Dataset Information

MRI radiomic signature predicts intracranial progression-free survival in patients with brain metastases of ALK-positive non-small cell lung cancer.

ABSTRACT:

Background

Intracranial progression is considered an important cause of treatment failure in anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer (NSCLC) patients. Recent advances in targeted therapy and radiomics have generated considerable interest for the exploration of prognostic imaging biomarkers to predict the clinical course. Here, we developed a magnetic resonance imaging (MRI) radiomic signature that can stratify survival and intracranial progression.

Methods

We analyzed 87 brain metastatic lesions in 24 ALK-positive NSCLC patients undergoing ALK-inhibitor ensartinib therapy and divided them into training (n=61) and validation (n=26) sets. Radiomic features were extracted and screened from contrast-enhanced MR images. Combined with these selected features, the Rad-score was calculated with multivariate logistic regression. The predictive model and Rad-score performance were assessed in the training set and validated in the validation set; decision curve analysis was performed with the combined training and validation sets to estimate Rad-score's patient-stratification ability.

Results

The prediction model constructed with nine selected radiomic features could predict intracranial progression within 51 weeks (AUC =0.84 and 0.85 in the training and validation sets, respectively), while clinical and regular MRI characteristics were independent of progression (P>0.05). The decision-curve analysis showed that the radiomic prediction model was clinically useful. The Kaplan-Meier analysis showed that the progression-free survival (PFS) difference between the high- and low-risk groups distinguished by the Rad-score was significant (P=0.017).

Conclusions

Radiomics may provide prognostic information and improve pretreatment risk stratification in ALK-positive NSCLC patients with brain metastases undergoing ensartinib treatment, allowing follow-up and treatment to be tailored to the patient's individual risk profile.

SUBMITTER: Zhao S

PROVIDER: S-EPMC7867779 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

MRI radiomic signature predicts intracranial progression-free survival in patients with brain metastases of ALK-positive non-small cell lung cancer.

Zhao Shijun S Hou Donghui D Zheng Xiaomin X Song Wei W Liu Xiaoqing X Wang Sicong S Zhou Lina L Tao Xiuli X Lv Lv L Sun Qi Q Jin Yujing Y Ding Lieming L Mao Li L Wu Ning N

Translational lung cancer research 20210101 1

<h4>Background</h4>Intracranial progression is considered an important cause of treatment failure in anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer (NSCLC) patients. Recent advances in targeted therapy and radiomics have generated considerable interest for the exploration of prognostic imaging biomarkers to predict the clinical course. Here, we developed a magnetic resonance imaging (MRI) radiomic signature that can stratify survival and intracranial progression.<h4>Methods ...[more]

PMID: 33569319

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Project description:BackgroundCentral nervous system (CNS) metastases in patients with ALK-positive non-small cell lung cancer (NSCLC) are a cause of substantial morbidity and mortality. Although alectinib had demonstrated promising intracranial efficacy in several clinical trials, data were limited on its CNS activity in real-world settings.MethodsIn this retrospective study, ALK-positive NSCLC patients with brain metastases (BM) or leptomeningeal metastases (LM) from six hospitals in China were divided into three cohorts based on the treatment history before the administration of alectinib. ALK-TKI-naive patients were enrolled in cohort 1, cohort 2 included patients who experienced intracranial progression with or without extracranial progression after treatment with crizotinib, and cohort 3 included patients who developed progression only in CNS following treatment with other second-generation ALK-TKIs. The definition and evaluation of intracranial and extracranial lesions were based on Response Evaluation Criteria in Solid Tumors version 1.1.ResultsSixty-five patients were eligible and included in our study (cohort 1: 20, cohort 2: 32, cohort 3: 13). For the overall population and patients with uncontrolled CNS metastases, similar intracranial response in CNS target lesions was observed: cohort 1: 81.8% and 80%; cohort 2: 76.5% and 86.7%; cohort 3: 42.8% and 33.3%. For patients in these three cohorts, 75% (6/8), 78.6% (11/14), and 83.3% (5/6) were reported to have significant improvement in CNS-related symptoms respectively. The number of patients who were in need of mannitol or corticosteroids decreased remarkably after the treatment of alectinib (p < 0.001), and there was also a steep fall-over in the number of patients with ECOG ≥2 points before and after the administration of alectinib (p = 0.003). All patients (8/8) diagnosed with LM ± BM experienced substantial alleviation in CNS-related symptoms. In cohort 1 and cohort 2, no significant difference in CNS-time to progression was found between patients with symptomatic or asymptomatic BM when treated with alectinib alone.ConclusionsOur study substantiated the potent CNS activity of alectinib in real-world settings. Patients with symptomatic and asymptomatic BM could benefit from alectinib comparatively, which indicated that alectinib alone might defer the timing of local treatment. However, our results should be treated cautiously owing to limited sample size.

Project description:Prognostic biomarkers that can reliably predict the disease-free survival (DFS) of locally advanced cervical cancer (LACC) are needed for identifying those patients at high risk for progression, who may benefit from a more aggressive treatment. In the present study, we aimed to construct a multiparametric MRI-derived radiomic signature for predicting DFS of LACC patients who underwent concurrent chemoradiotherapy (CCRT).MethodsThis multicenter retrospective study recruited 263 patients with International Federation of Gynecology and Obetrics (FIGO) stage IB-IVA treated with CCRT for whom pretreatment MRI scans were performed. They were randomly divided into two groups: primary cohort (n = 178) and validation cohort (n = 85). The LASSO regression and Cox proportional hazard regression were conducted to construct the radiomic signature (RS). According to the cutoff of the RS value, patients were dichotomized into low- and high-risk groups. Pearson's correlation and Kaplan-Meier analysis were conducted to evaluate the association between the RS and DFS. The RS, the clinical model incorporating FIGO stage and lymph node metastasis by the multivariate Cox proportional hazard model, and a combined model incorporating RS and clinical model were constructed to estimate DFS individually.ResultsThe final radiomic signature consisted of four radiomic features: T2W_wavelet-LH_ glszm_Size Zone NonUniformity, ADC_wavelet-HL-first order_ Median, ADC_wavelet-HH-glrlm_Long Run Low Gray Level Emphasis, and ADC_wavelet _LL_gldm_Large Dependence High Gray Emphasis. Higher RS was significantly associated with worse DFS in the primary and validation cohorts (both p<0.001). The RS demonstrated better prognostic performance in predicting DFS than the clinical model in both cohorts (C-index, 0.736-0.758 for RS, and 0.603-0.649 for clinical model). However, the combined model showed no significant improvement (C-index, 0.648, 95% CI, 0.571-0.685).ConclusionsThe present study indicated that the multiparametric MRI-derived radiomic signature could be used as a non-invasive prognostic tool for predicting DFS in LACC patients.

Dataset Information

MRI radiomic signature predicts intracranial progression-free survival in patients with brain metastases of ALK-positive non-small cell lung cancer.

Background

Methods

Results

Conclusions

Publications

MRI radiomic signature predicts intracranial progression-free survival in patients with brain metastases of ALK-positive non-small cell lung cancer.

Similar Datasets

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