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Gomez D, Feng JJ, Cheok S, Shah I, Dicharry H, Cote DJ, Briggs RG, Guerra GA, Peterson R, Salhia B, Neman J, Attenello F, Chow F, Musabelliu EK, Zada G. Incidence of brain metastasis according to patient race and primary cancer origin: a systematic review. J Neurooncol 2024; 169:457-467. [PMID: 38896356 PMCID: PMC11341633 DOI: 10.1007/s11060-024-04748-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Accepted: 06/11/2024] [Indexed: 06/21/2024]
Abstract
PURPOSE A systematic review was conducted to investigate differences in incidence and primary origin of synchronous brain metastasis (sBM) in varying racial groups with different primary cancers. METHODS Adhering to PRISMA 2020 guidelines a search was conducted using PubMed and Ovid databases for publications from January 2000 to January 2023, with search terms including combinations of "brain metastasis," "race," "ethnicity," and "incidence." Three independent reviewers screened for inclusion criteria encompassing studies clearly reporting primary cancer sites, patient demographics including race, and synchronous BM (sBM) incidence. RESULTS Of 806 articles, 10 studies comprised of mainly adult patients from the United States met final inclusion for data analysis. Higher sBM incidence proportions were observed in American Indian/Alaska native patients for primary breast (p < 0.001), colorectal (p = 0.015), and esophageal cancers (p = 0.024) as well as in Asian or Pacific islanders for primary stomach (p < 0.001), thyroid (p = 0.006), and lung/bronchus cancers (p < 0.001) yet higher proportions in White patients for malignant melanoma (p < 0.001). Compared to White patients, Black patients had higher sBM incidence likelihood in breast cancer (OR = 1.27, p = 0.01) but lower likelihood in renal (OR = 0.46, p < 0.001) and esophageal cancers (OR = 0.31, p = 0.005). American Indian/Alaska native patients had a higher sBM likelihood (OR = 3.78, p = 0.004) relative to White patients in esophageal cancer. CONCLUSIONS These findings reveal several comparative racial differences in sBM incidence arising from different primary cancer origins, underscoring a need for further research to explain these variations. Identifying the factors contributing to these disparities holds the potential to promote greater equity in oncological care according to cancer type.
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Affiliation(s)
- David Gomez
- Department of Neurosurgery Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
| | - Jeffrey J Feng
- Western Michigan University Homer Stryker M.D. School of Medicine, Kalamazoo, MI, USA
| | - Stephanie Cheok
- Department of Neurological Surgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Ishan Shah
- Department of Neurosurgery Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Holly Dicharry
- LSU Health Shreveport School of Medicine, Louisiana State University, Shreveport, LA, 71103, USA
| | - David J Cote
- Department of Neurosurgery Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Robert G Briggs
- Department of Neurosurgery Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Gage A Guerra
- Department of Neurosurgery Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Racheal Peterson
- Department of Neurosurgery Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Bodour Salhia
- Department of Neurosurgery Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90033, USA
| | - Josh Neman
- Department of Neurosurgery Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90033, USA
| | - Frank Attenello
- Department of Neurosurgery Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Frances Chow
- Department of Neurosurgery Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
- Department of Neurology, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Erion K Musabelliu
- Krembil Research Institute, University Health Network, and University of Toronto, Toronto, ON, Canada
| | - Gabriel Zada
- Department of Neurosurgery Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
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Bashti M, DeLong C, Gurses ME, Lu VM, Merenzon M, Morell A, Daggubati L, Komotar RJ, Shah AH, Ivan ME. Surgical Management of Brain Metastasis from Esophageal Cancer: A Systematic Review and Single-Center Experience. World Neurosurg 2024; 187:e568-e576. [PMID: 38704143 DOI: 10.1016/j.wneu.2024.04.130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 04/21/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND Brain metastases from esophageal cancer (BMEC) are rare and aggressive, with limited literature on optimal treatment modalities and a standard of care yet to be established. The objective of this study was to systematically review existing literature and perform a retrospective analysis of our institution's patients to evaluate the influence of different treatment modalities on patient outcomes. METHODS A systematic review of the literature following Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines and a retrospective review of our institutional experience with BMEC were both conducted. Data based on mean survival,histology, metastasis location, and treatment modality were abstracted. RESULTS A total of 48 studies representing 136 patients with BMEC were identified, in addition to the 11 patients treated at our institution. There were a total of 100 males (12 unreported), with a median age of 62.2 at diagnosis in our systematic review, along with 8 males with a median age of 62 in our institutional review. Collectively, survival rates observed based on histology were not similar (squamous cell carcinoma: 9.2 months, adenocarcinoma: 13.4 months), however, based on treatment modalities (surgery: 11.6 months, radiation: 10.4 months, chemotherapy: 12.3 months), and metastasis location (supratentorial: 10.5 months, infratentorial: 9.9 months), the survival times were comparable. CONCLUSIONS Our review suggests that causes of death were often independent of brain metastases highlighting the need for further studies on early detection and prevention of primary esophageal cancer, as well as improved treatment modalities for BMECs.
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Affiliation(s)
- Malek Bashti
- Department of Neurosurgery, Miller School of Medicine, University of Miami, Miami, Florida, USA.
| | - Chase DeLong
- Department of Neurosurgery, Miller School of Medicine, University of Miami, Miami, Florida, USA
| | - Muhammet Enes Gurses
- Department of Neurosurgery, Miller School of Medicine, University of Miami, Miami, Florida, USA
| | - Victor M Lu
- Department of Neurosurgery, Miller School of Medicine, University of Miami, Miami, Florida, USA
| | - Martín Merenzon
- Department of Neurosurgery, Miller School of Medicine, University of Miami, Miami, Florida, USA
| | - Alexis Morell
- Department of Neurosurgery, Miller School of Medicine, University of Miami, Miami, Florida, USA
| | - Lekhaj Daggubati
- Department of Neurosurgery, Miller School of Medicine, University of Miami, Miami, Florida, USA
| | - Ricardo Jorge Komotar
- Department of Neurosurgery, Miller School of Medicine, University of Miami, Miami, Florida, USA; Sylvester Cancer Center, University of Miami Health System, Miami, Florida, USA
| | - Ashish H Shah
- Department of Neurosurgery, Miller School of Medicine, University of Miami, Miami, Florida, USA
| | - Michael E Ivan
- Department of Neurosurgery, Miller School of Medicine, University of Miami, Miami, Florida, USA; Sylvester Cancer Center, University of Miami Health System, Miami, Florida, USA
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Patel Z, Bhatia SS, Han P, McCloskey J, Kassar A. A rare case of tumor-to-tumor metastasis of esophageal adenocarcinoma into meningioma. Radiol Case Rep 2024; 19:2874-2878. [PMID: 38706816 PMCID: PMC11066990 DOI: 10.1016/j.radcr.2024.03.071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 03/18/2024] [Accepted: 03/25/2024] [Indexed: 05/07/2024] Open
Abstract
Tumor-to-tumor metastasis is a known but rare occurrence and is characterized as 2 distinct tumor types in same anatomic location. We present a rare case of intracranial tumor-to-tumor metastasis of esophageal adenocarcinoma into meningioma. Our case emphasizes the rare occurrence of intracranial tumor-to-tumor metastasis and importance of histology and immunohistochemical analysis in distinguishing between metastasis and meningioma, especially when faced with ambiguous demarcation. Awareness of this occurrence is crucial, given that metastases might be the initial indication of an underlying tumor and it can impact the clinical management decisions.
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Affiliation(s)
- Zalak Patel
- Department of Radiology; West Virginia University, Morgantown, WV, USA
| | - Smita S Bhatia
- School of Medicine, West Virginia University, Morgantown, WV, USA
| | - PengCheng Han
- Department of Pathology, Anatomy and Laboratory Medicine; West Virginia University, Morgantown, WV, USA
| | - Justin McCloskey
- Department of Radiology; West Virginia University, Morgantown, WV, USA
| | - Adam Kassar
- Department of Radiology; West Virginia University, Morgantown, WV, USA
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Wan J, Zeng Y. Prediction of hepatic metastasis in esophageal cancer based on machine learning. Sci Rep 2024; 14:14507. [PMID: 38914571 PMCID: PMC11196737 DOI: 10.1038/s41598-024-63213-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 05/27/2024] [Indexed: 06/26/2024] Open
Abstract
This study aimed to establish a machine learning (ML) model for predicting hepatic metastasis in esophageal cancer. We retrospectively analyzed patients with esophageal cancer recorded in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2020. We identified 11 indicators associated with the risk of liver metastasis through univariate and multivariate logistic regression. Subsequently, these indicators were incorporated into six ML classifiers to build corresponding predictive models. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. A total of 17,800 patients diagnosed with esophageal cancer were included in this study. Age, primary site, histology, tumor grade, T stage, N stage, surgical intervention, radiotherapy, chemotherapy, bone metastasis, and lung metastasis were independent risk factors for hepatic metastasis in esophageal cancer patients. Among the six models developed, the ML model constructed using the GBM algorithm exhibited the highest performance during internal validation of the dataset, with AUC, accuracy, sensitivity, and specificity of 0.885, 0.868, 0.667, and 0.888, respectively. Based on the GBM algorithm, we developed an accessible web-based prediction tool (accessible at https://project2-dngisws9d7xkygjcvnue8u.streamlit.app/ ) for predicting the risk of hepatic metastasis in esophageal cancer.
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Affiliation(s)
- Jun Wan
- Department of Emergency surgery, Yangtze University Jingzhou Hospital, jingzhou, China
| | - Yukai Zeng
- Department of Thoracic Surgery, China-Japan Union Hospital of Jilin University, No. 126 Xiantai street, Changchun, Jilin, China.
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Tian C, Li Y, Li M. Risk, Predictive Factors, and Nomogram of Liver Metastatic Gastroesophageal Junction Cancer: A New Study Based on the Surveillance, Epidemiology, and End Results Database. Cureus 2024; 16:e63429. [PMID: 39077222 PMCID: PMC11284346 DOI: 10.7759/cureus.63429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/28/2024] [Indexed: 07/31/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Liver metastases are associated with a poor prognosis in gastroesophageal junction (GEJ) cancer patients. The high rate of liver involvement is attributed to the unique anatomical location of the GEJ, which is close to the liver. Patients with liver metastasis typically have advanced, unresectable disease and limited treatment options. Therefore, early detection and prediction are crucial to guide appropriate treatment planning and improve the outcomes for patients with GEJ cancer at risk of liver metastases. Using data from the Surveillance, Epidemiology, and End Results (SEER) database, the present study aimed to elucidate the incidence and risk factors of liver metastases in GEJ cancer patients diagnosed between 2010 and 2019. METHODS This research employed univariable and multivariable logistic regression models to identify risk factors for the development of liver metastases. A predictive nomogram for liver metastases was developed and assessed. Patients' overall survival (OS) with liver metastases was analyzed using the Kaplan-Meier method. RESULTS The study included 1,322 eligible patients with GEJ cancer, 181 (13.6%) of whom were diagnosed with liver metastases. The median overall survival (mOS) for patients with liver metastasis was approximately eight months, compared to a shorter mOS for patients without liver metastasis (P < 0.001). Factors significantly associated with the occurrence of liver metastasis included N3 stage (OR: 1.84; 95% CI: (1.13-2.96); P < 0.001), surgery (OR: 0.09; 95% CI: (0.06-0.14); P < 0.001), lung metastasis (OR: 2.88; 95% CI: (1.78-4.63); P < 0.001), chemotherapy (OR: 0.54; 95% CI: (0.32-0.87); P < 0.001), and radiation therapy (OR: 0.33; 95% CI: (0.25-0.45); P < 0.001). The nomogram demonstrated good performance in predicting liver metastases in GEJ cancer patients (c-index: 0.820). CONCLUSIONS The study identified lymph node status, surgical, lung metastasis, chemotherapy, and radiation as important predictors of outcomes for patients with GEJ cancer. The developed nomogram might be a valuable tool for predicting the risk of liver metastases in GEJ cancer patients, potentially enhancing clinical decision-making processes. By predicting the risk of liver metastasis occurrence, clinicians might intervene in patients with GEJ cancers as early as possible.
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Affiliation(s)
- Chenrui Tian
- Oncology, Xinxiang Medical University, Xinxiang, CHN
| | - Yang Li
- Oncology, Xinxiang Medical University, Xinxiang, CHN
| | - Min Li
- Pathogenic Biology, School of Basic Medical Sciences, Xinxiang Medical University, Xinxiang, CHN
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Liu Y, Hu H, Han Y, Li Z, Yang J, Zhang X, Chen L, Chen F, Li W, Huang G. Development and external validation of a novel score for predicting postoperative 30‑day mortality in tumor craniotomy patients: A cross‑sectional diagnostic study. Oncol Lett 2024; 27:205. [PMID: 38516688 PMCID: PMC10956384 DOI: 10.3892/ol.2024.14338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 02/15/2024] [Indexed: 03/23/2024] Open
Abstract
The identification of patients with craniotomy at high risk for postoperative 30-day mortality may contribute to achieving targeted delivery of interventions. The present study aimed to develop a personalized nomogram and scoring system for predicting the risk of postoperative 30-day mortality in such patients. In this retrospective cross-sectional study, 18,642 patients with craniotomy were stratified into a training cohort (n=7,800; year of surgery, 2012-2013) and an external validation cohort (n=10,842; year of surgery, 2014-2015). The least absolute shrinkage and selection operator (LASSO) model was used to select the most important variables among the candidate variables. Furthermore, a stepwise logistic regression model was established to screen out the risk factors based on the predictors chosen by the LASSO model. The model and a nomogram were constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot analysis were used to assess the model's discrimination ability and accuracy. The associated risk factors were categorized according to clinical cutoff points to create a scoring model for postoperative 30-day mortality. The total score was divided into four risk categories: Extremely high, high, intermediate and low risk. The postoperative 30-day mortality rates were 2.43 and 2.58% in the training and validation cohort, respectively. A simple nomogram and scoring system were developed for predicting the risk of postoperative 30-day mortality according to the white blood cell count; hematocrit and blood urea nitrogen levels; age range; functional health status; and incidence of disseminated cancer cells. The ROC AUC of the nomogram was 0.795 (95% CI: 0.764 to 0.826) in the training cohort and it was 0.738 (95% CI: 0.7091 to 0.7674) in the validation cohort. The calibration demonstrated a perfect fit between the predicted 30-day mortality risk and the observed 30-day mortality risk. Low, intermediate, high and extremely high risk statuses for 30-day mortality were associated with total scores of (-1.5 to -1), (-0.5 to 0.5), (1 to 2) and (2.5 to 9), respectively. A personalized nomogram and scoring system for predicting postoperative 30-day mortality in adult patients who underwent craniotomy were developed and validated, and individuals at high risk of 30-day mortality were able to be identified.
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Affiliation(s)
- Yufei Liu
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong 518035, P.R. China
- Shenzhen University Health Science Center, Shenzhen University, Shenzhen, Guangdong 518000, P.R. China
| | - Haofei Hu
- Shenzhen University Health Science Center, Shenzhen University, Shenzhen, Guangdong 518000, P.R. China
- Department of Nephrology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong 518035, P.R. China
| | - Yong Han
- Shenzhen University Health Science Center, Shenzhen University, Shenzhen, Guangdong 518000, P.R. China
- Department of Emergency, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong 518035, P.R. China
| | - Zongyang Li
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong 518035, P.R. China
- Shenzhen University Health Science Center, Shenzhen University, Shenzhen, Guangdong 518000, P.R. China
| | - Jihu Yang
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong 518035, P.R. China
- Shenzhen University Health Science Center, Shenzhen University, Shenzhen, Guangdong 518000, P.R. China
| | - Xiejun Zhang
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong 518035, P.R. China
- Shenzhen University Health Science Center, Shenzhen University, Shenzhen, Guangdong 518000, P.R. China
| | - Lei Chen
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong 518035, P.R. China
- Shenzhen University Health Science Center, Shenzhen University, Shenzhen, Guangdong 518000, P.R. China
| | - Fanfan Chen
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong 518035, P.R. China
- Shenzhen University Health Science Center, Shenzhen University, Shenzhen, Guangdong 518000, P.R. China
| | - Weiping Li
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong 518035, P.R. China
- Shenzhen University Health Science Center, Shenzhen University, Shenzhen, Guangdong 518000, P.R. China
| | - Guodong Huang
- Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong 518035, P.R. China
- Shenzhen University Health Science Center, Shenzhen University, Shenzhen, Guangdong 518000, P.R. China
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He W, Yu Y, Yan Z, Luo N, Yang W, Li F, Jin H, Zhang Y, Ma X, Ma M. Nomograms for Predicting Risk and Survival of Esophageal Cancer Lung Metastases: a SEER Analysis. J Cancer 2024; 15:3370-3380. [PMID: 38817873 PMCID: PMC11134440 DOI: 10.7150/jca.92389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 04/07/2024] [Indexed: 06/01/2024] Open
Abstract
Background: The overall survival rate is notably low for esophageal cancer patients with lung metastases (LM), presenting significant challenges in their treatment. Methods: Through the Surveillance, Epidemiology, and End Results (SEER) program, individuals diagnosed with esophageal cancer between 2010 and 2015 were enrolled. Based on whether esophageal cancer metastasized to the lungs, we used propensity score matching (PSM) to balance correlated variables. Propensity score matching was a critical step in our study that helped to minimize the impact of possible confounders on the study results. We balanced variables related to lung metastases using the PSM method to ensure more accurate comparisons between the study and control groups. Specifically, we performed PSM in the following steps. First, we performed a univariate logistic regression analysis to screen for variables associated with lung metastasis. For each patient, we calculated their propensity scores using a logistic regression model, taking into account several factors, including gender, T-stage, N-stage, surgical history, radiotherapy history, chemotherapy history, and bone/brain/liver metastases. We used a 1:1 matching ratio based on the propensity score to ensure more balanced baseline characteristics between the study and control groups after matching. After matching, we validated the balance of baseline characteristics to ensure that the effect of confounders was minimized. We used logistic regression to identify risk variables for LM, while Cox regression was used to find independent prognostic factors. We then created nomograms and assessed their accuracy using the calibration curve, receiver operating curves (ROC), and C index. Results: In the post-PSM cohort, individuals diagnosed with LM experienced a median overall survival (OS) of 5.0 months (95% confidence interval [CI] 4.3-5.7), which was significantly lower than those without LM (P<0.001). LM has been associated to sex, T stage, N stage, surgery, radiation, chemotherapy, and bone/brain/liver metastases. LM survival was affected by radiation, chemotherapy, and bone/liver metastases. The nomograms' predictive power was proved using the ROC curve, C-index, and validation curve. Conclusion: Patients with LM have a worse chance of surviving esophageal cancer. The nomograms can effectively predict the risk and prognosis of lung metastases from esophageal cancer.
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Affiliation(s)
- Wenhui He
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
- School of Nursing, Gansu University of Traditional Chinese Medicine, Lanzhou730000, Gansu Province, China
| | - Youzhen Yu
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
- School of Nursing, Gansu University of Traditional Chinese Medicine, Lanzhou730000, Gansu Province, China
| | - Ziting Yan
- School of Nursing, Gansu University of Traditional Chinese Medicine, Lanzhou730000, Gansu Province, China
| | - Na Luo
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
- School of Nursing, Gansu University of Traditional Chinese Medicine, Lanzhou730000, Gansu Province, China
| | - Wenwen Yang
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
- The First Clinical Medical College, Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Fanfan Li
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
- School of Nursing, Gansu University of Traditional Chinese Medicine, Lanzhou730000, Gansu Province, China
| | - Hongying Jin
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
- School of Nursing, Gansu University of Traditional Chinese Medicine, Lanzhou730000, Gansu Province, China
| | - Yimei Zhang
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
- School of Nursing, Gansu University of Traditional Chinese Medicine, Lanzhou730000, Gansu Province, China
| | - Xiaoli Ma
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
- Gansu Province International Cooperation Base for Research and Application of Key technology of Thoracic Surgery, The First Hospital of Lanzhou University, Lanzhou, 730000, Gansu Province, China
| | - Minjie Ma
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
- Gansu Province International Cooperation Base for Research and Application of Key technology of Thoracic Surgery, The First Hospital of Lanzhou University, Lanzhou, 730000, Gansu Province, China
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Baccili Cury Megid T, Baskurt Z, Ma LX, Barron CC, Farooq A, Saltiel MP, Wang X, Bach Y, Ayoama H, Jang RW, Chen E, Veit-Haibach P, Wang B, Kalimuthu S, Cotton J, Wong R, Mesci A, Elimova E. Leptomeningeal carcinomatosis and brain metastases in gastroesophageal carcinoma: a real-world analysis of clinical and pathologic characteristics and outcomes. J Neurooncol 2024; 167:111-122. [PMID: 38372902 PMCID: PMC10978709 DOI: 10.1007/s11060-024-04576-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 01/16/2024] [Indexed: 02/20/2024]
Abstract
BACKGROUND Brain metastasis (BrM) and Leptomeningeal Carcinomatosis (LMC) are uncommon complications in gastroesophageal carcinoma (GEC) patients. These patients have a poor prognosis and are challenging to treat. We described the clinicopathologic features and outcomes in the largest cohort of Central Nervous System (CNS) metastasis in GEC patients. METHODS single-center retrospective study of GEC treated from 2007 to 2021. Clinicopathologic characteristics and treatment modalities were reviewed. Survival was calculated from the date of CNS diagnosis until date of death/last follow-up using the Kaplan-Meier method. A multivariable Cox proportional hazards regression model was used. RESULTS Of 3283 GEC patients, 100 (3.04%) were diagnosed with BrM and 20 with LMC (0.61%). Patients with known human epidermal growth factor receptor 2 (HER2) status (N = 48), 60% were HER2 positive (defined as IHC 3 + or IHC 2+/FISH+). Among LMC patients most were signet-ring subtype (85%), and only 15% (2/13) were HER2 positive. Median survival was 0.7; 3.8; and 7.7 months in BrM patients treated with best supportive care, radiation, and surgery, respectively (p < 0.001). In LMC, median survival was 0.7 month in patients who had best supportive care (7/19) and 2.8 months for those who had whole brain radiation therapy (p = 0.015). Multivariate analysis showed worse outcomes in ECOG ≥ 2 (p = 0.002), number of BrM ≥ 4 (p < 0.001) and number of metastatic sites (p = 0.009). CONCLUSION HER2 expression were enriched in patients with BrM, while it is uncommon in LMC. Patients treated with surgery followed by radiation had an improved OS in BrM and WBRT benefited patients with LMC.
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Affiliation(s)
| | - Zeynep Baskurt
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Lucy X Ma
- Medical Oncology and Hematology at Princess Margaret Cancer Centre, Toronto, Canada
| | - Carly C Barron
- Medical Oncology and Hematology at Princess Margaret Cancer Centre, Toronto, Canada
| | - Abdul Farooq
- Medical Oncology and Hematology at Princess Margaret Cancer Centre, Toronto, Canada
| | | | - Xin Wang
- Medical Oncology and Hematology at Princess Margaret Cancer Centre, Toronto, Canada
| | - Yvonne Bach
- Department of Medical Oncology and Hematology, University of Toronto, Toronto, Canada
| | - Hiroko Ayoama
- Department of Medical Oncology and Hematology, University of Toronto, Toronto, Canada
| | - Raymond W Jang
- Medical Oncology and Hematology at Princess Margaret Cancer Centre, Toronto, Canada
| | - Eric Chen
- Medical Oncology and Hematology at Princess Margaret Cancer Centre, Toronto, Canada
| | - Patrick Veit-Haibach
- Toronto Joint Department Medical Imaging and University Health Network, Sinai Health System, University Medical Imaging Toronto, Women's College Hospital, Toronto, Canada
| | - Ben Wang
- Department of Pathology, Princess Margaret Cancer Centre, Toronto, Canada
| | | | - James Cotton
- Department of Pathology, Princess Margaret Cancer Centre, Toronto, Canada
| | - Rebecca Wong
- Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Canada
| | - Aruz Mesci
- Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Canada
| | - Elena Elimova
- Medical Oncology and Hematology at Princess Margaret Cancer Centre, Toronto, Canada.
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Liang K, Feliciano JL, Marrone KA, Murray JC, Hann CL, Anagnostou V, Tackett SA, Shin EJ, Hales RK, Voong KR, Battafarano RJ, Yang SC, Broderick SR, Ha JS, Forde PM, Brahmer JR, Lam VK. Clinical features and outcomes of advanced HER2+ esophageal/GEJ cancer with brain metastasis. ESMO Open 2024; 9:102199. [PMID: 38071928 PMCID: PMC10837776 DOI: 10.1016/j.esmoop.2023.102199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/30/2023] [Accepted: 11/15/2023] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Brain metastasis (BRM) is uncommon in gastroesophageal cancer. As such, clinicopathologic and molecular determinants of BRM and impact on clinical outcome remain incompletely understood. METHODS We retrospectively analyzed clinicopathologic data from advanced esophageal/gastroesophageal junction (E/GEJ) patients at Johns Hopkins from 2003 to 2021. We investigated the association between several clinical and molecular features and the occurrence of BRM, with particular focus on human epidermal growth factor receptor 2 (HER2) overexpression. Survival outcomes and time to BRM onset were also evaluated. RESULTS We included 515 patients with advanced E/GEJ cancer. Tumors were 78.3% esophageal primary, 82.9% adenocarcinoma, 31.0% HER2 positive. Cumulative incidence of BRM in the overall cohort and within HER2+ subgroup was 13.8% and 24.3%, respectively. HER2 overexpression was associated with increased risk of BRM [odds ratio 2.45; 95% confidence interval (CI) 1.10-5.46]. On initial presentation with BRM, 50.7% had a solitary brain lesion and 11.3% were asymptomatic. HER2+ status was associated with longer median time to onset of BRM (14.0 versus 6.3 months, P < 0.01), improved median progression free survival on first-line systemic therapy (hazard ratio 0.35, 95% CI 0.16-0.80), and improved median overall survival (hazard ratio 0.20, 95% CI 0.08-0.54) in patients with BRM. CONCLUSION HER2 overexpression identifies a gastroesophageal cancer molecular subtype that is significantly associated with increased risk of BRM, though with later onset of BRM and improved survival likely reflecting the impact of central nervous system-penetrant HER2-directed therapy. The prevalence of asymptomatic and solitary brain lesions suggests that brain surveillance for HER2+ patients warrants prospective investigation.
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Affiliation(s)
- K Liang
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA
| | - J L Feliciano
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA
| | - K A Marrone
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA
| | - J C Murray
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA
| | - C L Hann
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA
| | - V Anagnostou
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA
| | - S A Tackett
- Department of Medicine, Biostatistics, Epidemiology and Data Management (BEAD) Core, Johns Hopkins University School of Medicine, Baltimore, USA
| | - E J Shin
- Department of Gastroenterology & Hepatology, Johns Hopkins University School of Medicine, Baltimore, USA
| | - R K Hales
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, USA
| | - K R Voong
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, USA
| | - R J Battafarano
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA
| | - S C Yang
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA
| | - S R Broderick
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA
| | - J S Ha
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA
| | - P M Forde
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA
| | - J R Brahmer
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA
| | - V K Lam
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA.
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Hu Z, Yang S, Xu Z, Zhang X, Wang H, Fan G, Liao X. Prevalence and risk factors of bone metastasis and the development of bone metastatic prognostic classification system: a pan-cancer population study. Aging (Albany NY) 2023; 15:13134-13149. [PMID: 37983179 DOI: 10.18632/aging.205224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 10/12/2023] [Indexed: 11/22/2023]
Abstract
BACKGROUND The prevalence of bone metastasis (BM) varies among primary cancer patients, and it has a significant impact on prognosis. However, there is a lack of research in this area. This study aims to explore the clinical characteristics, prevalence, and risk factors, and to establish a prognostic classification system for pan-cancer patients with BM. METHODS The data obtained from the Surveillance, Epidemiology and End Results database were investigated. The prevalence and prognosis of patients with BM were analyzed. Hierarchical clustering was used to develop a prognostic classification system. RESULTS From 2010 to 2019, the prevalence of BM has increased by 41.43%. BM most commonly occurs in cancers that originate in the adrenal gland, lung and bronchus and overlapping lesion of digestive systems. Negative prognostic factors included older age, male sex, poorer grade, unmarried status, low income, non-metropolitan living, advanced tumor stages, previous chemotherapy, and synchronous liver, lung, and brain metastasis. Three categories with significantly different survival time were identified in the classification system. CONCLUSIONS The clinical features, prevalence, risk factors, and prognostic factors in pan-cancer patients with BM were investigated. A prognostic classification system was developed to provide survival information and aid physicians in selecting personalized treatment plans for patients with BM.
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Affiliation(s)
- Zhouyang Hu
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China
| | - Sheng Yang
- Department of Orthopedics, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
- Spinal Pain Research Institute, Tongji University School of Medicine, Shanghai, China
| | - Zhipeng Xu
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China
| | - Xiaoling Zhang
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China
| | - Hong Wang
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China
| | - Guoxin Fan
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China
| | - Xiang Liao
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China
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11
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Singh R, Bowden G, Mathieu D, Perlow HK, Palmer JD, Elhamdani S, Shepard M, Liang Y, Nabeel AM, Reda WA, Tawadros SR, Abdelkarim K, El-Shehaby AMN, Emad RM, Elazzazi AH, Warnick RE, Gozal YM, Daly M, McShane B, Addis-Jackson M, Karthikeyan G, Smith S, Picozzi P, Franzini A, Kaisman-Elbaz T, Yang HC, Wei Z, Legarreta A, Hess J, Templeton K, Pikis S, Mantziaris G, Simonova G, Liscak R, Peker S, Samanci Y, Chiang V, Niranjan A, Kersh CR, Lee CC, Trifiletti DM, Lunsford LD, Sheehan JP. Local Control and Survival Outcomes After Stereotactic Radiosurgery for Brain Metastases From Gastrointestinal Primaries: An International Multicenter Analysis. Neurosurgery 2023; 93:592-598. [PMID: 36942965 DOI: 10.1227/neu.0000000000002456] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 01/17/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND There are limited data regarding outcomes for patients with gastrointestinal (GI) primaries and brain metastases treated with stereotactic radiosurgery (SRS). OBJECTIVE To examine clinical outcomes after SRS for patients with brain metastases from GI primaries and evaluate potential prognostic factors. METHODS The International Radiosurgery Research Foundation centers were queried for patients with brain metastases from GI primaries managed with SRS. Primary outcomes were local control (LC) and overall survival (OS). Kaplan-Meier analysis was used for univariate analysis (UVA) of prognostic factors. Factors significant on UVA were evaluated with a Cox multivariate analysis proportional hazards model. Logistic regressions were used to examine correlations with RN. RESULTS We identified 263 eligible patients with 543 brain metastases. Common primary sites were rectal (31.2%), colon (31.2%), and esophagus (25.5%) with a median age of 61.6 years (range: 37-91.4 years) and a median Karnofsky performance status (KPS) of 90% (range: 40%-100%). One-year and 2-year LC rates were 83.5% (95% CI: 78.9%-87.1%) and 73.0% (95% CI: 66.4%-78.5%), respectively. On UVA, age >65 years ( P = .001), dose <20 Gy ( P = .006) for single-fraction plans, KPS <90% ( P < .001), and planning target volume ≥2cc ( P = .007) were associated with inferior LC. All factors other than dose were significant on multivariate analysis ( P ≤ .002). One-year and 2-year OS rates were 68.0% (95% CI: 61.5%-73.6%) and 31.2% (95% CI: 24.6%-37.9%), respectively. Age > 65 years ( P = .006), KPS <90% ( P = .005), and extracranial metastases ( P = .05) were associated with inferior OS. CONCLUSION SRS resulted in comparable LC with common primaries. Age and KPS were associated with both LC and OS with planning target volume and extracranial metastases correlating with LC and OS, respectively. These factors should be considered in GI cancer patient selection for SRS.
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Affiliation(s)
- Raj Singh
- Department of Radiation Oncology, Virginia Commonwealth University Health System, Richmond, Virginia, USA
| | - Greg Bowden
- Department of Neurosurgery, University of Alberta, Edmonton, Canada
| | - David Mathieu
- Department of Neurosurgery, Université de Sherbrooke, Sherbrooke, Canada
| | - Haley K Perlow
- Departments of Radiation Oncology and Neurosurgery, The James Cancer Hospital and Solove Research Institute, Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Joshua D Palmer
- Departments of Radiation Oncology and Neurosurgery, The James Cancer Hospital and Solove Research Institute, Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Shahed Elhamdani
- Department of Neurosurgery, Allegheny Health Network, Pittsburgh, Pennsylvania, USA
| | - Matthew Shepard
- Department of Neurosurgery, Allegheny Health Network, Pittsburgh, Pennsylvania, USA
| | - Yun Liang
- Department of Radiation Oncology, Allegheny Health Network, Pittsburgh, Pennsylvania, USA
| | - Ahmed M Nabeel
- Department of Neurosurgery, Gamma Knife Center, Nasser Institute Hospital, Cairo, Egypt
- Department of Neurosurgery, Benha University, Banha, Egypt
| | - Wael A Reda
- Department of Neurosurgery, Gamma Knife Center, Nasser Institute Hospital, Cairo, Egypt
- Department of Neurosurgery, Ain Shams University, Cairo, Egypt
| | - Sameh R Tawadros
- Department of Neurosurgery, Gamma Knife Center, Nasser Institute Hospital, Cairo, Egypt
- Department of Neurosurgery, Ain Shams University, Cairo, Egypt
| | - Khaled Abdelkarim
- Department of Neurosurgery, Gamma Knife Center, Nasser Institute Hospital, Cairo, Egypt
- Department of Clinical Oncology, Ain Shams University, Cairo, Egypt
| | - Amr M N El-Shehaby
- Department of Neurosurgery, Gamma Knife Center, Nasser Institute Hospital, Cairo, Egypt
- Department of Neurosurgery, Ain Shams University, Cairo, Egypt
| | - Reem M Emad
- Department of Neurosurgery, Gamma Knife Center, Nasser Institute Hospital, Cairo, Egypt
- Department of Radiation Oncology, National Cancer Institute, Cairo University, Giza City, Egypt
| | | | - Ronald E Warnick
- Department of Neurosurgery, Gamma Knife Center, Jewish Hospital, Mayfield Clinic, Cincinnati, Ohio, USA
| | - Yair M Gozal
- Department of Neurosurgery, Gamma Knife Center, Jewish Hospital, Mayfield Clinic, Cincinnati, Ohio, USA
| | - Megan Daly
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Brendan McShane
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Marcel Addis-Jackson
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Gokul Karthikeyan
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sian Smith
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Piero Picozzi
- Department of Neurosurgery, Humanitas Research Hospital - IRCCS, Rozzano, Italy
| | - Andrea Franzini
- Department of Neurosurgery, Humanitas Research Hospital - IRCCS, Rozzano, Italy
| | - Tehila Kaisman-Elbaz
- Department of Neurosurgery, Rose Ella Burkhart Brain Tumor and Neuro-Oncology Center, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Huai-Che Yang
- Department of Neurosurgery, Neurological Institute, Taipei Veteran General Hospital, Taipei, China
- Department of Neurosurgery, National Yang Ming Chiao Tung University School of Medicine, Taipei, China
| | - Zhishuo Wei
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Andrew Legarreta
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Judith Hess
- Department of Neurosurgery, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Kelsey Templeton
- Department of Neurosurgery, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Stylianos Pikis
- Department of Neurological Surgery, University of Virginia, Charlottesville, Virginia, USA
| | - Georgios Mantziaris
- Department of Neurological Surgery, University of Virginia, Charlottesville, Virginia, USA
| | - Gabriela Simonova
- Department of Stereotactic and Radiation Neurosurgery, Na Homolce Hospital, Prague, Czechia
| | - Roman Liscak
- Department of Stereotactic and Radiation Neurosurgery, Na Homolce Hospital, Prague, Czechia
| | - Selcuk Peker
- Department of Neurosurgery, Koc University School of Medicine, Istanbul, Turkey
| | - Yavuz Samanci
- Department of Neurosurgery, Koc University School of Medicine, Istanbul, Turkey
| | - Veronica Chiang
- Department of Neurosurgery, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Ajay Niranjan
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Charles R Kersh
- Department of Radiation Oncology, University of Virginia, Charlottesville, Virginia, USA
| | - Cheng-Chia Lee
- Department of Neurosurgery, Neurological Institute, Taipei Veteran General Hospital, Taipei, China
- Department of Neurosurgery, National Yang Ming Chiao Tung University School of Medicine, Taipei, China
| | - Daniel M Trifiletti
- Department of Radiation Oncology, Mayo Clinic Florida, Jacksonville, Florida, USA
| | - L Dade Lunsford
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Jason P Sheehan
- Department of Neurological Surgery, University of Virginia, Charlottesville, Virginia, USA
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12
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Sun S, Yang W, Yang Y, Fan M, Wang F, He L, Han B, Chen C. Nomogram for predicting survival after lymphatic metastasis in esophageal cancer: A SEER analysis. Medicine (Baltimore) 2023; 102:e34189. [PMID: 37543759 PMCID: PMC10402951 DOI: 10.1097/md.0000000000034189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/07/2023] Open
Abstract
Lymphatic metastasis (LM) is a significant mechanism for the spread of esophageal cancer (EC) and predicts the poor prognosis of EC patients. This research aimed to assess the survival of patients with LM from EC by developing a nomogram. In this retrospective study, EC patients with LM from 2004 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were divided by year of diagnosis into a training cohort and a validation cohort. Univariate and multivariate Cox regression analyses were employed to determine the prognostic factors of LM, and a nomogram was constructed. The discrimination and calibration of the nomogram were compared by the C-index, area under the curve value, and calibration plots. The survival time difference was compared using Kaplan-Meier curves. A total of 11,695 patients with EC were included in this analysis. LM occurred in 56.5% (n = 6614) of EC patients. In the post-propensity score matching (PSM) cohort, patients with LM had significantly lower median overall survival (OS) than those without LM. Multivariate Cox regression was used to identify the eleven independent prognostic factors. The C-index was 0.709 in both the training and test sets, revealing the good predictive performance of the nomogram. Based on the results of calibration plots and the receiver operating characteristic (ROC) curve, we demonstrate the great performance of the prognostic model. The survival time of EC patients with LM was remarkably lower than that of EC patients without LM. The nomogram model established in this study can precisely predict the survival of EC patients with LM.
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Affiliation(s)
- Shuo Sun
- The First Clinical Medical College, Lanzhou University, Lanzhou, Gansu Province, China
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou, Gansu Province, China
| | - Wenwen Yang
- The First Clinical Medical College, Lanzhou University, Lanzhou, Gansu Province, China
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou, Gansu Province, China
| | - Yanjiang Yang
- Qilu hospital of Shandong University, Shandong University, Jinan, Shandong province, China
| | - Mengmeng Fan
- School of Management, Lanzhou University, Lanzhou, Gansu Province, China
| | - Feng Wang
- The First Clinical Medical College, Lanzhou University, Lanzhou, Gansu Province, China
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou, Gansu Province, China
| | - Li He
- The First Clinical Medical College, Lanzhou University, Lanzhou, Gansu Province, China
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou, Gansu Province, China
| | - Biao Han
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou, Gansu Province, China
- Gansu Province International Cooperation Base for Research and Application of Key Technology of Thoracic Surgery, The First Hospital of Lanzhou University, Lanzhou, Gansu Province, China
| | - Chang Chen
- The First Clinical Medical College, Lanzhou University, Lanzhou, Gansu Province, China
- Department of Thoracic Surgery, the First Hospital of Lanzhou University, Lanzhou, Gansu Province, China
- Gansu Province International Cooperation Base for Research and Application of Key Technology of Thoracic Surgery, The First Hospital of Lanzhou University, Lanzhou, Gansu Province, China
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13
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Taori S, Wei Z, Deng H, Hadjipanayis CG, Lunsford LD, Niranjan A. Stereotactic radiosurgery for patients with brain metastases from gastroesophageal cancers. J Neurooncol 2023; 164:147-155. [PMID: 37470878 DOI: 10.1007/s11060-023-04392-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 07/06/2023] [Indexed: 07/21/2023]
Abstract
BACKGROUND The prognosis of patients with brain metastases from gastroesophageal (GE) cancers remains unclear despite recent advances in systemic therapies. The authors present a large single-institution experience in the use of stereotactic radiosurgery (SRS). METHODS A retrospective review of 71 GE cancer patients (64 male, 90.14%) who underwent Gamma Knife SRS was conducted. Overall, 243 brain metastases were treated and the median number of metastases per patient was 2 (range:1-21). The median age at SRS was 66 years (range: 26-85) and the median treatment day KPS was 80 (range: 50-100). The median cumulative tumor volume was 6.7 cc (range: 0.27-104.76) and the median single-session margin dose was 18 Gy (range: 12-20). RESULTS The median overall survival after SRS was 7 months (range: 1-64). At last follow up, 54 (76.06%) patients were deceased, 8 of whom (14.81%) expired secondary to their intracranial metastases. Four patients (5.63%) experienced local tumor progression at a median time of 8 months (range: 2-13) after SRS. Ten patients (14%) experienced new remote tumor development at a median time of 4 months (range: 0-14) after SRS. Whole-brain radiation therapy (2 patients, 20%) and repeat SRS (8 patients, 80%) were used for newly developed tumors. The incidence of transient adverse radiation effects was 8.45%. CONCLUSIONS In this study, the 12-month local tumor control rate was 90%. Incidences of adverse radiation effect rates were rare. The median overall survival of 7 months indicates the poor prognosis of patients with brain spread of their GE cancer.
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Affiliation(s)
- Suchet Taori
- School of Medicine, University of Pittsburgh Medical Center, Pennsylvania, PA, USA
| | - Zhishuo Wei
- School of Medicine, University of Pittsburgh Medical Center, Pennsylvania, PA, USA
| | - Hansen Deng
- School of Medicine, University of Pittsburgh Medical Center, Pennsylvania, PA, USA
| | | | - L Dade Lunsford
- School of Medicine, University of Pittsburgh Medical Center, Pennsylvania, PA, USA
| | - Ajay Niranjan
- Department of Neurological Surgery, University of Pittsburgh Medical Center, 200 Lothrop Street, Pittsburgh, PA, 15213, USA.
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14
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Hao Y, Li G. Prediction of distant organ metastasis and overall survival of lung cancer patients: a SEER population-based cohort study. Front Oncol 2023; 13:1075385. [PMID: 37377915 PMCID: PMC10291234 DOI: 10.3389/fonc.2023.1075385] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 05/30/2023] [Indexed: 06/29/2023] Open
Abstract
Background Distant organ metastasis is a common event in lung cancer (LC). However, the preferential metastatic pattern of different pathological types of LC and its effect on prognosis have not been comprehensively elucidated. This study aimed to explore the distant metastasis pattern and construct nomograms predicting the metastasis and survival of LC patients using the Surveillance, Epidemiology, and End Results (SEER) database. Methods LC data were downloaded from the SEER database to conduct logistic regression and investigate risk factors for developing organ metastasis. A Cox regression analysis was conducted to investigate prognostic factors of LC. A Kaplan-Meier analysis was used to estimate overall survival outcomes. Nomograms were constructed to predict the probability of organ metastasis and the 1-, 3- and 5-year survival probability of LC patients. Receiver operating characteristic curves were used to evaluate the diagnostic accuracy of the nomograms. All statistical analyses were conducted within R software. Results The liver is the most common metastatic organ of small cell carcinoma. The brain is the most likely metastasis site of large cell carcinoma, and bone is the most likely metastasis site for squamous cell carcinoma and adenocarcinoma. Patients with triple metastases (brain-bone-liver) have the worst prognosis, and for nonsquamous carcinoma with single organ metastasis, liver metastases conferred the worst prognosis. Our nomograms based on clinical factors could predict the metastasis and prognosis of LC patients. Conclusion Different pathological types of LC have different preferential metastatic sites. Our nomograms showed good performance in predicting distant metastasis and overall survival. These results will provide a reference for clinicians and contribute to clinical evaluations and individualized therapeutic strategies.
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15
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Qiu B, Shen Z, Yang D, Wang Q. Applying machine learning techniques to predict the risk of lung metastases from rectal cancer: a real-world retrospective study. Front Oncol 2023; 13:1183072. [PMID: 37293595 PMCID: PMC10247137 DOI: 10.3389/fonc.2023.1183072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 05/11/2023] [Indexed: 06/10/2023] Open
Abstract
Background Metastasis in the lungs is common in patients with rectal cancer, and it can have severe consequences on their survival and quality of life. Therefore, it is essential to identify patients who may be at risk of developing lung metastasis from rectal cancer. Methods In this study, we utilized eight machine-learning methods to create a model for predicting the risk of lung metastasis in patients with rectal cancer. Our cohort consisted of 27,180 rectal cancer patients selected from the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2017 for model development. Additionally, we validated our models using 1118 rectal cancer patients from a Chinese hospital to evaluate model performance and generalizability. We assessed our models' performance using various metrics, including the area under the curve (AUC), the area under the precision-recall curve (AUPR), the Matthews Correlation Coefficient (MCC), decision curve analysis (DCA), and calibration curves. Finally, we applied the best model to develop a web-based calculator for predicting the risk of lung metastasis in patients with rectal cancer. Result Our study employed tenfold cross-validation to assess the performance of eight machine-learning models for predicting the risk of lung metastasis in patients with rectal cancer. The AUC values ranged from 0.73 to 0.96 in the training set, with the extreme gradient boosting (XGB) model achieving the highest AUC value of 0.96. Moreover, the XGB model obtained the best AUPR and MCC in the training set, reaching 0.98 and 0.88, respectively. We found that the XGB model demonstrated the best predictive power, achieving an AUC of 0.87, an AUPR of 0.60, an accuracy of 0.92, and a sensitivity of 0.93 in the internal test set. Furthermore, the XGB model was evaluated in the external test set and achieved an AUC of 0.91, an AUPR of 0.63, an accuracy of 0.93, a sensitivity of 0.92, and a specificity of 0.93. The XGB model obtained the highest MCC in the internal test set and external validation set, with 0.61 and 0.68, respectively. Based on the DCA and calibration curve analysis, the XGB model had better clinical decision-making ability and predictive power than the other seven models. Lastly, we developed an online web calculator using the XGB model to assist doctors in making informed decisions and to facilitate the model's wider adoption (https://share.streamlit.io/woshiwz/rectal_cancer/main/lung.py). Conclusion In this study, we developed an XGB model based on clinicopathological information to predict the risk of lung metastasis in patients with rectal cancer, which may help physicians make clinical decisions.
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Affiliation(s)
- Binxu Qiu
- Department of Gastric and Colorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, China
| | - Zixiong Shen
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Dongliang Yang
- Department of Gastric and Colorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, China
| | - Quan Wang
- Department of Gastric and Colorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, China
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16
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Li D, Zhang M, Zhang H. Survival Prediction Models for Ovarian Cancer Patients with Lung Metastasis: A Retrospective Cohort Study Based on SEER Database. INT J COMPUT INT SYS 2023. [DOI: 10.1007/s44196-023-00196-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023] Open
Abstract
AbstractTo develop a random forest prediction model for the and short- and long-term survival of ovarian cancer patients with lung metastasis. This retrospective cohort study enrolled primary ovarian cancer patients with lung metastasis from the surveillance, epidemiology and end results (SEER) database (2010–2015). All eligible women were randomly divided into the training (n = 1357) and testing set (n = 582). The outcomes were 1-, 3- and 5-year survival. Predictive factors were screened by random forest analysis. The prediction models for predicting the 1-, 3- and 5-year survival were conducted using the training set, and the internal validation was carried out by the testing set. The performance of the models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). The subgroups based on the pathological classification further assessed the model’s performance. Totally 1345 patients suffered from death within 5 years. The median follow-up was 7.00 (1.00, 21.00) months. Age at diagnosis, race, marital status, tumor size, tumor grade, TNM stage, brain metastasis, liver metastasis, bone metastasis, etc. were predictors. The AUCs of the prediction model for the 1-, 3-, 5-year survival in the testing set were 0.849 [95% confidence interval (CI): 0.820–0.884], 0.789 (95% CI 0.753–0.826) and 0.763 (95% CI 0.723–0.802), respectively. The results of subgroups on different pathological classifications showed that the AUCs of the model were over 0.7. This random forest model performed well predictive ability for the short- and long-term survival of ovarian cancer patients with lung metastasis, which may be beneficial to identify high-risk individuals for intelligent medical services.
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Risk and prognostic factors of brain metastasis in lung cancer patients: a Surveillance, Epidemiology, and End Results population‑based cohort study. Eur J Cancer Prev 2023:00008469-990000000-00038. [PMID: 36804864 PMCID: PMC10373853 DOI: 10.1097/cej.0000000000000790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
BACKGROUND Brain is a common metastasis site in lung cancer patients. However, homogeneous and heterogeneous risk/prognostic factors of brain metastasis for lung cancer patients have not been comprehensively elucidated. This study aimed to explore the brain metastasis risk and prognostic factors in lung cancer patients using the Surveillance, Epidemiology, and End Results (SEER) database. METHODS Lung cancer data were downloaded from SEER database to investigate risk factors for developing brain metastasis using logistic regression analysis. Univariate and multivariate Cox analyses were used to identify potential prognostic factors. Kaplan-Meier analysis was conducted to evaluate the survival. Propensity score matching was conducted to eliminate baseline differences between two groups. RESULTS A total of 10 818 (14.1%) patients with brain metastasis were diagnosed among 76 483 lung cancer patients. For non-small-cell lung carcinoma (NSCLC), distant liver/bone/lymph node metastases, higher T, N stages were risk factors. Black race, bone metastases and distant lymph node metastases and T4 were brain metastasis risk factors for SCLC patients. Cox analysis suggested that older age, male, primary lesion at main bronchus, liver/ bone/distant lymph node metastases, T2-4, N1-3, no surgery/chemotherapy/radiotherapy were associated with worse prognosis of NSCLC-brain metastasis patients. Age older than 80, liver/bone metastases, without radiotherapy and chemotherapy were associated with worse prognosis of SCLC-brain metastasis patients. Surgery of primary site could prolong the overall survival (OS) of NSCLC patients with brain metastasis, but not SCLC. CONCLUSION In this study, we analyzed the homogeneous and heterogeneous risk/prognostic factors of brain metastasis in lung cancer patients. What is more, our results showed that surgery of primary site was associated with longer OS of NSCLC patients with brain metastasis.
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Luo P, Wu J, Chen X, Yang Y, Zhang R, Qi X, Li Y. A population-based investigation: How to identify high-risk T1-2N0 esophageal cancer patients? Front Surg 2023; 9:1003487. [PMID: 36733675 PMCID: PMC9888256 DOI: 10.3389/fsurg.2022.1003487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 10/31/2022] [Indexed: 01/09/2023] Open
Abstract
Purpose Newly diagnosed T1-2N0 esophageal cancer (EC) is generally deemed as early local disease, with distant metastases (DM) easily overlooked. This retrospective study aimed to describe the metastatic patterns, identify risk factors and established a risk prediction model for DM in T1-2N0 EC patients. Methods A total of 4623 T1-2N0 EC patients were identified in the Surveillance, Epidemiology and End Results (SEER) database from 2004 to 2018. Multivariable logistic regression was used to identify risk factors for DM. A nomogram was developed for presentation of the final model. Results Of 4623 T1-2N0 patients, 4062 (87.9%) had M0 disease and 561 (12.1%) had M1 disease. The most common metastatic site was liver (n = 156, 47.3%), followed by lung (n = 89, 27.0%), bone (n = 70, 21.2%) and brain (n = 15, 4.5%). Variables independently associated with DM included age at diagnosis, gender, tumor grade, primary site, tumor size and T stage. A nomogram based on the variables had a good predictive accuracy (area under the curve: 0.750). Independent risk factors for bone metastases (BoM), brain metastases (BrM), liver metastases (LiM) and lung metastases (LuM) were identified, respectively. Conclusions We identified independent predictive factors for DM, as well as for BoM, BrM, LiM and LuM. Above all, a practical and convenient nomogram with a great accuracy to predict DM probability for T1-2N0 EC patients was established.
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Affiliation(s)
- Peng Luo
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie Wu
- Department of Urology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiankai Chen
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yafan Yang
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ruixiang Zhang
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiuzhu Qi
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yin Li
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China,Correspondence: Yin Li
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Yang Y, He C. Construction and validation of a nomogram model to predict the overall survival rate of esophageal cancer patients receiving neoadjuvant chemotherapy: A population-based study. Front Surg 2023; 9:1066092. [PMID: 36743892 PMCID: PMC9894093 DOI: 10.3389/fsurg.2022.1066092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 11/01/2022] [Indexed: 01/20/2023] Open
Abstract
Introduction The development of neoadjuvant chemotherapy(nCT) improves the overall survival (OS) of patients with esophageal cancer(EC). The aim of this study was to determine the independent prognostic factors of EC patients receiving nCT, and to construct a nomogram model for predicting OS. Method This retrospective analysis was conducted from the National Cancer Institute's Surveillance Epidemiology and End Results, Clinicopathological data of patients with EC who received nCT from 2004 to 2015. The included patients were randomly divided into the training cohort and the validation cohort. Univariate and multivariate Cox proportional hazards models were used to analyze the patients in the training cohort to determine the independent prognostic factors. Based on the independent prognostic variables, nomogram models for 1-year, 2-year and 3-year OS were constructed. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the discriminative ability. The calibration curves, decision curve analysis (DCA) and Kaplan-Meier (K-M) survival analysis were used to evaluate the predictive accuracy and clinical application value. Results A total of 2,493 patients were enrolled, with 1,748 patients in the training cohort and 745 patients in the validation cohort. Gender, marital status, tumor pathological grade, T stage, N stage, and M stage were identified as independent prognostic factor (P < 0.05). A novel nomogram model was constructed. ROC curve analysis revealed that the model had moderate predictive performance, which was better than that of the AJCC TNM staging system.The calibration curves showed a high agreement between the actual observed values and the predicted values. The DCA suggested that the newly constructed prediction model had good clinical application value. K-M survival analysis showed that the model was helpful to accurately distinguish the prognosis of patients with different risk levels. Conclusions Gender, tumor pathological grade, marital status, T stage, N stage and M stage were identified as independent prognostic factors for overall survival of patients with esophageal cancer who received neoadjuvant chemotherapy. A nomogram prediction model was established, which was helpful to accurately and reliably predict the overall survival rate of patients with esophageal cancer who received neoadjuvant chemotherapy at 1, 2 and 3 years.
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Wei X, Luo P, Chen X, Wang Z, Xu L, Xie H, Yang Y, Zhang R, Yu Y, Li H, Liu Q, Qin J, Li Y. Is it necessary for patients with potentially resectable esophageal squamous cell cancer to receive routine preoperative brain MRI/CT? Thorac Cancer 2022; 13:3304-3309. [PMID: 36226869 PMCID: PMC9715792 DOI: 10.1111/1759-7714.14686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 09/23/2022] [Accepted: 09/26/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND This study aimed to investigate the value and efficiency of routine brain MRI or CT in the preoperative workup for patients with potentially resectable (cT1-4a N0-3 ) thoracic esophageal squamous cell cancer (ESCC). METHODS This was a prospective cross-sectional clinical trial (ChiCTR1800020304). A total of 385 patients with potentially resectable (cT1-4a N0-3 ) thoracic ESCC diagnosed from October 2018 to August 2020 were included. Plain brain MRI or CT was performed preoperatively to detect brain metastases (BrM). The primary endpoint was BrM detected by imaging. RESULTS Of all 385 patients, the rate of positive brain MRI/CT findings was 1% (n = 4). BrM Patients received chemoradiotherapy, and the median OS was 6 months (95% CI: 4.303-7.697). All 381 remaining patients with initial negative brain MRI/CT diagnosis revealed no brain-associated symptoms within 6 months. The median follow-up for patients without BrM was 20 months (range, from 6 to 32). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of plain MRI or CT to detect BrM were all 100%. CONCLUSIONS Preoperative plain MRI or CT is an effective method to detect BrM for potentially resectable (cT1-4a N0-3 ) thoracic ESCC. However, due to the low incidence, the value of brain MRI/CT as a routinely preoperational examination in potentially resectable esophageal squamous cell cancer is rather limited. Therefore, preoperative brain MRI/CT should not be recommended as a routine preoperative examination for ESCC.
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Affiliation(s)
- Xiufeng Wei
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical SciencesPeking Union Medical CollegeBeijingChina
| | - Peng Luo
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical SciencesPeking Union Medical CollegeBeijingChina
| | - Xiankai Chen
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical SciencesPeking Union Medical CollegeBeijingChina
| | - Zhen Wang
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical SciencesPeking Union Medical CollegeBeijingChina
| | - Lei Xu
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical SciencesPeking Union Medical CollegeBeijingChina
| | - Hounai Xie
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical SciencesPeking Union Medical CollegeBeijingChina
| | - Yafan Yang
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical SciencesPeking Union Medical CollegeBeijingChina
| | - Ruixiang Zhang
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical SciencesPeking Union Medical CollegeBeijingChina
| | - Yongkui Yu
- Department of Thoracic SurgeryThe Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer HospitalZhengzhouChina
| | - Haomiao Li
- Department of Thoracic SurgeryThe Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer HospitalZhengzhouChina
| | - Qi Liu
- Department of Thoracic SurgeryThe Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer HospitalZhengzhouChina
| | - Jianjun Qin
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical SciencesPeking Union Medical CollegeBeijingChina
| | - Yin Li
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical SciencesPeking Union Medical CollegeBeijingChina
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Luo P, Wei X, Liu C, Chen X, Yang Y, Zhang R, Kang X, Qin J, Qi X, Li Y. The risk and prognostic factors for liver metastases in esophageal cancer patients: A large-cohort based study. Thorac Cancer 2022; 13:2960-2969. [PMID: 36168908 PMCID: PMC9626357 DOI: 10.1111/1759-7714.14642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/23/2022] [Accepted: 08/24/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND This retrospective study aimed to explore risk factors for liver metastases (LiM) in patients with esophageal cancer (EC) and to identify prognostic factors in patients initially diagnosed with LiM. METHODS A total of 28 654 EC patients were retrieved from the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2018. A multivariate logistic regression model was utilized to identify risk factors for LiM. A Cox regression model was used to identify prognostic factors for patients with LiM. RESULTS Of 28 654 EC patients, 4062 (14.2%) had LiM at diagnosis. The median overall survival (OS) for patients with and without LiM was 6.00 (95% CI: 5.70-6.30) months and 15.00 (95% CI: 14.64-15.36) months, respectively. Variables significantly associated with LiM included gender, age, tumor site, histology, tumor grade, tumor size, clinical T stage, clinical N stage, bone metastases (BoM), brain metastases (BrM) and lung metastases (LuM). Variables independently predicting survival for EC patients with LiM were age, histology, tumor grade, BoM, BrM, LuM, and chemotherapy. A risk prediction model and two survival prediction models were then constructed revealing satisfactory predictive accuracy. CONCLUSIONS Based on the largest known cohort of EC, independent predictors of LiM and prognostic indicators of survival for patients with LiM were identified. Two models for predicting survival as well as a risk prediction model were developed with robust predictive accuracy.
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Affiliation(s)
- Peng Luo
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Xiufeng Wei
- Department of Thoracic Surgery, Beijing Chuiyangliu HospitalChuiyangliu Hospital Affiliated to Tsinghua UniversityBeijingChina
| | - Chen Liu
- Department of Ophthalmology, Shanghai Changhai HospitalNaval Military Medical UniversityShanghaiChina
| | - Xiankai Chen
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Yafan Yang
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Ruixiang Zhang
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Xiaozheng Kang
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Jianjun Qin
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Xiuzhu Qi
- Department of UltrasoundFudan University Shanghai Cancer CenterShanghaiChina,Department of Oncology, Shanghai Medical CollegeFudan UniversityShanghaiChina
| | - Yin Li
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
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Tsai C, Nguyen B, Luthra A, Chou JF, Feder L, Tang LH, Strong VE, Molena D, Jones DR, Coit DG, Ilson DH, Ku GY, Cowzer D, Cadley J, Capanu M, Schultz N, Beal K, Moss NS, Janjigian YY, Maron SB. Outcomes and Molecular Features of Brain Metastasis in Gastroesophageal Adenocarcinoma. JAMA Netw Open 2022; 5:e2228083. [PMID: 36001319 PMCID: PMC9403772 DOI: 10.1001/jamanetworkopen.2022.28083] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
IMPORTANCE Brain metastasis (BrM) in gastroesophageal adenocarcinoma (GEA) is a rare and poorly understood phenomenon associated with poor prognosis. OBJECTIVES To examine the clinical and genomic features of patients with BrM from GEA and evaluate factors associated with survival. DESIGN, SETTING, AND PARTICIPANTS In this single-institution retrospective cohort study, 68 patients with BrM from GEA diagnosed between January 1, 2008, and December 31, 2020, were identified via review of billing codes and imaging reports from the electronic medical record with follow-up through November 3, 2021. Genomic data were derived from the Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets clinical sequencing platform. EXPOSURES Treatment with BrM resection and/or radiotherapy. MAIN OUTCOMES AND MEASURES Overall survival after BrM diagnosis. RESULTS Sixty-eight patients (median age at diagnosis, 57.4 years [IQR, 49.8-66.4 years]; 59 [86.8%] male; 55 [85.9%] White) participated in the study. A total of 57 (83.8%) had primary tumors in the distal esophagus or gastroesophageal junction. Median time from initial diagnosis to BrM diagnosis was 16.9 months (IQR, 8.5-27.7 months). Median survival from BrM diagnosis was 8.7 months (95% CI, 5.5-11.5 months). Overall survival was 35% (95% CI, 25%-48%) at 1 year and 24% (95% CI, 16%-37%) at 2 years. In a multivariable analysis, an Eastern Cooperative Oncology Group performance status of 2 or greater (hazard ratio [HR], 4.66; 95% CI, 1.47-14.70; P = .009) and lack of surgical or radiotherapeutic intervention (HR, 7.71; 95% CI, 2.01-29.60; P = .003) were associated with increased risk of all-cause mortality, whereas 3 or more extracranial sites of disease (HR, 1.85; 95% CI, 0.64-5.29; P = .25) and 4 or more BrMs (HR, 2.15; 95% CI, 0.93-4.98; P = .07) were not statistically significant. A total of 31 patients (45.6%) had ERBB2 (formerly HER2 or HER2/neu)-positive tumors, and alterations in ERBB2 were enriched in BrM relative to primary tumors (8 [47.1%] vs 7 [20.6%], P = .05), as were alterations in PTPRT (7 [41.2%] vs 4 [11.8%], P = .03). CONCLUSIONS AND RELEVANCE This study suggests that that a notable proportion of patients with BrM from GEA achieve survival exceeding 1 and 2 years from BrM diagnosis, a more favorable prognosis than previously reported. Good performance status and treatment with combination surgery and radiotherapy were associated with the best outcomes. ERBB2 positivity and amplification as well as PTPRT alterations were enriched in BrM tissue compared with primary tumors; therefore, further study should be pursued to identify whether these variables represent genomic risk factors for BrM development.
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Affiliation(s)
- Charlton Tsai
- Department of Medicine, New York Presbyterian/Weill Cornell Medicine, New York, New York
| | - Bastien Nguyen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Anisha Luthra
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Joanne F. Chou
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Lara Feder
- Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Laura H. Tang
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Vivian E. Strong
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Daniela Molena
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - David R. Jones
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Daniel G. Coit
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - David H. Ilson
- Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Geoffrey Y. Ku
- Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Darren Cowzer
- Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - John Cadley
- Department of Digital Informatics and Technology Solutions, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Marinela Capanu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Nikolaus Schultz
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kathryn Beal
- Department of Radiation Oncology and Brain Metastasis Center, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Nelson S. Moss
- Department of Neurosurgery and Brain Metastasis Center, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Yelena Y. Janjigian
- Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Steven B. Maron
- Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
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