Prognosis and predictors of site of first metastasis after definitive radiation therapy for non-small cell lung cancer

Tang, Chad; Liao, Zhongxing; Hess, Kenneth R.; Chance, William W.; Zhuang, Yan; Jensen, G.L.; Xu, Ting; Komaki, Ritsuko; Gomez, Daniel R.

doi:10.3109/0284186x.2016.1154602

Cited by 22 publications

(13 citation statements)

References 26 publications

(33 reference statements)

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“…Cox et al demonstrated nonsquamous histology to be associated with DM from a pooled analysis of four Radiation Therapy Oncology Group (RTOG) trials including 1765 patients. Similar results were con rmed by Tang et al, who also identi ed nonsquamous histology and advanced initial disease stage predicted metastasis at any site [38,39]. In addition, compared with SCC, the risk of DM is higher in AC [40].…”

Section: Discussionsupporting

confidence: 82%

Radiomics Nomograms for Predicting Different Patterns of Distant Metastases After Definitive Chemoradiotherapy for Locally Advanced Non-Small-Cell Lung Cancer

Tong¹,

Chen²,

Chen³

et al. 2020

Preprint

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Background: To develop and validate radiomics-based nomograms for predicting different patterns of distant metastases (DMs), including oligometastases and polymetastases, after definitive chemoradiotherapy (CRT) for locally advanced non-small-cell lung cancer (LA-NSCLC).Methods: In all, 139 LA-NSCLC patients treated with definitive CRT between January 2014 and December 2017 were analyzed retrospectively. Computed tomography (CT) radiomics features were quantitatively extracted by 3D Slicer software. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied for date dimension reduction, feature selection and radiomics signature development. Univariate and multivariate Cox regression analyses were utilized to identify independent predictors of DMs. Finally, a nomogram that incorporates both the radiomics signature and independent clinical risk factors was developed and assessed according to the Harrell concordance index (C-index), calibration curve and clinical usefulness.Results: With a median follow-up of 20.6 months, DMs were detected by follow-up imaging in 47 patients (26 patients had oligometastatic disease). For those who experienced DM, the first metastases were most common in the lung (32.1%), followed by the brain (20.7%), and bone (15.1%). The median time to DMs was 13.5 months. Twelve radiomics features were found to be predictive for DMs. Multivariate Cox proportional hazards testing revealed that histology subtype and overall stage were independent clinical risk factors for DMs (p-value < 0.05). The radiomics nomogram combining the selected radiomics signature, histology subtype and overall stage predicted DMs better than either the radiomics signature or clinical factors alone (p-value < 0.001). The model showed good discrimination and good calibration. Furthermore, the radiomics signature consisting of nine selected features was significantly associated with oligometastases (p-value < 0.001). The radiomics-based nomogram showed strong discrimination ability for the prediction of oligometastatic disease. The C-index was 0.800 (95% CI= 0.710 to 0.890) for the primary cohort, and 0.828 (95% CI= 0.686 to 0.970) for the validation cohort.Conclusion: The above-described radiomics nomograms can precisely predict DM and the oligometastatic state in patients with LA-NSCLC, which may constitute a useful clinical tool to guide subsequent personalized treatments.

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Section: Discussionsupporting

confidence: 82%

Radiomics Nomograms for Predicting Different Patterns of Distant Metastases After Definitive Chemoradiotherapy for Locally Advanced Non-Small-Cell Lung Cancer

Tong¹,

Chen²,

Chen³

et al. 2020

Preprint

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“…Furthermore, we observed an increased frequency of bone and pleural metastasis in the DLM group. In a study of 1074 patients with non-metastatic NSCLC treated with radiation therapy, the four most frequent sites of initial distant metastasis were brain (146/456), lung/effusion (125/456), bone (98/456), and liver (63/456) [23]. Thus, brain and bone seem more likely first sites of distant metastasis than liver.…”

Section: Discussionmentioning

confidence: 99%

“…However, in the study by Hoang et al, liver metastasis was identified as a poor prognostic factor in patients with stage IIIB or IV NSCLC [4]. Furthermore, liver or bone metastasis as the first site of distant metastasis after radiation therapy for NSCLC was found to be associated with worse prognosis [23]. The results of these studies were not consistent, and the studies were performed in the era prior to EGFR mutation testing and TKIs.…”

Section: Discussionmentioning

confidence: 99%

The impact of de novo liver metastasis on clinical outcome in patients with advanced non-small-cell lung cancer

et al. 2017

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Liver metastasis has been found to affect outcome in prostate cancer and colorectal cancer, but its role in lung cancer is unclear. The current study aimed to evaluate the impact of de novo liver metastasis (DLM) on stage IV non-small cell lung cancer (NSCLC) outcomes and to examine whether tyrosine kinase inhibitors (TKI) reverse poor prognosis in patients with DLM and epidermal growth factor receptor (EGFR)-mutant NSCLC. Among 1392 newly diagnosed NSCLC patients, 490 patients with stage IV disease treated between November 2010 and March 2014 at Kaohsiung Chang Gung Memorial Hospital were included. Patients were divided into two groups according to DLM status. There were 75 patients in the DLM group and 415 patients in the non-DLM group. The DLM group included more patients with bone metastasis, fewer patients with a lymphocyte-to-monocyte ratio (LMR) > 3.1, and fewer patients with pleural metastasis. In the DLM group, Eastern Cooperative Oncology Group performance status 3–4 and LMR ≦3.1 were associated with poor outcome. In patients without DLM, overall survival (OS) was longer in patients with EGFR-mutant NSCLC than in those without (20.2 vs. 7.3 months, p < 0.001). Among DLM patients, OS was similar between the EGFR-mutant and wild-type EGFR tumor subgroups (11.9 vs. 7.7 months, p = 0.155). We found that DLM was a significant poor prognostic factor in the EGFR-mutant patients treated with EGFR-TKIs, whereas DLM did not affect the prognosis of EGFR-wild-type patients.

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“…Besides common metrics like tumor volume, more information can be extracted from medical images using radiomics, potentially able to more accurately monitor tumor changes over the course of treatment. Previously, radiomics has been shown to be able to predict outcome for NSCLC patients using CT imaging [6,8,19,29]. Moreover, a recent study showed that similar prognostic information can be derived using CBCT imaging [12].…”

Section: Discussionmentioning

confidence: 97%

“…This is the principle of extracting large amounts of quantitative features from medical images acquired in clinical practice. Combining different types of information, i.e., radiomics, genomics, demographics, models can be developed to predict treatment outcome and improve treatment decisions [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Feature selection methodology for longitudinal cone-beam CT radiomics

et al. 2017

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This study describes a feature selection methodology for longitudinal radiomics that is able to select reproducible delta radiomics features that are informative due to their change during treatment, which can potentially be used for treatment decisions concerning adaptive radiotherapy. Nonetheless, the prognostic value of the selected delta radiomic features should be investigated in future studies.

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Prognosis and predictors of site of first metastasis after definitive radiation therapy for non-small cell lung cancer

Cited by 22 publications

References 26 publications

Radiomics Nomograms for Predicting Different Patterns of Distant Metastases After Definitive Chemoradiotherapy for Locally Advanced Non-Small-Cell Lung Cancer

Radiomics Nomograms for Predicting Different Patterns of Distant Metastases After Definitive Chemoradiotherapy for Locally Advanced Non-Small-Cell Lung Cancer

The impact of de novo liver metastasis on clinical outcome in patients with advanced non-small-cell lung cancer

Feature selection methodology for longitudinal cone-beam CT radiomics

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