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Zhang L, Zhang Q, Wu Q, Zhao L, Gao Y, Li X, Guan S, Yan M. Establishment of a prognostic nomogram for elderly patients with limited-stage small cell lung cancer receiving radiotherapy. Sci Rep 2024; 14:11990. [PMID: 38796503 PMCID: PMC11127957 DOI: 10.1038/s41598-024-62533-x] [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: 07/30/2023] [Accepted: 05/17/2024] [Indexed: 05/28/2024] Open
Abstract
The present study explored the risk factors associated with radiotherapy in seniors diagnosed with limited-stage small cell lung cancer (LS-SCLC) to construct and validate a prognostic nomogram. The study retrospectively included 137 elderly patients with LS-SCLC who previously received radiotherapy. Univariate and multivariate COX analyses were conducted to identify independent risk factors and determine optimal cut-off values. Kaplan-Meier survival curves and nomograms were constructed to predict survival. Calibration and receiver operating characteristic (ROC) curves were used to evaluate the accuracy and consistency of the nomogram. Illness rating scale-geriatric (CIRS-G) score, treatment strategy, lymphocyte-to-monocyte ratio (LMR), white blood cell-to-monocyte ratio (WMR), and prognostic nutritional index (PNI) were discovered to be independent prognostic factors. Based on the findings of our multivariate analysis, a risk nomogram was developed to assess patient prognosis. Internal bootstrap resampling was utilized to validate the model, and while the accuracy of the AUC curve at 1 year was modest at 0.657 (95% CI 0.458-0.856), good results were achieved in predicting 3- and 5 year survival with AUCs of 0.757 (95% CI 0.670-0.843) and 0.768 (95% CI 0.643-0.893), respectively. Calibration curves for 1-, 3-, and 5 year overall survival probabilities demonstrated good cocsistency between expected and actual outcomes. Patients with concurrent chemoradiotherapy, CIRS-G score > 5 points and low PNI, WMR and LMR correlated with poor prognosis. The nomogram model developed based on these factors demonstrated good predictive performance and provides a simple, accessible, and practical tool for clinicians to guide clinical decision-making and study design.
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Affiliation(s)
- Lixia Zhang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital,National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Qingfen Zhang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital,National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Qian Wu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital,National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Lujun Zhao
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital,National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China.
| | - Yunbin Gao
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital,National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Xue Li
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital,National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Song Guan
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital,National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
| | - Meng Yan
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital,National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
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Shah NK, Kim KN, Grewal A, Wang X, Ben-Josef E, Plastaras JP, Metz JM, Goel A, Taunk NK, Shabason JE, Lukens JN, Berman AT, Wojcieszynski AP. Activity Monitoring for Toxicity Detection and Management in Patients Undergoing Chemoradiation for Gastrointestinal Malignancies. JCO Oncol Pract 2022; 18:e896-e906. [PMID: 35157497 DOI: 10.1200/op.21.00671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Physical activity is associated with decreased hospitalization during cancer treatment. We hypothesize that activity data can help identify and triage high-risk patients with GI cancer undergoing concurrent chemoradiation. MATERIALS AND METHODS This prospective study randomly assigned patients to activity monitoring versus observation. In the intervention arm, a 20% decrease in daily steps or 20% increase in heart rate triggered triage visits to provide supportive care, medication changes, and escalation of care. In the observation group, activity data were recorded but not monitored. The primary objective was to show a 20% increase in triage visits in the intervention group. Secondary objectives were estimating the rates of emergency department (ED) visits and hospitalizations. Crude and adjusted odds ratios were computed using logistic regression modeling. RESULTS There were 22 patients in the intervention and 18 in the observation group. Baseline patient and treatment characteristics were similar. The primary objective was met, with 3.4 more triage visits in the intervention group than in the observation group (95% CI, 2.10 to 5.50; P < .0001). Twenty-six (65.0%) patients required at least one triage visit, with a higher rate in the intervention arm compared with that in the observation arm (86.4% v 38.9%; odds ratio, 9.95; 95% CI, 2.13 to 46.56; P = .004). There was no statistically significant difference in ED visit (9.1% v 22.2%; P = .38) or hospitalization (4.5% v 16.7%; P = .31). CONCLUSION It is feasible to use activity data to trigger triage visits for symptom management. Further studies are investigating whether automated activity monitoring can assist with early outpatient management to decrease ED visits and hospitalizations.
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Affiliation(s)
- Nishant K Shah
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Kristine N Kim
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Amardeep Grewal
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Xingmei Wang
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Edgar Ben-Josef
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - John P Plastaras
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - James M Metz
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Arun Goel
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Neil K Taunk
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jacob E Shabason
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - John N Lukens
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Abigail T Berman
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Andrzej P Wojcieszynski
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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