Nomogram to predict subsequent brain metastasis in patients with metastatic breast cancer

Brain metastasis risk prediction model in females with hormone receptor-positive breast cancer

BACKGROUND

Breast cancer is a leading cause of cancer-related deaths in females, and the hormone receptor-positive subtype is the most frequent. Breast cancer is a common source of brain metastases; therefore, we aimed to generate a brain metastases prediction model in females with hormone receptor-positive breast cancer.

METHODS

The primary cohort included 3,682 females with hormone receptor-positive breast cancer treated at a single center from May 2009 to May 2020. Patients were randomly divided into a training dataset (n = 2,455) and a validation dataset (n = 1,227). In the training dataset, simple logistic regression analyses were used to measure associations between variables and the diagnosis of brain metastases and to build multivariable models. The model with better calibration and discrimination capacity was tested in the validation dataset to measure its predictive performance.

RESULTS

The variables incorporated in the model included age, tumor size, axillary lymph node status, clinical stage at diagnosis, HER2 expression, Ki-67 proliferation index, and the modified Scarff-Bloom-Richardson grade. The area under the curve was 0.81 (95 % CI 0.75-0.86), p < 0.001 in the validation dataset. The study presents a guide for the clinical use of the model.

CONCLUSION

A brain metastases prediction model in females with hormone receptor-positive breast cancer helps assess the individual risk of brain metastases.

Prevention of Brain Metastases: A New Frontier

This review discusses the topic of prevention of brain metastases from the most frequent solid tumor types, i.e., lung cancer, breast cancer and melanoma. Within each tumor type, the risk of brain metastasis is related to disease status and molecular subtype (i.e., EGFR-mutant non-small cell lung cancer, HER2-positive and triple-negative breast cancer, BRAF and NRAF-mutant melanoma). Prophylactic cranial irradiation is the standard of care in patients in small cell lung cancer responsive to chemotherapy but at the price of late neurocognitive decline. More recently, several molecular agents with the capability to target molecular alterations driving tumor growth have proven as effective in the prevention of secondary relapse into the brain in clinical trials. This is the case for EGFR-mutant or ALK-rearranged non-small cell lung cancer inhibitors, tucatinib and trastuzumab-deruxtecan for HER2-positive breast cancer and BRAF inhibitors for melanoma. The need for screening with an MRI in asymptomatic patients at risk of brain metastases is emphasized.

Navigating breast cancer brain metastasis: Risk factors, prognostic indicators, and treatment perspectives

In this editorial, we comment on the article by Chen et al. We specifically focus on the risk factors, prognostic factors, and management of brain metastasis (BM) in breast cancer (BC). BC is the second most common cancer to have BM after lung cancer. Independent risk factors for BM in BC are: HER-2 positive BC, triple-negative BC, and germline BRCA mutation. Other factors associated with BM are lung metastasis, age less than 40 years, and African and American ancestry. Even though risk factors associated with BM in BC are elucidated, there is a lack of data on predictive models for BM in BC. Few studies have been made to formulate predictive models or nomograms to address this issue, where age, grade of tumor, HER-2 receptor status, and number of metastatic sites (1 vs > 1) were predictive of BM in metastatic BC. However, none have been used in clinical practice. National Comprehensive Cancer Network recommends screening of BM in advanced BC only when the patient is symptomatic or suspicious of central nervous system symptoms; routine screening for BM in BC is not recommended in the guidelines. BM decreases the quality of life and will have a significant psychological impact. Further studies are required for designing validated nomograms or predictive models for BM in BC; these models can be used in the future to develop treatment approaches to prevent BM, which improves the quality of life and overall survival.

Central nervous system metastases in breast cancer patients with germline BRCA pathogenic variants compared to non-carriers: a matched-pair analysis

BACKGROUND

Breast cancer is a common cause for central nervous system (CNS) metastasis, resulting in a significant reduction in overall survival. Germline pathogenic variants (PVs) in BRCA1/2 are the most common genetic risk factor for breast cancer, associated with poor prognostic factors. This study sought to explore the patterns and outcome of CNS metastases in breast cancer patients with germline PVs in BRCA1/2 genes.

METHODS

A retrospective cohort of 75 breast cancer patients with known BRCA1/2 mutation status, who were diagnosed with CNS metastases in 2006-2021. Histopathology, characteristics of CNS disease, treatments, and survival were compared between BRCA1/2 carriers (n = 25) and non-carriers (n = 50), using propensity score matching (1:2 ratio) to control for the possible influence of tumor receptor status (ER, PR, HER2) and patient age. Pearson chi-square or Fisher exact test and Kaplan-Meier survival curves with log-rank test were used for statistical analyses.

RESULTS

Patients with PVs in BRCA1/2 had more high-grade tumors (88% vs. 68%, P = 0.060), were younger at CNS disease diagnosis (median 46.69 vs. 55.02 years, P = 0.003) and had better ECOG performance status (ECOG PS 0 in 20% vs. 2%, P = 0.033), but without significant differences in systemic or CNS-directed treatment approaches. BRCA1/2 mutation was associated with a higher rate of temporal lobe involvement (52% vs. 26%, P = 0.026) and leptomeningeal spread (40% vs. 20%, P = 0.020). Survival after diagnosis of CNS disease was shorter (median 8.03 vs. 28.36 months, P < 0.0001), with no significant differences in time to development of CNS metastases or overall-survival.

CONCLUSION

Patients with CNS metastatic breast cancer and PVs in BRCA1/2 showed a higher rate of leptomeningeal and temporal lobe involvement, and a shorter survival with CNS disease. To the best of our knowledge, this is the first study suggesting an exclusive impact of germline BRCA1/2 mutations in CNS metastatic breast cancer.

Development and validation of a novel nomogram model to assess the risk of gastric contents in outpatients undergoing elective sedative gastrointestinal endoscopy procedures

BACKGROUND

Gastric contents may contribute to patients' aspiration during anesthesia. Ultrasound can accurately assess the risk of gastric contents in patients undergoing sedative gastrointestinal endoscopy (GIE) procedures, but its efficiency is limited. Therefore, developing an accurate and efficient model to predict gastric contents in outpatients undergoing elective sedative GIE procedures is greatly desirable.

METHODS

This study retrospectively analyzed 1501 patients undergoing sedative GIE procedures. Gastric contents were observed under direct gastroscopic vision and suctioned through the endoscope. High-risk gastric contents were defined as having solid content or liquid volume > 25 ml and pH < 2.5; otherwise, they were considered low-risk gastric contents. Univariate analysis and multivariate analysis were used to select the independent risk factors to predict high-risk gastric contents. Based on the selected independent risk factors, we assigned values to each independent risk factor and established a novel nomogram. The performance of the nomogram was verified in the testing cohort by the metrics of discrimination, calibration, and clinical usefulness. In addition, an online accessible web calculator was constructed.

RESULTS

We found BMI, cerebral infarction, cirrhosis, male, age, diabetes, and gastroesophageal reflux disease were risk factors for gastric contents. The AUROCs were 0.911 and 0.864 in the development and testing cohort, respectively. Moreover, the nomogram showed good calibration ability. Decision curve analysis and Clinical impact curve demonstrated that the predictive nomogram was clinically useful. The website of the nomogram was https://medication.shinyapps.io/dynnomapp/.

CONCLUSIONS

This study demonstrates that clinical variables can be combined with algorithmic techniques to predict gastric contents in outpatients. Nomogram was constructed from routine variables, and the web calculator had excellent clinical applicability to assess the risk of gastric contents accurately and efficiently in outpatients, assist anesthesiologists in assessment and identify the most appropriate patients for ultrasound.

A brain metastasis prediction model in women with breast cancer

BACKGROUND

Breast cancer (BC) is a leading cause of mortality and the most frequent malignancy in women, and most deaths are due to metastatic disease, particularly brain metastases (BM). Currently, no biomarker or prediction model is used to predict BM accurately. The objective was to generate a BM prediction model from variables obtained at BC diagnosis.

METHODS

A retrospective cohort of women with BC diagnosed from 2009 to 2020 at a single center was divided into a training dataset (TD) and a validation dataset (VD). The prediction model was generated in the TD, and its performance was measured in the VD using the area under the curve (AUC) and C-statistic.

RESULTS

The cohort (n = 5009) was divided into a TD (n = 3339) and a VD (n = 1670). In the TD, the model with the best performance (lowest AIC) was built with the following variables: age, estrogen receptor status, tumor size, axillary adenopathy, anatomic clinical stage, Ki-67 expression, and Scarff-Bloom-Richardson score. This model had an AUC of 0.79 (95%CI, 0.76-0.82; p < 0.0001) in the TD. The 10-fold cross-validation showed the good stability of the model. The model displayed an AUC of 0.81 (95%CI, 0.77-0.85; P < 0.0001) in the VD. Four groups, according to the risk of BM, were generated. In the low-risk group, 1.2% were diagnosed with BM (reference); in the medium-risk group, 5.0% [HR 4.01 (95%CI, 1.8 - 8.8); P < 0.0001); in the high-risk group, 8.5% [HR 8.33 (95%CI, 4.1-17.1); P < 0.0001]; and in the very high-risk group, 23.7% [HR 29.72 (95%CI, 14.9 - 59.1); P < 0.0001].

CONCLUSION

This prediction model built with clinical and pathological variables at BC diagnosis demonstrated robust performance in determining the individual risk of BM among patients with BC, but external validation in different cohorts is needed.

Development and external validation of a prediction model for brain metastases in patients with metastatic breast cancer

BACKGROUND

Breast cancer patients with brain metastasis (BM) have a poor prognosis. This study aims to identify the risk factors of BM in patients with metastatic breast cancer (MBC) and establish a competing risk model for predicting the risk of brain metastases at different time points along the course of disease.

METHODS

Patients with MBC admitted to the breast disease center of Peking University First Hospital from 2008 to 2019 were selected and retrospectively analyzed to establish a risk prediction model for brain metastases. Patients with MBC admitted to eight breast disease centers from 2015 to 2017 were selected for external validation of the competing risk model. The competing risk approach was used to estimate cumulative incidence. Univariate Fine-Gray competing risk regression, optimal subset regression, and LASSO Cox regression were used to screen potential predictors of brain metastases. Based on the results, a competing risk model for predicting brain metastases was established. The discrimination of the model was evaluated using AUC, Brier score, and C-index. The calibration was evaluated by the calibration curves. The model was assessed for clinical utility by decision curve analysis (DCA), as well as by comparing the cumulative incidence of brain metastases between groups with different predicted risks.

RESULTS

From 2008 to 2019, a total of 327 patients with MBC in the breast disease center of Peking University First Hospital were admitted into the training set for this study. Among them, 74 (22.6%) patients developed brain metastases. From 2015 to 2017, a total of 160 patients with MBC in eight breast disease centers were admitted into the validation set for this study. Among them, 26 (16.3%) patients developed brain metastases. BMI, age, histological type, breast cancer subtype, and extracranial metastasis pattern were included in the final competing risk model for BM. The C-index of the prediction model in the validation set was 0.695, and the AUCs for predicting the risk of brain metastases within 1, 3, and 5 years were 0.674, 0.670, and 0.729, respectively. Time-dependent DCA curves demonstrated a net benefit of the prediction model with thresholds of 9-26% and 13-40% when predicting the risk of brain metastases at 1 and 3 years, respectively. Significant differences were observed in the cumulative incidence of brain metastases between groups with different predicted risks (P < 0.05 by Gray's test).

CONCLUSIONS

In this study, a competing risk model for BM was innovatively established, with the multicenter data being used as an independent external validation set to confirm the predictive efficiency and universality of the model. The C-index, calibration curves, and DCA of the prediction model indicated good discrimination, calibration, and clinical utility, respectively. Considering the high risk of death in patients with metastatic breast cancer, the competing risk model of this study is more accurate in predicting the risk of brain metastases compared with the traditional Logistic and Cox regression models.

Prognosis of breast cancer patients with brain metastasis treated with radiotherapy

BACKGROUND

Life expectancy for patients diagnosed with metastatic breast cancer (BC) has improved in recent years, especially due to better systemic treatment. This has led to an increased incidence of brain metastases (BM), and BC is now the leading cause of BM in women. Treatment of BM primarily consists of surgery and/or radiotherapy. We aimed to investigate survival time and prognostic factors for BC patients treated with radiotherapy for BM.

MATERIAL & METHODS

During the period 1st of January 2015 to 1st of June 2020, 144 consecutive BC patients treated for BM from one centre were retrospectively analyzed. The patients were either diagnosed with BM as the first metastatic lesion, or developed BM during palliative therapy for distant non-brain metastasis. The study was approved by the Central Denmark Region.

RESULTS

Median age at BM diagnosis was 66 years, and 90% of the patients already had extracranial metastatic disease at BM diagnosis. Median overall survival after diagnosis of BM was 6.1 months. Short survival was observed for patients with poor performance status, leptomeningeal metastasis or more than three solid BM. Several of these factors were overrepresented in patients with estrogen receptor-positive (ER+) tumours who had poorer survival than patients with different receptor status.

CONCLUSION

The number of metastatic BC patients developing BM is high, and survival following local treatment remains poor. Several prognostic factors appear to influence survival after radiotherapy. Treatment of BC patients with BM should be individualized according to performance status, leptomeningeal disease, number of BM, and receptor status of the disease.

Risk of metastasis in BRCA2 carriers diagnosed with triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) is the neoplasia most associated with BRCA1 germline pathogenic variants (PV) and is more likely to develop metastases than the other breast cancer (BC) subtypes, mainly in the lungs and the central nervous system (CNS). Recently, BRCA2 carriers were shown to have a higher risk for developing CNS metastases. However, the patterns of recurrence and metastases of BRCA2 carriers with TNBC are unknown.

METHODS

TNBC patient data attending the A.C. Camargo Cancer Center, from 1998 through 2020, were verified either by medical records or by BRCA1/2 genetic testing carried out. Multivariable logistic regression models were fit to the data to assess the independent factors for bone and CNS metastases. Adjustment was done using all independent variables with p < 0.2 in the univariable Cox model to describe the relationship between the independent variables until time of death.

RESULTS

A total of 388 TNBC patients were evaluated. We identified PV in BRCA1/2 genes in 21% (82/388), being 17.7% (69/388) in BRCA1 and only 3.3% (13/388) in BRCA2. A total of 120 patients (31%) developed distant metastases. Bone or CNS metastases were observed in 40% and 60% of BRCA2 PV carriers (p = 0.155), respectively. The BRCA2 carriers tended to have a higher likelihood of developing bone metastases (OR, 4.06; 95% CI, 0.82-20.01; p = 0.085), when compared to BRCA1 carriers (OR, 0.6; 95% CI, 0.12-2.87; p = 0.528). BRCA2 carriers had an OR of 1.75 (95% CI, 0.33-9.14; p = 0.503) for CNS metastasis development, while BRCA1 carriers had an OR of 0.72 (95% CI, 0.23-2.23; p = 0.574).

CONCLUSIONS

Patients with TNBC and PV in the BRCA2 gene had higher frequencies of secondary bone involvement and CNS in the course of the disease. However, the BRCA2 PV did not represent an independent outcome predictor of metastases and overall survival. Efforts to increase the number of BRCA2 carriers among TNBC patients are crucial for determining their risk of developing bone and CNS metastases compared to BRCA2 noncarriers.

A Novel Hematological Inflammation-Nutrition Score (HINS) and Its Related Nomogram Model to Predict Survival Outcome in Advanced Gastric Cancer Patients Receiving First-Line Palliative Chemotherapy

Purpose

This study aims to construct a novel hematological inflammation-nutrition score (HINS) and investigate its prognostic value in patients with advanced gastric cancer (AGC). We investigated the risk stratification performance of HINS and developed a HINS-based nomogram model to predict overall survival by combining traditional predictors.

Patients and Methods

We conducted a retrospective study on 812 AGC patients who received first-line platinum- or fluoropyrimidine-containing chemotherapy at The First Affiliated Hospital of Zhengzhou University Hospital between 2014 and 2019. Patients were randomly divided into a training cohort (N=609) and a validation cohort (N=203). HINS (0-2) was constructed based on a pre-chemotherapy systemic immune-inflammation index (SII) and albumin (ALB). Prognostic factors were screened by univariate and multivariate COX proportional regression models. Significant factors were used to construct a nomogram model. Internal validation was performed by calibration curves, time-dependent receiver operating characteristics (ROC) curves, and decision curve analysis (DCA), evaluating its prediction consistency, discrimination ability, and clinical net benefit.

Results

HINS was constructed based on SII and ALB. HINS showed a better stratification ability than JCOG prognostic index, with significant differences between groups. Multivariate analysis showed that ECOG ≥1 (HR: 1.379; P=0.005), Stage IV (HR: 1.581; P <0.001), diffuse-type histology (HR: 1.586; P <0.001), number of metastases ≥2 (HR: 1.274; P=0.038), without prior gastrectomy (HR: 1.830; P <0.001), ALP ≥ULN (HR: 1.335; P=0.034), HINS (P <0.001) were independent factors of OS. We successfully established a HINS-based nomogram model that showed a strong discriminative ability, accuracy, and clinical utility in training and validation cohorts.

Conclusion

HINS shows a superior risk stratification ability, which might be a potential prognostic biomarker for AGC patients receiving palliative first-line palliative chemotherapy. The HINS-based nomogram model is a convenient and efficient tool for managing prognosis and follow-up treatments.

An Immunohistochemical Study of Breast Cancer Brain Metastases: The Role of CD44 and AKT in the Prognosis

Breast cancer is a major health burden, and up to one-third of patients with breast cancer develop brain metastases, which are linked to a very poor prognosis. Few biomarkers are available to predict the prognosis of patients with metastases. Assessment by immunohistochemistry may be used as a tool to predict the behavior of these tumors. A retrospective transversal study including 114 patients (diagnosed between 2000 and 2016) with breast cancer brain metastases was carried out using archival biological material from 114 patients with breast cancer brain metastases. Expression of CD44, HER2, ER, PR, CA9, PDL-1, CD133, ALDH1, PTEN, AKT, PI3K, and AR markers was assessed by immunohistochemistry. The overexpression of CD44 and AKT was associated with worse overall survival ( P =0.047 and P =0,034, respectively), on univariate analysis, in the cohort of parenchymal and bone metastases; the impact of AKT expression was also evident in the parenchymal cohort on uni ( P =0.021) and multivariate analysis ( P =0.027). The remaining markers did not exhibit a statistical correlation. Immunohistochemistry markers such as CD44 and AKT may have a prognostic impact on survival in patients with breast cancer brain metastases. The conjugation with other markers may help with the stratification of patients and therapy.

Implications for surveillance for breast cancer patients based on the internally and externally validated BRENDA-metastatic recurrence score

PURPOSE

Although the incidence of distant relapse is decreasing, 20-30% of patients with early breast cancer die of metastasis. The aim of this study is to characterize patients with metastasis-free survival(MFS) less than 5 years, to analyze the most probable site of metastases according to the internally and externally validated BRENDA-score. The BRENDA-score is a combination of the biological subtype and clinical staging.

METHOD

3832 patients with primary diagnosis of breast cancer and either distant metastatic recurrence within 5 years or MFS ≥ 5 years were assigned to this study. Patients were classified for metastatic recurrence according to the BRENDA-score. 1765 patients were in a validation set. Statistical methods were Kaplan-Meier curves, Cox regression analysis, Exhausted CHAID, likelihood-ratio tests and the Nearest Neighbor Estimation method.

RESULTS

There was a significant(p < 0.001) difference between the Kaplan-Meier MFS-functions of M0-patients stratified by BRENDA-score. The BRENDA score outperforms intrinsic subtypes and the Nottingham prognostic score. It fits the original data and the validation set equally well (p = 0.179).There was a significant(p < 0.001) difference between mean BRENDA-Index for patients with MFS < 5y(21.0 ± 9.0) and patients with MFS ≥ 5y(mean BRENDA-Index 11.7 ± 8.2). 55.6% of the very high risk patients(BRENDA-Index ≥ 27) had metastases within 5 years. The most likely primary metastatic site was bone(30%) followed by liver(19%) and lung(18%). The discriminatory ability(areas under the time dependent ROC curve) of the BRENDA score is good to acceptable for the first 5 years. In the very low/low risk (intermediate, high/very high) risk group 50% of all metastases were diagnosed within 26 months. Guideline adherence had a highly significant influence on outcome independent of the risk group.

CONCLUSION

The evaluation showed that the BRENDA-Score is a robust predictive tool for breast cancer recurrence and site of metastases in the first five years after diagnosis. It outperforms intrinsic subtypes and the Nottingham prognostic score. The BRENDA-score could be a tool for a risk orientated and targeted follow up.

Systematic review of the management of brain metastases from hormone receptor positive breast cancer

INTRODUCTION

Brain metastases are a common cause of morbidity and mortality in patients with breast cancer. Local central nervous system (CNS) directed therapies are usually the first line treatment for breast cancer brain metastases (BCBM), but those must be followed by systemic therapies to achieve long-term benefit. Systemic therapy for hormone receptor (HR⁺) breast cancer has evolved in the last 10 years, but their role when brain metastases occur is uncertain.

METHODS

We performed a systematic review of the literature focused on management of HR⁺ BCBM by searching Medline/PubMed, EBSCO, and Cochrane databases. The PRISMA guidelines were used for systematic review.

RESULTS

Out of 807 articles identified, 98 fulfilled the inclusion criteria in their relevance to the management of HR⁺ BCBM.

CONCLUSIONS

Similar to brain metastases from other neoplasms, local CNS directed therapies are the first line treatment for HR⁺ BCBM. Although the quality of evidence is low, after local therapies, our review supports the combination of targeted and endocrine therapies for both CNS and systemic management. Upon exhaustion of targeted/endocrine therapies, case series and retrospective reports suggest that certain chemotherapy agents are active against HR⁺ BCBM. Early phase clinical trials for HR⁺ BCBM are ongoing, but there is a need for prospective randomized trials to guide management and improve patients' outcome.

Interactive effects of molecular subtypes with tumor size and extracranial metastatic pattern on risk of brain metastasis in breast cancer patients: A population-based study

BACKGROUND

Early detection of brain metastasis (BM) is essential for prognostic improvement in breast cancer (BC) patients. The aim was to identify predictors of BCBM in different molecular subtypes on a population-based level.

METHODS

The Surveillance, Epidemiology, and End Results database was used to select BC patients diagnosed from 2010 to 2018. We evaluated the incidence and risk factors of BCBM and tested the interaction effects between molecular subtypes and other risk factors.

RESULTS

Among the 527,525 selected patients, molecular subtypes significantly interacted with T stage and extracranial metastasis (ECM) patterns on the risk of BM in the whole BC population (interaction p = 0.002, <0.001, respectively) and after excluding patients with unknown states of key factors. BM development was independent of the T stage only in HR-/HER2- patients (trend p = 0.126). We selected BC patients with single-organ ECM and found a significant interaction between molecular subtypes and ECM patterns (interaction p = 0.013). The impact of ECM patterns on the risk of BM was limited to HR-/HER2- patients (trend p < 0.001), for whom using bone metastasis as a reference, lung metastasis increased the risk of BM (OR = 1.936, 95% CI: 1.300-2.882, p = 0.001).

CONCLUSION

T stage and ECM patterns had different associations with BM in different molecular subtypes. HR-/HER2- BC had distinct features on BM development, manifested as a lack of tumor size effect and is associated with lung metastasis. Close surveillance for BM should be considered for HR-/HER2- BC patients.

Clinical usefulness of the lymphocyte-to-monocyte ratio and aggregate index of systemic inflammation in patients with esophageal cancer: a retrospective cohort study

BACKGROUND

Multiple perioperative inflammatory markers are considered important factors affecting the long-term survival of esophageal cancer (EC) patients. Hematological parameters, whether single or combined, have high predictive value.

AIM

To investigate the inflammatory status of patients with preoperative EC using blood inflammatory markers, and to establish and validate competing risk nomogram prediction models for overall survival (OS) and progression-free survival (PFS) in EC patients.

METHODS

A total of 508 EC patients who received radical surgery (RS) treatment in The First Affiliated Hospital of Zhengzhou University from August 5, 2013, to May 1, 2019, were enrolled and randomly divided into a training cohort (356 cases) and a validation cohort (152 cases). We performed least absolute shrinkage and selection operator (LASSO)-univariate Cox- multivariate Cox regression analyses to establish nomogram models. The index of concordance (C-index), time-dependent receiver operating characteristic (ROC) curves, time-dependent area under curve (AUC) and calibration curves were used to evaluate the discrimination and calibration of the nomograms, and decision curve analysis (DCA) was used to evaluate the net benefit of the nomograms. The relative integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated to evaluate the improvement in predictive accuracy of our new model compared with the AJCC staging system and another traditional model. Finally, the relationship between systemic inflammatory response markers and prognostic survival was explored according to risk plot, time-dependent AUC, Kaplan-Meier and restricted cubic spline (RCS).

RESULTS

Based on the multivariate analysis for overall survival (OS) in the training cohort, nomograms with 10 variables, including the aggregate index of systemic inflammation (AISI) and lymphocyte-to-monocyte ratio (LMR), were established. Time-dependent ROC, time-dependent AUC, calibration curves, and DCA showed that the 1-, 3-, and 5 year OS and PFS probabilities predicted by the nomograms were consistent with the actual observations. The C-index, NRI, and IDI of the nomograms showed better performance than the AJCC staging system and another prediction model. Moreover, risk plot, time-dependent AUC, and Kaplan-Meier showed that higher AISI scores and lower LMR were associated with poorer prognosis, and there was a nonlinear relationship between them and survival risk.

CONCLUSION

AISI and LMR are easy to obtain, reproducible and minimally invasive prognostic tools that can be used as markers to guide the clinical treatment and prognosis of patients with EC.

Incidence, characteristics, and management of central nervous system metastases in patients with inflammatory breast cancer

BACKGROUND

Patients with inflammatory breast cancer (IBC) have a high risk of central nervous system metastasis (mCNS). The purpose of this study was to quantify the incidence of and identify risk factors for mCNS in patients with IBC.

METHODS

The authors retrospectively reviewed patients diagnosed with IBC between 1997 and 2019. mCNS-free survival time was defined as the date from the diagnosis of IBC to the date of diagnosis of mCNS or the date of death, whichever occurred first. A competing risks hazard model was used to evaluate risk factors for mCNS.

RESULTS

A total of 531 patients were identified; 372 patients with stage III and 159 patients with de novo stage IV disease. During the study, there were a total of 124 patients who had mCNS. The 1-, 2-, and 5-year incidence of mCNS was 5%, 9%, and 18% in stage III patients (median follow-up: 5.6 years) and 17%, 30%, and 42% in stage IV patients (1.8 years). Multivariate analysis identified triple-negative tumor subtype as a significant risk factor for mCNS for stage III patients. For patients diagnosed with metastatic disease, visceral metastasis as first metastatic site, triple-negative subtype, and younger age at diagnosis of metastases were risk factors for mCNS.

CONCLUSIONS

Patients with IBC, particularly those with triple-negative IBC, visceral metastasis, and those at a younger age at diagnosis of metastatic disease, are at significant risk of developing mCNS. Further investigation into prevention of mCNS and whether early detection of mCNS is associated with improved IBC patient outcomes is warranted.

A Novel Strategy for Predicting 72-h Mortality After Admission in Patients With Polytrauma: A Study on the Development and Validation of a Web-Based Calculator

Background

Early and accessible screening of patients with polytrauma at a high risk of hospital death is essential. The purpose of this research was to seek an accurate and convenient solution to predict deaths occurring within 72 h after admission of these patients.

Methods

A secondary analysis was conducted on 3,075 patients with polytrauma from the Dryad database. We imputed missing values in eligible individuals with the k-nearest neighbor algorithm and then randomly stratified them into the training group (n = 2,461) and the validation group (n = 614) based on a proportion of 8:2. The restricted cubic spline, univariate, backward stepwise, and multivariate logistic regression methods were employed to determine the suitable predictors. Calibration and receiver operating characteristic (ROC) curves were applied to assess the calibration and discrimination of the obtained model. The decision curve analysis was then chosen as the measure to examine the clinical usage.

Results

Age, the Glasgow Coma Scale score, the Injury Severity Score, base excess, and the initial lactate level were inferred as independent prognostic factors related to mortality. These factors were then integrated and applied to construct a model. The performance of calibration plots, ROC curves, and decision curve analysis indicated that the model had satisfactory predictive power for 72-h mortality after admission of patients with polytrauma. Moreover, we developed a nomogram for visualization and a web-based calculator for convenient application (https://songandwen.shinyapps.io/DynNomapp/).

Conclusions

A convenient web-based calculator was constructed to robustly estimate the risk of death in patients with polytrauma within 72 h after admission, which may aid in further rationalization of clinical decision-making and accurate individual treatment.

Postoperative Pneumonia in Geriatric Patients With a Hip Fracture: Incidence, Risk Factors and a Predictive Nomogram

Objectives

To evaluate the incidence and risk factors of postoperative pneumonia (POP) in geriatric patients with a hip fracture after surgery, to design a predictive nomogram, and to validate the accuracy of the nomogram.

Design

Retrospective study.

Setting

A tertiary hospital affiliated to a medical university.

Patients/Participants

We retrospectively studied 1285 surgical-treated geriatric patients with a hip fracture from April 2010 to April 2018.

Intervention

Surgical treatment was performed on the patients of this study. The procedure methods were classified as: total hip arthroplasty, hemiarthroplasty, percutaneous fixation, intramedullary nail fixation, and plate/screw fixation.

Main Outcome Measurement

The primary interest of end point of this study is the development of POP during the postoperative period. The postoperative period in this study was defined as the time from 24 hours after surgery to discharge. The diagnostic criteria for pneumonia were set according to the guidelines built by the Infectious Diseases Society of America and the American Thoracic Society (Guidelines for the Management of Adults with Hospital-Acquired, Ventilator-Associated, and Healthcare-Associated Pneumonia, 2005). Potential variables for developing POP were identified using logistic regression analyses initially and were further selected via the method of LASSO. Then the independent risk factors were identified by multivariable regression analyses. A predictive nomogram was built based on the multiple regression model, and the calibration abilities of the nomogram was measured by Harrel C-index, calibration plot and Hosmer–Lemeshow test, respectively. Decision curve analysis was carried out to assess the net benefit due to threshold probability and an on-line questionnaire survey was conducted among the clinicians to assess the applicability of the nomogram coherently.

Results

Of the 1285 patients, 70 (5.4%) developed POP. COPD, number of comorbidities, ASA classification >2, preoperative dependent functional status and cognitive impairment were identified as independent risk factors of POP. The nomogram built based on the results showed good accordance between the predicted probabilities and the observed frequency. The decision curve analysis confirmed the clinical utility of the nomogram when the threshold probabilities were between 5% and 65% due to the net benefit, while the results of on-line questionnaire among 200 clinicians showed that 91.5% of the participants had a mental threshold of intervention between 5-50%.

Conclusion

(1). COPD, number of comorbidities, ASA classification >2, preoperative dependent functional status and cognitive impairment are independent risk factors for POP. (2). The nomogram built in this study has a good accordance between the predictive risk and the observational incidence. The results of decision curve and questionnaire among clinicians show well applicability of the nomogram.

Loss of ARID1A expression is associated with systemic inflammation markers and has important prognostic significance in gastric cancer

BACKGROUND

The tumor suppressor gene AT-rich interactive domain 1A (ARID1A) and systemic inflammatory response (SIR) have been reported to be related to the sensitivity to immunotherapy. This study intended to explore the relationship between ARID1A expression and SIR, and to further elucidate the prognostic value of ARID1A expression in gastric cancer (GC).

METHODS

The mRNA and protein expression of ARID1A were detected in 272 formalin-fixed paraffin-embedded (FFPE) tumor tissues. The data of nine systemic inflammation markers were collected 1 week before gastrectomy. Univariate and multivariate COX analysis were used to screen out independent predictors of GC.

RESULTS

Negative expression of ARID1A protein was related to GC with deficient mismatch repair (dMMR) (p = 0.033), positive programmed cell death-ligand 1 (PD-L1) (p = 0.005) and lower albumin level (p = 0.0064). Low expression of ARID1A mRNA was common in GC with abnormal E-cadherin (p = 0.020) and a higher platelet/lymphocyte ratio (PLR) (p = 0.0391). Multivariate COX analysis showed that the expression of ARID1A protein (p = 0.023), age (p = 0.004), T stage (p = 0.009) and N stage (p = 0.009) were independent predictors of GC. The nomogram established by independent predictors can accurately evaluate the survival risk of patients with GC.

CONCLUSIONS

The loss of ARID1A protein expression was associated with the dMMR subtype and high expression of PD-L1 in GC. Negative ARID1A protein and low expression of mRNA were associated with aberrant systemic inflammatory markers. The expression of ARID1A protein had important prognostic significance in GC.

Evolving management of HER2+ breast cancer brain metastases and leptomeningeal disease

Patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer are at a particularly high risk of breast cancer brain metastasis (BCBM) and leptomeningeal disease (LMD). Improvements in systemic therapy have translated to improved survival for patients with HER2-positive BCBM and LMD. However, the optimal management of these cases is rapidly evolving and requires a multidisciplinary approach. Herein, a team of radiation oncologists, medical oncologists, neuro-oncologists, and breast surgeon created a review of the evolving management of HER2-positive BCBM and LMD. We assess the epidemiology, diagnosis, and evolving treatment options for patients with HER2-positive BCBM and LMD, as well as the ongoing prospective clinical trials enrolling these patients. The management of HER2-positive BCBM and LMD represents an increasingly common challenge that involves the coordination of local and systemic therapy. Advances in systemic therapy have resulted in an improved prognosis, and promising targeted therapies currently under prospective investigation have the potential to further benefit these patients.

Anatomical and topographical variations in the distribution of brain metastases based on primary cancer origin and molecular subtypes: a systematic review

Background

While it has been suspected that different primary cancers have varying predilections for metastasis in certain brain regions, recent advances in neuroimaging and spatial modeling analytics have facilitated further exploration into this field.

Methods

A systematic electronic database search for studies analyzing the distribution of brain metastases (BMs) from any primary systematic cancer published between January 1990 and July 2020 was conducted using PRISMA guidelines.

Results

Two authors independently reviewed 1957 abstracts, 46 of which underwent full-text analysis. A third author arbitrated both lists; 13 studies met inclusion/exclusion criteria. All were retrospective single- or multi-institution database reviews analyzing over 8227 BMs from 2599 patients with breast (8 studies), lung (7 studies), melanoma (5 studies), gastrointestinal (4 studies), renal (3 studies), and prostate (1 study) cancers. Breast, lung, and colorectal cancers tended to metastasize to more posterior/caudal topographic and vascular neuroanatomical regions, particularly the cerebellum, with notable differences based on subtype and receptor expression. HER-2-positive breast cancers were less likely to arise in the frontal lobes or subcortical region, while ER-positive and PR-positive breast metastases were less likely to arise in the occipital lobe or cerebellum. BM from lung adenocarcinoma tended to arise in the frontal lobes and squamous cell carcinoma in the cerebellum. Melanoma metastasized more to the frontal and temporal lobes.

Conclusion

The observed topographical distribution of BM likely develops based on primary cancer type, molecular subtype, and genetic profile. Further studies analyzing this association and relationships to vascular distribution are merited to potentially improve patient treatment and outcomes.

Computer-assisted quantification of tumor-associated collagen signatures to improve the prognosis prediction of breast cancer

BACKGROUND

Collagen fibers play an important role in tumor initiation, progression, and invasion. Our previous research has already shown that large-scale tumor-associated collagen signatures (TACS) are powerful prognostic biomarkers independent of clinicopathological factors in invasive breast cancer. However, they are observed on a macroscale and are more suitable for identifying high-risk patients. It is necessary to investigate the effect of the corresponding microscopic features of TACS so as to more accurately and comprehensively predict the prognosis of breast cancer patients.

METHODS

In this retrospective and multicenter study, we included 942 invasive breast cancer patients in both a training cohort (n = 355) and an internal validation cohort (n = 334) from one clinical center and in an external validation cohort (n = 253) from a different clinical center. TACS corresponding microscopic features (TCMFs) were firstly extracted from multiphoton images for each patient, and then least absolute shrinkage and selection operator (LASSO) regression was applied to select the most robust features to build a TCMF-score. Finally, the Cox proportional hazard regression analysis was used to evaluate the association of TCMF-score with disease-free survival (DFS).

RESULTS

TCMF-score is significantly associated with DFS in univariate Cox proportional hazard regression analysis. After adjusting for clinical variables by multivariate Cox regression analysis, the TCMF-score remains an independent prognostic indicator. Remarkably, the TCMF model performs better than the clinical (CLI) model in the three cohorts and is particularly outstanding in the ER-positive and lower-risk subgroups. By contrast, the TACS model is more suitable for the ER-negative and higher-risk subgroups. When the TACS and TCMF are combined, they could complement each other and perform well in all patients. As expected, the full model (CLI+TCMF+TACS) achieves the best performance (AUC 0.905, [0.873-0.938]; 0.896, [0.860-0.931]; 0.882, [0.840-0.925] in the three cohorts).

CONCLUSION

These results demonstrate that the TCMF-score is an independent prognostic factor for breast cancer, and the increased prognostic performance (TCMF+TACS-score) may help us develop more appropriate treatment protocols.

Association of the neutrophil-to-lymphocyte ratio with brain metastases in Hispanic breast cancer patients

BACKGROUND

Breast cancer (BC) is one of the most common cancers and leading cause of cancer-related deaths in women. Metastatic disease, particularly brain metastases (BM), is associated with death in BC patients. The neutrophil-to-lymphocyte ratio (NLR) has been associated with BC prognosis, but it is not usually used in clinical practice and has not been associated with BM. We aimed to determine if there is an association between NLR and BM and if NLR is associated with survival in a Hispanic population.

METHODS

A retrospective cohort with a total of 2,104 patients with a confirmed diagnosis of BC at a single referral center were randomly divided into training and validation datasets. Univariable and multivariable analyses were performed to study the association of NLR with BM and/or survival.

RESULTS

No significant differences between datasets were identified. A high NLR (> 2.2) was associated with a higher frequency of BM after multivariable adjustment in both datasets. Overall survival was shorter in patients with a high NLR; however, the most important factor associated with outcome was the presence of BM. The interaction NLR/BM was not statistically significant.

CONCLUSION

A high NLR at BC diagnosis was associated with a higher frequency of BM, and the presence of BM was associated with worse overall survival in Hispanic BC patients.

Value of Nomogram Incorporated Preoperative Tumor Volume and the Number of Postoperative Pathologically Lymph Node Metastasis Regions on Predicting the Prognosis of Thoracic Esophageal Squamous Cell Carcinoma

Background

The aim of this study was to explore the influence of preoperative tumor volume, length, maximum diameter and the number of postoperative pathologically lymph node metastasis (LNM) regions on survival prognosis of esophageal squamous cell carcinoma (ESCC) patients.

Methods

A total of 296 patients with ESCC treated by standard curative esophagectomy were retrospectively analyzed. These patients were grouped for further analysis according to the optimal threshold of preoperative tumor volume, length, maximum diameter and the number of postoperative pathologically LNM regions. Kaplan–Meier method was used to calculate survival rate and survival comparison was performed by Log rank test. The Cox proportional hazards model was used to carry out univariate and multivariate analyses. Nomogram model was established by integrating statistically significant clinicopathological parameters, and the predictive value was further verified by calibration curve, concordance index (C-index) and decision curve.

Results

The univariate and multivariate Cox regression analysis all showed that differentiation grade, TNM stage, adjuvant therapy, preoperative tumor volume and the number of post operative pathologically LNM regions were independent prognostic factors for PFS and OS (all P<0.05). The C-indexes of PFS and OS by nomograms were predicted to be 0.747 (95% CI: 0.717–0.777) and 0.732 (95% CI: 0.697–0.767), respectively, which were significantly higher than the 7th AJCC TNM stage, the C-indexes 0.612 (95% CI: 0.574–0.650) and 0.633 (95% CI: 0.595–0.671), separately. In addition, the calibration curves of nomogram models were highly consistent with actual observation for the five-year PFS and OS rate, and the decision curve analysis also showed that nomogram models had higher clinical application potentials than TNM staging model in predicting survival prognosis of thoracic ESCC after surgery.

Conclusion

The nomograms incorporated preoperative tumor volume and the number of postoperative pathologically LNM areas are of great value in predicting survival prognosis of patients with thoracic ESCC.

A nomogram for predicting brain metastasis in patients with de novo stage IV breast cancer

Background

Brain metastasis (BM) is a very serious event in patients with breast cancer. The aim of this study was to establish a nomogram to predict the risk of BM in patients with de novo stage IV breast cancer.

Methods

We gathered female patients diagnosed with de novo stage IV breast cancer between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. After randomly allocating the patients to the training set and verification set, we used univariate and multivariate logistic regression to analyze the relationship between BM and clinicopathological features. Finally, we developed a nomogram which was validated by the analysis of calibration curve and receiver operating characteristic curve.

Results

Of 7,154 patients with de novo stage IV breast cancer, 422 developed BM. Age, tumor size, subtype, and the degree of lung involvement were significantly correlated with BM. The nomogram had discriminatory ability with an area under curve (AUC) of 0.640 [95% confidence interval (CI): 0.607 to 0.673] in the training set, and 0.644 (95% CI: 0.595 to 0.693) in the validation set.

Conclusions

Our study developed a nomogram to predict BM for de novo stage IV breast cancer, thus helping clinicians to identify patients at high-risk of BM and implement early preventive interventions to improve their prognoses.

Prophylactic cranial irradiation reduces the incidence of brain metastasis in a mouse model of metastatic, HER2-positive breast cancer

Prophylactic cranial irradiation (PCI) can reduce the incidence of brain metastasis and improve overall survival in some patients with acute lymphoblastic leukemia or small-cell lung cancer. We examined the potential effects of PCI in a mouse model of breast cancer brain metastasis. The HER2+ inflammatory breast cancer cell line MDA-IBC3 was labeled with green fluorescent protein and injected via tail-vein into female SCID/Beige mice. Mice were then given 0 Gy or 4 Gy of whole-brain irradiation 2 days before tumor-cell injection or 5 days, 3 weeks, or 6 weeks after tumor-cell injection. Mice were sacrificed 4-weeks or 8-weeks after injection and brain tissues were examined for metastasis by fluorescent stereomicroscopy. In the unirradiated control group, brain metastases were present in 77% of mice at 4 weeks and in 90% of mice at 8 weeks; by comparison, rates for the group given PCI at 5 days after tumor-cell injection were 20% at 4 weeks (p=0.01) and 30% at 8 weeks (p=0.02). The PCI group also had fewer brain metastases per mouse at 4 weeks (p=0.03) and 8 weeks (p=0.006) versus the unirradiated control as well as a lower metastatic burden (p=0.01). Irradiation given either before tumor-cell injection or 3-6 weeks afterward had no significant effect on brain metastases compared to the unirradiated control. These results underscore the importance of timing for irradiating subclinical disease. Clinical whole brain strategies to target subclinical brain disease as safely as possible may warrant further study.

Breast Cancer Brain Metastasis-Overview of Disease State, Treatment Options and Future Perspectives

Simple Summary

In this review, we present the latest information on the pathophysiology, diagnosis, and local and systemic treatment of brain metastases from breast cancer, with a focus on recent publications. Improving the local treatment and subtype-specific systemic therapies through advancements in basic and translational research will contribute to better clinical outcomes for patients with breast cancer brain metastasis.

Abstract

Breast cancer is the second most common origin of brain metastasis after lung cancer. Brain metastasis in breast cancer is commonly found in patients with advanced course disease and has a poor prognosis because the blood–brain barrier is thought to be a major obstacle to the delivery of many drugs in the central nervous system. Therefore, local treatments including surgery, stereotactic radiation therapy, and whole-brain radiation therapy are currently considered the gold standard treatments. Meanwhile, new targeted therapies based on subtype have recently been developed. Some drugs can exceed the blood–brain barrier and enter the central nervous system. New technology for early detection and personalized medicine for metastasis are warranted. In this review, we summarize the historical overview of treatment with a focus on local treatment, the latest drug treatment strategies, and future perspectives using novel therapeutic agents for breast cancer patients with brain metastasis, including ongoing clinical trials.

Predictive Nomogram of Subsequent Liver Metastasis After Mastectomy or Breast-Conserving Surgery in Patients With Nonmetastatic Breast Cancer

Background:

Metastasis accounts for the majority of deaths in patients with breast cancer. Liver metastasis is reported common for breast cancer patients. The purpose of this study was to construct a nomogram to predict the likelihood of subsequent liver metastasis in patients with nonmetastatic breast cancer, thus high-risk patient populations can be prevented and monitored.

Methods:

A total of 1840 patients with stage I-III breast cancer were retrospectively included and analyzed. A nomogram was constructed to predict liver metastasis based on multivariate logistic regression analysis. SEER database was used for external validation. C-index, calibration curve and decision curve analysis were used to evaluate the predictive performance of the model.

Results:

The nomogram included 3 variables related to liver metastasis: HER2 status (odds ratio (OR) 1.86, 95%CI 1.02 to 3.41; P = 0.045), tumor size (OR 3.62, 1.91 to 6.87; P < 0.001) and lymph node metastasis (OR 2.26, 1.18 to 4.34; P = 0.014). The C index of the training cohort, internal validation cohort and external validation cohort were 0.699, 0.814 and 0.791, respectively. The nomogram was well-calibrated, with no statistical difference between the predicted and the observed probabilities.

Conclusion:

We have developed and validated a robust tool enabled to predict subsequent liver metastasis in patients with nonmetastatic breast cancer. Distinguishing a population of patients at high risk of liver metastasis will facilitate preventive treatment or monitoring of liver metastasis.

A Simple-to-Use Web-Based Calculator for Survival Prediction in Acute Respiratory Distress Syndrome

Background: The aim of this study was to construct and validate a simple-to-use model to predict the survival of patients with acute respiratory distress syndrome.

Methods: A total of 197 patients with acute respiratory distress syndrome were selected from the Dryad Digital Repository. All eligible individuals were randomly stratified into the training set (n=133) and the validation set (n=64) as 2: 1 ratio. LASSO regression analysis was used to select the optimal predictors, and receiver operating characteristic and calibration curves were used to evaluate accuracy and discrimination of the model. Clinical usefulness of the model was also assessed using decision curve analysis and Kaplan-Meier analysis.

Results: Age, albumin, platelet count, PaO₂/FiO₂, lactate dehydrogenase, high-resolution computed tomography score, and etiology were identified as independent prognostic factors based on LASSO regression analysis; these factors were integrated for the construction of the nomogram. Results of calibration plots, decision curve analysis, and receiver operating characteristic analysis showed that this model has good predictive ability of patient survival in acute respiratory distress syndrome. Moreover, a significant difference in the 28-day survival was shown between the patients stratified into different risk groups (P < 0.001). For convenient application, we also established a web-based calculator (https://huangl.shinyapps.io/ARDSprognosis/).

Conclusions: We satisfactorily constructed a simple-to-use model based on seven relevant factors to predict survival and prognosis of patients with acute respiratory distress syndrome. This model can aid personalized treatment and clinical decision-making.

Association Between Ultrasound Parameters and History of Ischemic or Hemorrhagic Stroke in Patients With Moyamoya Disease

Objective: To explore the association between ultrasound parameters and previous ischemic or hemorrhagic stroke in patients with moyamoya disease (MMD), and develop an ultrasound-based nomogram to identify stroke in patients with MMD.

Methods: We prospectively enrolled 52 consecutive patients (92 hemispheres) with MMD at the Beijing Tiantan Hospital. Thirty-six patients (65 hemispheres) were assigned to the training dataset from September 2019 to February 2020, and 16 patients (27 hemispheres) were assigned to the validation dataset from March 2020 to July 2020. Multivariate logistic regression analysis was applied to identify ultrasound parameters associated with previous history of ipsilateral stroke in patients with MMD, and a nomogram was subsequently constructed to identify stroke in patients with MMD. The performance of the nomogram was evaluated with respect to discrimination, calibration, and clinical usefulness.

Results: Multivariate analysis indicated that the flow volume (FV) of the extracranial internal carotid artery (EICA) and the peak systolic velocity (PSV) of the posterior cerebral artery (PCA) were independently associated with ipsilateral stroke in patients with MMD, a nomogram incorporating these two parameters was constructed to identify stroke patients. The area under the receiver operating characteristic (AUROC) curves was 0.776 (95% CI, 0.656–0.870) in the training dataset and 0.753 (95% CI, 0.550–0.897) in the validation dataset suggested that the model had good discrimination ability. The calibration plot showed good agreement in both the two datasets. The decision curve analysis (DCA) revealed that the nomogram was clinically useful.

Conclusions: Ultrasound parameters of EICA and PCA are independently associated with history of previous ipsilateral ischemic or hemorrhagic stroke in patients with MMD. The present ultrasound-based nomogram might provide information to identify MMD patients with high risk of stroke. Future long-term follow-up studies are needed to prove the predictive value in other independent cohorts.

Clinical Trial Registration:http://www.chictr.org.cn/index.aspx. Unique Identifier: ChiCTR1900026075.

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).

Nomogram to predict overall survival for patients with non-metastatic cervical esophageal cancer: a SEER-based population study

Background

Cervical esophageal cancer (CEC) is an uncommon malignancy with poor prognosis, and there is no specific model that can be used to accurately predict the survival of patients with CEC.

Methods

The Surveillance, Epidemiology, and End Results (SEER) database was searched for patients with non-metastatic CEC from 2004 to 2015. Overall survival (OS) and disease-specific survival (DSS) rates were calculated using the Kaplan-Meier method. Predictive factors were analyzed by Cox's proportional hazards regression, and a nomogram was created to predict survival probability using R software.

Results

We identified 601 patients with CEC, 94.3% of whom had squamous cell carcinoma (SCC). The median follow-up time was 71 months. The median OS and DSS for the overall population were 15 and 18 months, respectively. There was a statistically significant decrease in surgical rates over time, from 16.7% in 2004 to 8% in 2015 (P=0.035). Comprehensive strategies consisting of two or three treatment modalities were correlated with significantly better OS and DSS (P<0.001 for both). We randomly assigned half of the patients to the training cohort (n=300) and the other half to the validation cohort (n=301). Multivariate Cox regression analysis was performed using the training cohort. Age, sex, tumor size, stages in the 7th edition of the American Joint Committee on Cancer (AJCC) staging system, and treatment with surgery, radiotherapy, or chemotherapy were identified as independent risk factors for OS. These factors were incorporated into the development of a nomogram for predicting 1-, 3-, and 5-year OS rates. The C-index of the nomogram was 0.743, which was statistically higher than that of the AJCC staging system. The internal validation, using bootstrap resampling and external validation, demonstrated the accuracy of the nomogram.

Conclusions

We developed and validated the first nomogram for CEC. This nomogram could be used to predict the OS of CEC patients with a relatively high accuracy.

Predicting the Risk of Malignancy of Lung Nodules Diagnosed as Indeterminate on Radial Endobronchial Ultrasound-Guided Biopsy

The next diagnostic step in cases of indeterminate radial probe endobronchial ultrasound (radial EBUS)-guided biopsy results remains uncertain. This study aimed to identify risk factors for malignancy based on clinical findings, chest computed tomography (CT), and radial EBUS images, and to estimate the risk of malignancy in lung nodules that showed indeterminate radial EBUS-guided biopsy results by constructing a nomogram. This retrospective study included 157 patients with indeterminate results on an initial radial EBUS biopsy performed at the Samsung Medical Center from January 2017 to December 2018, but with a definitive final diagnosis. Medical records, chest CT, radial EBUS images, and the final diagnoses were reviewed. Patients were randomly divided into training and validation sets. Factors related to malignancy were identified through logistic regression analysis, and a nomogram was constructed using the training set and subsequently applied to the validation set. Six factors in univariable and multivariable analyses, including upper lobe location, spiculation, satellite nodules, echogenicity, presence of dots or linear arcs, and patency of vessels and bronchi predicted malignancy. A nomogram was constructed based on these predictors. The area under the curve (AUC) value of the nomogram was 0.858 using the chest CT factors, which improved to 0.952 when radial EBUS factors were added. The calibration curve showed good agreement between the actual and nomogram-predicted malignancy outcomes. The utility of radial EBUS images for revealing risk factors of malignancy was confirmed. Furthermore, our nomogram was able to predict the probability of malignancy in lung nodules with indeterminate radial EBUS-guided biopsy results.

A clinical nomogram and heat map for assessing survival in patients with stage I non-small cell lung cancer after complete resection

Background:

Assessing the prognosis of patients with early-stage non-small cell lung cancer (NSCLC) has become a major clinical issue. This study aimed to devise an effective clinical nomogram and heat map for assessing the survival of patients with stage I NSCLC receiving complete resection.

Methods:

Nomograms were established based on a retrospective study of 654 patients with stage I NSCLC who underwent radical resection at Sun Yat-Sen University Cancer Center between January 2009 and December 2014. The concordance index (C-index) and calibration curve were used to measure the accuracy and discriminative ability of the final nomogram. Heat maps were constructed with prognostic factors and survival probabilities. Survival curves were depicted using the Kaplan–Meier method, and the log-rank test was used to determine significance. Patients were classified into low- and high-risk subgroups using recursive partitioning analysis based on nomogram scores.

Results:

In univariate and multivariate analyses, the independent factors for overall survival (OS) and disease-free survival (DFS) were age, sex, tumor size, and visceral pleural invasion, which were all selected in the nomogram. The C-indices of the nomogram for predicting OS and DFS were 0.694 [95% confidence interval (CI) 0.651–0.737] and 0.653 (95% CI 0.61–0.696), respectively. The calibration curves for OS and DFS probabilities showed a good agreement between the nomogram prediction and actual observation. A heat map was generated using the above independent factors for OS and DFS. High-risk patients had shorter OS [hazard ratio (HR) = 3.535, 95% CI 2.444–5.113, p < 0.001] and DFS (HR = 2.607, 95% CI 1.922–3.537, p < 0.001) than low-risk patients.

Conclusion:

We established a prognostic nomogram and heat map that can be useful for evaluating survival in patients with stage I NSCLC after complete resection. The tools resulted in more accurate prediction and may guide clinicians in making treatment decisions.

A risk stratification model for predicting brain metastasis and brain screening benefit in patients with metastatic triple-negative breast cancer

BACKGROUND

Patients with metastatic triple-negative breast cancer (mTNBC) frequently experience brain metastasis. This study aimed to identify prognostic factors and construct a nomogram for predicting brain metastasis possibility and brain screening benefit in mTNBC patients.

METHODS

Patients with mTNBC treated at our institution between January 2011 and December 2018 were retrospectively analyzed. Fine and Gray's competing risks model was used to identify independent prognostic factors. By integrating these prognostic factors, a competing risk nomogram and risk stratification model were developed and evaluated with concordance index (C-index) and calibration curves.

RESULTS

A total of 472 patients were retrospectively analyzed, including 305 patients in the training set, 78 patients in the validation set I and 89 patients in the validation set II. Four clinicopathological factors were identified as independent prognostic factors in the nomogram: lung metastasis, number of metastatic organ sites, hilar/mediastinal lymph node metastasis and KI-67 index. The C-indexes and calibration plots showed that the nomogram exhibited a sufficient level of discrimination. A risk stratification was further generated to divide all the patients into three prognostic groups. The cumulative incidence of brain metastasis at 18 months was 5.3% (95% confidence interval [CI], 2.5%-9.7%) for patients in the low-risk group, while 14.3% (95% CI, 9.3%-20.4%) for patients with intermediate risk and 34.3% (95% CI, 26.8%-41.9%) for patients with high risk. Routine brain MRI screening improved overall survival in high-risk group (HR 0.67, 95% CI 0.46-0.98, P = .039), but not in low-risk group (HR 0.93, 95% CI 0.57-1.49, P = .751) and intermediate-risk group (HR 0.83, 95% CI 0.55-1.27, P = .386).

CONCLUSIONS

We have developed a robust tool that is able to predict subsequent brain metastasis in mTNBC patients. Our model will allow selection of patients at high risk for brain metastasis who might benefit from routine bran MRI screening.

Central nervous system metastases in breast cancer: the impact of age on patterns of development and outcome

PURPOSE

The purpose of this study is to explore differences in the pattern and outcome of central nervous system (CNS) involvement in breast cancer by age at diagnosis.

METHODS

A retrospective database of a tertiary cancer center yielded 174 consecutive patients with breast cancer who were diagnosed with CNS metastases in 2006-2019. Data on histopathology, characteristics of CNS involvement, treatments, and survival (at three time points during the disease course) were compared between patients aged ≤ 45 and > 45 years. Pearson Chi-square or Fisher exact test and Kaplan-Meier survival curves with log-rank test were used for statistical analyses.

RESULTS

Study population was divided according to age at diagnosis of breast cancer. 65 patients were ≤ 45 years old and 109 patients > 45 years old. The younger group was characterized by longer median overall survival (117.1 months vs 88 months, p = 0.017) and longer interval between breast cancer diagnosis to development of CNS metastases (97.4 months vs 75.9 months, p = 0.026). Median survival after development of CNS disease was not significantly different (18.7 months vs 11.1 months, p = 0.341), although it was significantly longer in younger patients within the subgroup of patients with triple-negative disease (22.5 vs 7.9 months, p = 0.033). There were no between-group differences in number, location, and clinical presentation of CNS metastases or in systemic and CNS-directed treatment approaches.

CONCLUSION

While the presentation of CNS involvement was similar between the different age groups, younger patients had significantly longer CNS-free interval and longer overall survival, and for the subgroups of triple-negative patients, younger age at breast cancer diagnosis was associated with longer survival after diagnosis of CNS disease.

Development of CNS metastases in breast cancer patients treated with curative intent: a case-control study

Background:

The aim of this study was to identify risk factors that may predispose breast cancer patients to the development of CNS metastases.

Materials & methods:

We conducted a matched case–control study of breast cancer patients treated with surgery with curative intent. A total of 71 cases and 71 controls were analyzed, matched by year of surgery.

Results:

In our multivariable model, positive lymph node status (odds ratio [OR]: 5.08; CI: 2.04–12.65), the use of neoadjuvant chemotherapy (OR: 6.02; CI: 2.06–17.57) and triple-negative breast cancer (OR: 5.44; CI: 1.99–14.90) were statistically significant predictors of the development of CNS metastases.

Conclusion:

Women with certain risk factors have an increased odds of developing CNS metastases and evaluation of utility in brain metastases screening should be considered.

Development and Validation of Prognostic Nomograms to Predict Overall and Cancer-Specific Survival for Patients with Adenocarcinoma of the Urinary Bladder: A Population-Based Study

BACKGROUNDS

Adenocarcinoma of the bladder (ACB) rarely occurs but is associated with poor outcome. We aim to establish reliable nomograms for estimating cancer-specific survival (CSS) and overall survival (OS) of ACB patients.

METHODS

ACB patients were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database (2004-2015). A total of 1,149 patients were randomly divided into training cohort (n = 692) and validation cohort (n = 457). Multivariate Cox proportional hazards regression models were employed to identify independent prognostic factors. Nomograms predicting OS and CSS were constructed utilizing screened factors. The performance of nomograms was internally and externally validated by calibration curves, the receiver operating characteristic (ROC) curves, concordance index (C-index), and decision curve analysis (DCA).

RESULTS

OS nomogram incorporated age, race, histologic grade, American Joint Committee of Cancer (AJCC) stage, metastasis, surgery, chemotherapy, and tumor size. The C-indices were 0.754 (95% CI: 0.732-0.775) for training set and 0.743 (95% CI: 0.712-0.767) for validation set. Meanwhile, the calibration plots for 3- and 5-year OS displayed fine concordance between actual and predicted outcomes. In addition, higher areas under the curve (AUCs) were seen in training cohort (3-year: 0.799 vs. 0.630; 5-year: 0.797 vs. 0.648) and validation cohort (3-year: 0.802 vs. 0.662; 5-year: 0.752 vs. 0.660). Finally, DCA curves of the nomograms exhibited larger net benefits than AJCC stage. CSS nomogram showed similar results.

CONCLUSION

Our study constructed and validated nomograms with improved discriminative abilities and clinical benefits to predict the survival outcomes of ACB patients. The models might assist clinicians in optimizing therapeutic management on individual levels.

Effects of Cancer Stem Cells in Triple-Negative Breast Cancer and Brain Metastasis: Challenges and Solutions

A higher propensity of developing brain metastasis exists in triple-negative breast cancer (TNBC). Upon comparing the metastatic patterns of all breast cancer subtypes, patients with TNBC exhibited increased risks of the brain being the initial metastatic site, early brain metastasis development, and shortest brain metastasis-related survival. Notably, the development of brain metastasis differs from that at other sites owing to the brain-unique microvasculature (blood brain barrier (BBB)) and intracerebral microenvironment. Studies of brain metastases from TNBC have revealed the poorest treatment response, mostly because of the relatively backward strategies to target vast disease heterogeneity and poor brain efficacy. Moreover, TNBC is highly associated with the existence of cancer stem cells (CSCs), which contribute to circulating cancer cell survival before BBB extravasation, evasion from immune surveillance, and plasticity in adaptation to the brain-specific microenvironment. We summarized recent literature regarding molecules and pathways and reviewed the effects of CSC biology during the formation of brain metastasis in TNBC. Along with the concept of individualized cancer therapy, certain strategies, namely the patient-derived xenograft model to overcome the lack of treatment-relevant TNBC classification and techniques in BBB disruption to enhance brain efficacy has been proposed in the hope of achieving treatment success.

Identification of risk factors for central nervous system metastasis in patients with breast cancer with neurologic symptoms

BACKGROUND

The current study was performed to identify factors that are present at the time of breast cancer (BC) diagnosis that are associated with a higher rate of central nervous system metastasis (CNSm).

METHODS

The authors analyzed a database of patients with a confirmed diagnosis of BC who were referred for a neuro-oncology consultation at the National Cancer Institute in Mexico City, Mexico, from June 2009 to June 2017. Information was collected prospectively and included demographic, pathologic, and clinical data at the time of diagnosis of BC. Bivariate and multivariate logistic regression models were built to estimate the associations between the development of CNSm and the time after BC diagnosis.

RESULTS

Among 970 patients with BC, 263 (27%) were diagnosed with CNSm. The median time from BC diagnosis to the development of CNSm was 33 months (interquartile range, 15-76 months). After multivariate analysis, age <50 years at the time of BC diagnosis (odds ratio [OR], 2.5; 95% confidence interval [95% CI], 1.8-3.5 [P < .0001]), human epidermal growth factor receptor 2 (HER2)-positive status (HER2+) (OR, 3.6; 95% CI, 2.1-6.1 [P < .0001]), luminal B/HER2+ subtype (OR, 3.1; 95% CI, 1.9-5.3 [P < .001]), triple-negative subtype(OR, 2.4; 95% CI, 1.5-4 [P = .001]), and Karnofsky performance status ≤70 (OR, 6.6; 95% CI, 4.5-9.6 [P < .0001]) were associated with a higher frequency of CNSm. Brain parenchyma was the most common site of CNSm. The median overall survival after a diagnosis of CNSm was 12.2 months (95% CI, 9.3-15.1 months).

CONCLUSIONS

CNSm is not uncommon among patients with BC, particularly in those with neurologic symptoms who require neuro-oncology evaluation and are aged <50 years at the time of diagnosis, have HER2+ or triple-negative subtypes, have a poor Karnofsky performance status, and/or have ≥2 non-CNS metastases.

Epileptogenesis and oncogenesis: An antineoplastic role for antiepileptic drugs in brain tumours?

The first description of epileptic seizures due to brain tumours occurred in 19th century. Nevertheless, after over one hundred years, scientific literature is still lacking on how epilepsy and its treatment can affect tumour burden, progression and clinical outcomes. In patients with brain tumours, epilepsy dramatically impacts their quality of life (QoL). Even antiepileptic therapy seems to affect tumor lesion development. Numerous studies suggest that certain actors involved in epileptogenesis (inflammatory changes, glutamate and its ionotropic and metabotropic receptors, GABA-A and its GABA-AR receptor, as well as certain ligand- and voltage-gated ion channel) may also contribute to tumorigenesis. Although some antiepileptic drugs (AEDs) are known operating on such mechanisms underlying epilepsy and tumor development, few preclinical and clinical studies have tried to investigate them as targets of pharmacological tools acting to control both phenomena. The primary aim of this review is to summarize known determinants and pathophysiological mechanisms of seizures, as well as of cell growth and spread, in patients with brain tumors. Therefore, a special focus will be provided on the anticancer effects of commonly prescribed AEDs (including levetiracetam, valproic acid, oxcarbazepine and others), with an overview of both preclinical and clinical data. Potential clinical applications of this finding are discussed.

Small molecule tyrosine kinase inhibitors in glioblastoma

Glioblastoma (GBM) is the most common malignant primary brain tumor, with poor survival despite treatment with surgery, radiotherapy, and chemotherapy with temozolomide. Little progress has been made over the last two decades, and there remain unmet medical needs. Approximately 45% of patients with GBM carry EGFR mutations, and 13% of them possess altered PDGFR genes. Moreover, VEGF/VEGFR mutations are also observed in the patient population. Tyrosine kinase inhibitors (TKIs) are emerging cancer therapy drugs that inhibit signal transduction cascades affecting cell proliferation, migration, and angiogenesis. Indications for small molecule TKIs have been successfully expanded to multiple types of cancer; however, none of the TKIs have been approved for patients with GBM. In this review, we summarize clinical trials of small molecule TKIs in patients with GBM and plausible hypotheses for negative clinical study results. We also discuss the potential TKI candidates that presented significant preclinical outcomes in patients with GBM.

Challenges in the treatment of breast cancer brain metastases: evidence, unresolved questions, and a practical algorithm

Breast cancer is the leading cause of brain metastases in women. Large randomized clinical trials that have evaluated local therapies in patients with brain metastases include patients with brain metastases from a variety of cancer types. The incidence of brain metastases in the breast cancer population continues to grow, which is, aside from the rising breast cancer incidence, mainly attributable to improvements in systemic therapies leading to more durable control of extracranial metastatic disease and prolonged survival. The management of breast cancer brain metastases remains challenging, even more so with the continued advancement of local and highly effective systemic therapies. For most patients, a metastases-directed initial approach (i.e., radiation, surgery) represents the most appropriate initial therapy. Treatment should be based on multidisciplinary team discussions and a shared decision with the patients taking into account the risks and benefits of each therapeutic modality with the goal of prolonging survival while maintaining quality of life. In this narrative review, a multidisciplinary group of experts will address challenging questions in the context of current scientific literature and propose a therapeutic algorithm for breast cancer patients with brain metastases.

microRNAs Orchestrate Pathophysiology of Breast Cancer Brain Metastasis: Advances in Therapy

Brain metastasis (BM) predominantly occurs in triple-negative (TN) and epidermal growth factor 2 (HER2)-positive breast cancer (BC) patients, and currently, there is an unmet need for the treatment of these patients. BM is a complex process that is regulated by the formation of a metastatic niche. A better understanding of the brain metastatic processes and the crosstalk between cancer cells and brain microenvironment is essential for designing a novel therapeutic approach. In this context, the aberrant expression of miRNA has been shown to be associated with BM. These non-coding RNAs/miRNAs regulate metastasis through modulating the formation of a metastatic niche and metabolic reprogramming via regulation of their target genes. However, the role of miRNA in breast cancer brain metastasis (BCBM) is poorly explored. Thus, identification and understanding of miRNAs in the pathobiology of BCBM may identify a novel candidate miRNA for the early diagnosis and prevention of this devastating process. In this review, we focus on understanding the role of candidate miRNAs in the regulation of BC brain metastatic processes as well as designing novel miRNA-based therapeutic strategies for BCBM.

Risk factors for breast cancer brain metastases: a systematic review

Background: Brain metastasis (BM) is an increasingly common and devastating complication of breast cancer (BC).

Methods: A systematic literature search of EMBASE and MEDLINE was conducted to elucidate the current state of knowledge on known and novel prognostic factors associated with 1) the risk for BCBM and 2) the time to brain metastases (TTBM).

Results: A total of 96 studies involving institutional records from 28 countries were identified. Of these, 69 studies reported risk factors of BCBM, 46 factors associated with the TTBM and twenty studies examined variables for both outcomes. Young age, estrogen receptor negativity (ER-), overexpression of human epidermal factor (HER2+), and higher presenting stage, histological grade, tumor size, Ki67 labeling index and nodal involvement were consistently found to be independent risk factors of BCBM. Of these, triple-negative BC (TNBC) subtype, ER-, higher presenting histological grade, tumor size, and nodal involvement were also reported to associate with shorter TTBM. In contrast, young age, hormone receptor negative (HR-) status, higher presenting stage, nodal involvement and development of liver metastasis were the most important risk factors for BM in HER2-positive patients.

Conclusions: The study provides a comprehensive and individual evaluation of the risk factors that could support the design of screening tools and interventional trials for early detection of BCBM.

HER2-targeted therapy prolongs survival in patients with HER2-positive breast cancer and intracranial metastatic disease: a systematic review and meta-analysis

BACKGROUND

Intracranial metastatic disease (IMD) is a serious and known complication of human epidermal growth factor receptor 2 (HER2)-positive breast cancer. The role of targeted therapy for patients with HER2-positive breast cancer and IMD remains unclear. In this study, we sought to evaluate the effect of HER2-targeted therapy on IMD from HER2-positive breast cancer.

METHODS

We searched MEDLINE, EMBASE, CENTRAL, and gray literature sources for interventional and observational studies reporting survival, response, and safety outcomes for patients with IMD receiving HER2-targeted therapy. We pooled outcomes through meta-analysis and examined confounder effects through forest plot stratification and meta-regression. Evidence quality was evaluated using GRADE (PROSPERO CRD42020161209).

RESULTS

A total of 97 studies (37 interventional and 60 observational) were included. HER2-targeted therapy was associated with prolonged overall survival (hazard ratio [HR] 0.47; 95% confidence interval [CI], 0.39-0.56) without significantly prolonged progression-free survival (HR 0.52; 95% CI, 0.27-1.02) versus non-targeted therapy; the intracranial objective response rate was 19% (95% CI, 12-27%), intracranial disease control rate 62% (95% CI, 55-69%), intracranial complete response rate 0% (95% CI, 0-0.01%), and grade 3+ adverse event rate 26% (95% CI, 11-45%). Risk of bias was high in 40% (39/97) of studies.

CONCLUSION

These findings support a potential role for systemic HER2-targeted therapy in the treatment of patients with IMD from HER2-positive metastatic breast cancer.

A nomogram constructed using intraoperative ex vivo shear-wave elastography precisely predicts metastasis of sentinel lymph nodes in breast cancer

OBJECTIVE

To develop a nomogram and validate its use for the intraoperative evaluation of nodal metastasis using shear-wave elastography (SWE) elasticity values and nodal size METHODS: We constructed a nomogram to predict metastasis using ex vivo SWE values and ultrasound features of 228 axillary LNs from fifty-five patients. We validated its use in an independent cohort comprising 80 patients. In the validation cohort, a total of 217 sentinel LNs were included.

RESULTS

We developed the nomogram using the nodal size and elasticity values of the development cohort to predict LN metastasis; the area under the curve (AUC) was 0.856 (95% confidence interval (CI), 0.783-0.929). In the validation cohort, 15 (7%) LNs were metastatic, and 202 (93%) were non-metastatic. The mean stiffness (23.54 and 10.41 kPa, p = 0.005) and elasticity ratio (3.24 and 1.49, p = 0.028) were significantly higher in the metastatic LNs than those in the non-metastatic LNs. However, the mean size of the metastatic LNs was not significantly larger than that of the non-metastatic LNs (8.70 mm vs 7.20 mm, respectively; p = 0.123). The AUC was 0.791 (95% CI, 0.668-0.915) in the validation cohort, and the calibration plots of the nomogram showed good agreement.

CONCLUSIONS

We developed a well-validated nomogram to predict LN metastasis. This nomogram, mainly based on ex vivo SWE values, can help evaluate nodal metastasis during surgery.

KEY POINTS

• A nomogram was developed based on axillary LN size and ex vivo SWE values such as mean stiffness and elasticity ratio to easily predict axillary LN metastasis during breast cancer surgery. • The constructed nomogram presented high predictive performance of sentinel LN metastasis with an independent cohort. • This nomogram can reduce unnecessary intraoperative frozen section which increases the surgical time and costs in breast cancer patients.

Incidence of Brain Metastases in Nonmetastatic and Metastatic Breast Cancer: Is There a Role for Screening?

BACKGROUND

Current National Comprehensive Cancer Network and American Society of Clinical Oncology guidelines recommend against screening breast cancer patients for asymptomatic brain metastases. Because brain metastases are a major cause of morbidity and mortality from breast cancer, we undertook a literature review to ascertain whether there might be a role for brain metastases screening in high-risk patient subgroups.

MATERIALS AND METHODS

A literature search was conducted on the OvidSP platform in the MedLine database, using MeSH terms and subject headings related to breast cancer, brain metastases, and incidence. The search was conducted without language or publication restrictions, and included articles indexed from January 1, 2006 to June 10, 2018. Experimental and observational studies that reported the incidence of brain metastases in patients with nonmetastatic or metastatic breast cancer were included.

RESULTS

One hundred seventy studies were identified, with 33 included in the final analysis. Among nonmetastatic breast cancer patients, incidence of brain metastases as site of first recurrence per year of median follow-up ranged from 0.1% to 3.2%. Although incidence of brain metastases was much higher among the metastatic breast cancer population overall, it was particularly high among metastatic HER2-overexpressing (HER2⁺) and triple-negative populations, ranging between 22% and 36% for the former, and 15%-37% for the latter in the absence of screening.

CONCLUSION

In patients with nonmetastatic breast cancer, screening for asymptomatic brain metastases cannot currently be justified. However, due to the high incidence of brain metastases among patients with metastatic HER2⁺ and triple-negative breast cancer, studies to determine the value of screening for brain metastases should be undertaken in these subgroups.

A Simple-to-Use Nomogram for Predicting the Survival of Early Hepatocellular Carcinoma Patients

Objective: This study aimed to develop and validate a simple-to-use nomogram for early hepatocellular carcinoma (HCC) patients undergoing a preoperative consultation and doctors conducting a postoperative evaluation.

Methods: A total of 2,225 HCC patients confirmed with stage I or II were selected from the Surveillance, Epidemiology, and End Results database between January 2010 and December 2015. The patients were randomly divided into two groups: a training group (n = 1,557) and a validation group (n = 668). Univariate and multivariate hazards regression analyses were used to identify independent prognostic factors. The Akaike information criterion (AIC) was used to select variables for the nomogram. The performance of the nomogram was validated concerning its ability of discrimination and calibration and its clinical utility.

Results: Age, alpha-fetoprotein (AFP), race, the degree of differentiation, and therapy method were significantly associated with the prognosis of early HCC patients. Based on the AIC results, five variables (age, race, AFP, degree of differentiation, and therapy method) were incorporated into the nomogram. The concordance indexes of the simple nomogram in the training and validation groups were 0.707 (95% CI: 0.683–0.731) and 0.733 (95% CI: 0.699–0.767), respectively. The areas under the receiver operating characteristic (ROC) curve of the nomogram in the training and validation groups were 0.744 and 0.764, respectively, for predicting 3-year survival, and 0.786 and 0.794, respectively, for predicting 5-year survival. Calibration plots showed good consistency between the predictions of the nomogram and the actual observations in both the training and validation groups. Decision curve analysis (DCA) showed that the simple nomogram was clinically useful, and the overall survival significantly differed between low- and high-risk groups divided by the median score of the nomogram in the training group (P < 0.001).

Conclusion: A simple-to-use nomogram based on a large population-based study is developed and validated, which is a conventional tool for doctors to facilitate the individual consultation of preoperative patients and the postoperative personalized evaluation.

A platform for artificial intelligence based identification of the extravasation potential of cancer cells into the brain metastatic niche

Brain metastases are the most lethal complication of advanced cancer; therefore, it is critical to identify when a tumor has the potential to metastasize to the brain. There are currently no interventions that shed light on the potential of primary tumors to metastasize to the brain. We constructed and tested a platform to quantitatively profile the dynamic phenotypes of cancer cells from aggressive triple negative breast cancer cell lines and patient derived xenografts (PDXs), generated from a primary tumor and brain metastases from tumors of diverse organs of origin. Combining an advanced live cell imaging algorithm and artificial intelligence, we profile cancer cell extravasation within a microfluidic blood-brain niche (μBBN) chip, to detect the minute differences between cells with brain metastatic potential and those without with a PPV of 0.91 in the context of this study. The results show remarkably sharp and reproducible distinction between cells that do and those which do not metastasize inside of the device.