Incidence and Prognosis Nomogram of Small Solitary Lung Cancer (≤2 cm) With Extra-Thoracic Metastasis at Initial Diagnosis:A Population-Based Study

Hu, Ao'ran; Chen, Zui; Liu, Chaoyuan; Gao, Yuan; Deng, Chao; Liu, Xianling

doi:10.1177/10732748221141560

Cited by 3 publications

(1 citation statement)

References 32 publications

(35 reference statements)

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“…Nomogram is a convenient and accurate method to predict survival outcomes, which has become very popular in malignant tumor survival prediction in recent years [ 16 ]. While many predictive models for extrapulmonary metastasis in NSCLC have been established, and their accuracy and discrimination had been validated by internal and external data [ 17 – 19 ], there is a dearth of research on intrapulmonary metastasis with predictive models, and the sample size of prognostic studies for these patients is relatively small. To fill this gap, we plan to construct a nomogram that can accurately predict the survival of these patients using data from the Surveillance, Epidemiology, and End Results (SEER) database.…”

Section: Introductionmentioning

confidence: 99%

Prognostic factors and survival prediction of resected non-small cell lung cancer with ipsilateral pulmonary metastases: a study based on the Surveillance, Epidemiology, and End Results (SEER) database

Zhang,

Zhang

2023

BMC Pulm Med

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Background Prognostic factors and survival outcomes of non-small cell lung cancer (NSCLC) with Ipsilateral pulmonary metastasis (IPM) are not well-defined. Thus, this study intended to identify the prognostic factors for these patients and construct a predictive nomogram model. Methods One thousand, seven hundred thirty-two patients with IPM identified between 2000 to 2019 were from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors were identified using multivariate Cox regression analyses. Nomograms were constructed to predict the overall survival (OS), C-index, the area under the curve (AUC), and the calibration curve to determine the predictive accuracy and discrimination; the decision curve analysis was used to confirm the clinical utility. Results Patients were randomly divided into training (n = 1213) and validation (n = 519) cohorts. In the training cohort, the multivariable analysis demonstrated that age, sex, primary tumor size, N status, number of regional lymph nodes removed, tumor grade, and chemotherapy were independent prognostic factors for IPM. We constructed a 1-year, 3-year, and 5-year OS prediction nomogram model using independent prognostic factors. The C-index of this model for OS prediction was 0.714 (95% confidence interval [CI], 0.692 to 0.773) in the training cohort and 0.695 (95% CI, 0.660 to 0.730) in the validation cohort. Based on the AUC of the receiver operating characteristic analysis, calibration plots, and decision curve analysis, we concluded that the prognosis model of IPM exhibited excellent performance. Patients with total nomogram points greater than 96 were considered high-risk. Conclusion We constructed and internally validated a nomogram to predict 1-year, 3-year, and 5-year OS for NSCLC patients with IPM according to independent prognostic factors. This nomogram demonstrated good calibration, discrimination, clinical utility, and practical decision-making effects for the prognosis of NSCLC patients with IPM.

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Section: Introductionmentioning

confidence: 99%

Prognostic factors and survival prediction of resected non-small cell lung cancer with ipsilateral pulmonary metastases: a study based on the Surveillance, Epidemiology, and End Results (SEER) database

Zhang,

Zhang

2023

BMC Pulm Med

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Advances in diagnosis and prediction for aggression of pure solid T1 lung cancer

Mu,

Huang,

et al. 2023

Precision Clinical Medicine

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A growing number of early-stage lung cancers presenting as malignant pulmonary nodules have been diagnosed because of the increased adoption of low-dose spiral computed tomography. But pure solid T1 lung cancer with ≤3 cm in the greatest dimension is not always at an early stage, despite its small size. This type of cancer can be highly aggressive and is associated with pathological involvement, metastasis, postoperative relapse, and even death. However, it is easily misdiagnosed or delay diagnosed in clinics and thus poses a serious threat to human health. The percentage of nodal or extrathoracic metastases has been reported to be >20% in T1 lung cancer. As such, understanding and identifying the aggressive characteristics of pure solid T1 lung cancer is crucial for prevention, diagnosis, and therapeutic strategies, and beneficial to improving the prognosis. With the widespread of lung cancer screening, these highly invasive pure solid T1 lung cancer will become the main advanced lung cancer in future. However, there is limited information regarding precision medicine on how to identify these “early-stage” aggressive lung cancers. To provide clinicians with new insights into early recognition and intervention of the highly invasive pure solid T1 lung cancer, this review summarizes its clinical characteristics, imaging, pathology, gene alterations, immune microenvironment, multi-omics, and current techniques for diagnosis and prediction.

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A nomogram model for diagnosing bone metastasis in category T1 Lung Adenocarcinoma

Li,

Gan,

Wang

et al. 2024

Preprint

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Rationale and Objectives: Bone metastasis (BM) significantly affects the prognosis of lung adenocarcinoma (LUAD) patients. Currently, no effective clinical model exists for predicting early BM in category T1 LUAD. This study aims to develop a model for timely BM detection by analyzing relevant influencing factors. Materials and Methods This retrospective study analyzed data from 478 patients with category T1 LUAD from August 2017 to August 2023. Of these, 334 patients were assigned to a training cohort and 144 to an internal validation cohort. Univariate and multivariate analyses identified BM risk factors, leading to a nomogram model. Model performance was evaluated using area under the curve (AUC), calibration curves, and decision curve analysis (DCA). An online calculator was also created to assess BM risk. Results Multivariate analysis revealed that alkaline phosphatase (ALP), carcinoembryonic antigen (CEA), nodule type, CT-reported N staging, and pleural effusion are independent BM risk factors. The nomogram showed strong accuracy, with AUC values of 0.929 in the training cohort and 0.954 in the validation cohort. Calibration analyses confirmed reliability, with DCA indicating high clinical benefit for both cohorts. Conclusion This nomogram effectively identifies high-risk patients for BM in category T1 LUAD, aiding personalized clinical decision-making.

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Incidence and Prognosis Nomogram of Small Solitary Lung Cancer (≤2 cm) With Extra-Thoracic Metastasis at Initial Diagnosis:A Population-Based Study

Cited by 3 publications

References 32 publications

Prognostic factors and survival prediction of resected non-small cell lung cancer with ipsilateral pulmonary metastases: a study based on the Surveillance, Epidemiology, and End Results (SEER) database

Prognostic factors and survival prediction of resected non-small cell lung cancer with ipsilateral pulmonary metastases: a study based on the Surveillance, Epidemiology, and End Results (SEER) database

Advances in diagnosis and prediction for aggression of pure solid T1 lung cancer

A nomogram model for diagnosing bone metastasis in category T1 Lung Adenocarcinoma

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