggested that the addition of induction chemotherapy (IC) to concurrent chemoradiotherapy (CCRT) could improve survival in locoregionally advanced NPC (LANPC). We aimed to develop a nomogram to estimate the benefit of IC for individual patients based on the data from a multicenter, randomized, phase III trial (NCT01245959) comparing IC plus CCRT with CCRT alone.

PARTICIPANTS AND METHODS

This study analyzed all 480 patients enrolled in the original trial. A nomogram was developed to predict 3-year failure-free survival (FFS) with or without IC.

RESULTS

With a median follow-up of 45 months, the 3-year FFS rates were 80.3% and 72.4% in the IC plus CCRT group and CCRT group, respectively (P = 0.034). In multivariate analysis, T category, N category and treatment group were predictive of FFS and were incorporated into the nomogram. Gender was also included due to its clinical importance. This nomogram predicted that the magnitude of benefit from IC could vary significantly.

CONCLUSION

We developed a convenient nomogram to estimate the benefit of IC for individual patients with LANPC. This tool can serve as a catalyst of individual treatment discussions and facilitator of informed decision-making.

External radiotherapy for breast cancer in the elderly

BACKGROUND

Breast cancer is the most common malignancy amongst elderly women and the main cause of mortality. A specific management for elderly woman is not clear because clinical trials are usually not customized for this subset of patients.

AIMS

The aim of this paper is to provide an overview of the available information on the main issues in the field of breast cancer radiotherapy in the elderly population.

MATERIALS AND METHODS

Authors discuss on different radiation treatments for breast cancer in the elderly, based on the data of the literature with a focus on new strategy: hypo-fractionation, accelerated partial breast irradiation, and the utility of a dose boost.

DISCUSSION

The treatment of breast cancer is not standardized in the elderly. The optimal management in this population often requires complex multidisciplinary supportive care due to multiple comorbidities to optimize their cancer care.

CONCLUSIONS

New options such as APBI or HyRT regimens should be taken into consideration and offered as a breach of duty to the elderly population. Furthermore, they should be extensively investigated through randomized clinical trials.

Benefit of adjuvant radiotherapy after breast-conserving therapy among elderly women with T1-T2N0 estrogen receptor-negative breast cancer

BACKGROUND

The purpose of the current study was to evaluate the impact of radiotherapy (RT) among women aged ≥ 70 years with T1-2N0 estrogen receptor (ER)-negative breast cancer using Surveillance, Epidemiology, and End Results (SEER)-Medicare-linked data.

METHODS

The study included 3432 women, 2850 of whom received and 582 of whom did not receive RT after breast-conserving surgery. Outcomes were estimated by the cumulative incidence method and compared with the Gray test. The Fine and Gray subdistribution hazard regression models were used to assess the impact of RT and other variables.

RESULTS

Women who received RT were more commonly aged <75 years (42% vs 16%), had T1 tumors (78% vs 65%), ductal carcinoma histology (91% vs 88%), a Charlson-Deyo Comorbidity Index of 0 (41% vs 25%), and had received chemotherapy (29% vs 12%). The 5-year cumulative incidence of mastectomy and breast cancer-specific death for patients who received versus those did not receive adjuvant RT was 4.9% and 8.3% versus 10.8% and 24.1%, respectively (P<.001). On multivariable analysis, the omission of RT was found to be an independent predictor of an increased risk of mastectomy (hazard ratio, 2.33; 95% confidence interval, 1.56-3.49). Among women aged ≥ 80 years or with T1N0 tumors, the mastectomy incidence with or without receipt of RT was 3.4% vs. 6.9%, and 5.3% vs 7.7%, respectively.

CONCLUSIONS

The use of adjuvant RT after breast-conserving surgery in older women with T1-2N0 estrogen receptor-negative breast cancer is associated with a reduced incidence of future mastectomy and breast cancer death. The magnitude of benefit may be small for women aged ≥80 years or those with T1 tumors. Cancer 2016;122:3059-3068. © 2016 American Cancer Society.

Treatment failure prediction for head-and-neck cancer radiation therapy

PURPOSE

Treatment outcome prediction is an important emerging topic in oncologic care. To support radiation oncologists on their decisions, with individualized, tailored treatment regimens increasingly becoming the standard of care, accurate tools to predict tumour response to treatment are needed. The goal of this work is to identify the most determinant factor(s) for treatment response aiming to develop prediction models that robustly estimate tumour response to radiation therapy in patients with head-and-neck cancer.

PATIENTS AND METHODS

A population-based cohort study was performed on 92 patients with head-and-neck cancer treated with radiation from 2007 until 2014 at the Portuguese Institute of Oncology of Coimbra (IPOCFG). Correlation analysis and multivariate binary logistic regression analysis were conducted in order to explore the predictive power of the considered predictors. Performance of the models is expressed as the area under the curve (AUC) of the receiver operating characteristics (ROC) curve. A nomogram to predict treatment failure was developed.

RESULTS

Significant prognostic factors for treatment failure, after multivariate regression, were older age, non-concomitant radiation therapy and larger primary tumour volume. A regression model with these predictors revealed an AUC of .78 for an independent data set.

CONCLUSION

For patients with head-and-neck cancer treated with definitive radiation, we have developed a prediction nomogram based on models that presented good discriminative ability in making predictions of tumour response to treatment. The probability of treatment failure is higher for older patients with larger tumours treated with non-concomitant radiation.

Clinical Nomogram for Predicting Survival of Esophageal Cancer Patients after Esophagectomy

The aim of this study was to construct an effective clinical nomogram for predicting the survival of esophageal cancer patients after esophagectomy. We identified esophageal cancer patients (n = 4,281) who underwent esophagectomy between 1988 and 2007 from the Surveillance, Epidemiology, and End Results (SEER) 18 registries database. Clinically significant parameters for survival were used to construct a nomogram based on Cox regression analyses. The model was validated using bootstrap resampling and a Chinese cohort (n = 145). A total of 4,109 patients from the SEER database were included for analysis. The multivariate analyses showed that the factors of age, race, histology, tumor site, tumor size, grade and depth of invasion, and the numbers of metastases and retrieved nodes were independent prognostic factors. All of these factors were selected into the nomogram. The nomogram showed a clear prognostic superiority over the seventh AJCC-TNM classification (C-index: SEER cohort, 0.716 vs 0.693, respectively; P < 0.01; Chinese cohort, 0.699 vs 0.680, respectively; P < 0.01). Calibration of the nomogram predicted the probabilities of 3- and 5-year survival, which corresponded closely with the actual survival rates. This novel prognostic model may improve clinicians’ abilities to predict individualized survival and to make treatment recommendations.

Older age impacts radiotherapy-related outcomes in soft tissue sarcoma

BACKGROUND

Radiation therapy (RT) is a standard component in the multimodality management of localized soft tissue sarcoma (STS). Increasing studies are focusing on biological modifiers that may influence the host's response to RT, including immunologic mechanisms known to change with the aging process. We hypothesized that the effects of RT would be influenced by age, contributing to differences in treatment outcome.

METHODS

Using Surveillance, Epidemiology, and End Results (1990-2011), we identified 30,898 adult patients (>18 y) with nonmetastatic STS undergoing initial surgery. We compared patient demographics, tumor characteristics, and treatments by age. Multivariable analyses were used to analyze overall survival (OS) and disease-specific survival (DSS). Hazard ratios (HRs) were calculated based on multivariable Cox proportional hazards models.

RESULTS

Mean age at diagnosis was 56.6 ± 16.8 y, and 33.6% of patients were ≥65 y. Of the total, 52.1% of patients were male and 67% were white; 59.9% of patients underwent surgery alone, 33.3% received adjuvant RT, and 6.8% neoadjuvant RT. On multivariable analysis, age, sex, year of diagnosis, histology, grade, size, marital status, and RT predicted OS, whereas age, year of diagnosis, ethnicity, histology, site, grade, RT, size, and marital status predicted DSS. In all patients, RT was associated with improved OS and DSS compared to surgery alone (median OS 136 ± 13 mo with RT versus 118 ± 9 mo without RT and 5-y OS 63.2 ± 1.4% with RT versus 60.5 ± 1.2% without, P < 0.01). Patients ≥65 y derived greater improvements in OS and DSS compared with patients <65 y. These benefits were most notable after neoadjuvant RT with patients ≥65 y having significantly better OS (HR = 0.63; 95% confidence interval = 0.53-0.75), whereas patients <65 y did not (HR = 0.96; 95% confidence interval = 0.83-1.10). In addition, interaction testing demonstrated a significant modifier effect between RT and age (P < 0.05).

CONCLUSIONS

RT is associated with improved survival in patients with STS undergoing surgical treatment, but improvements in oncologic outcome with RT were greatest among older patients. Further studies into the mechanism of these age-related effects are needed.

PREOP-Gallstones: A Prognostic Nomogram for the Management of Symptomatic Cholelithiasis in Older Patients

OBJECTIVE AND BACKGROUND

The decision regarding elective cholecystectomy in older patients with symptomatic cholelithiasis is complicated. We developed and validated a prognostic nomogram to guide shared decision making for these patients.

METHODS

We used Medicare claims (1996-2005) to identify the first episode of symptomatic cholelithiasis in patients older than 65 years who did not undergo hospitalization or elective cholecystectomy within 2.5 months of the episode. We described current patterns of care and modeled their risk of emergent gallstone-related hospitalization or cholecystectomy at 2 years. Model discrimination and calibration were assessed using a random split sample of patients.

RESULTS

We identified 92,436 patients who presented to the emergency department (8.3%) or physician's office (91.7%) and who were not immediately admitted. The diagnosis for the initial episode was biliary colic/dyskinesia (65.3%), acute cholecystitis (26.6%), choledocholithiasis (5.7%), or gallstone pancreatitis (2.4%). The 2-year emergent gallstone-related hospitalization rate was 11.1%, with associated in-hospital morbidity and mortality rates of 56.5% and 6.5%. Factors associated with gallstone-related acute hospitalization included male sex, increased age, fewer comorbid conditions, complicated biliary disease on initial presentation, and initial presentation to the emergency department. Our model was well calibrated and identified 51% of patients with a risk less than 10% for 2-year complications and 5.4% with a risk more than 40% (C statistic, 0.69; 95% confidence interval, 0.63-0.75).

CONCLUSIONS

Surgeons can use this prognostic nomogram to accurately provide patients with their 2-year risk of developing gallstone-related complications, allowing patients and physicians to make informed decisions in the context of their symptom severity and its impact on their quality of life.