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Predicting brain metastases for non-small cell lung cancer based on magnetic resonance imaging

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Abstract

In this study the relationship between brain structure and brain metastases (BM) occurrence was analyzed. A model for predicting the time of BM onset in patients with non-small cell lung cancer (NSCLC) was proposed. Twenty patients were used to develop the model, whereas the remaining 69 were used for independent validation and verification of the model. Magnetic resonance images were segmented into cerebrospinal fluid, gray matter (GM), and white matter using voxel-based morphometry. Automatic anatomic labeling template was used to extract 116 brain regions from the GM volume. The elapsed time between the MRI acquisitions and BM diagnosed was analyzed using the least absolute shrinkage and selection operator method. The model was validated using the leave-one-out cross validation (LOOCV) and permutation test. The GM volume of the extracted 11 regions of interest increased with the progression of BM from NSCLC. LOOCV test on the model indicated that the measured and predicted BM onset were highly correlated (r = 0.834, P = 0.0000). For the 69 independent validating patients, accuracy, sensitivity, and specificity of the model for predicting BM occurrence were 70, 75, and 66%, respectively, in 6 months and 74, 82, and 60%, respectively, in 1 year. The extracted brain GM volumes and interval times for BM occurrence were correlated. The established model based on MRI data may reliably predict BM in 6 months or 1 year. Further studies with larger sample size are needed to validate the findings in a clinical setting.

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Abbreviations

LASSO:

Least absolute shrinkage and selectionator operator

VBM:

Voxel-based-morphometry

MRI:

Magnetic resonance imaging

NSCLC:

Non-small cell lung cancer

BM:

Brain metastases

GM:

Gray matter

WM:

White matter

CSF:

Cerebrospinal fluid

AAL:

Automatic anatomic labeling

LOOCV:

Leave-one-out cross validation

SLEP:

Sparse learning with efficient projections

LA-NSCLC:

Locally advanced non-small-cell lung cancer

Interval time:

The elaspe time between the MRI acquisition and the time at which brain metastases were diagnosed

Onset time:

The onset time of brain metastases confirmed by clinical diagnosis

WMH:

White matter hyperintensities

Right superior frontal gyrus:

Frontal_Sup_R

Right superior frontal cortex:

Frontal_Inf_Orb_R

Left superior medial frontal gyrus:

Frontal_Sup_Medial_L

Right parahippocmpal gyrus:

ParaHippocampal_R

Left postcentral gyrus:

Postcentral_L

Left supramarginal gyrus:

SupraMarginal_L

Right caudate:

Caudate_R

Left cerebrlum crus1:

Cerebelum_Crus1_L

Left cerebrlum crus2:

Cerebelum_Crus2_L

Right cerebrlum crus2:

Cerebelum_Crus2_R

Left cerebrlum 9:

Cerebelum_9_R

t-BM:

The elapsed time between an MRI scan and the MRI scan at the time of BM diagnosis

t-BMp:

The predicted Δt-BM

t-BMo:

The original Δt-BM

TP:

True positive

FP:

False positive

TN:

True negative

FN:

False negative

ROC:

Receiver operating characteristic curves

PCI:

Prophylactic cranial irradiation

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    Authors and Affiliations

    1. Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, No.55, the 4th Section, Renmin South Road, Chengdu, 610041, Sichuan, China

      Gang Yin, Churong Li, Yangkun Luo, Lucia Clara Orlandini, Pei Wang & Jinyi Lang

    2. Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan, China

      Heng Chen

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    1. Gang Yin
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    2. Churong Li
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    Correspondence to Pei Wang or Jinyi Lang.

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    Gang Yin, Churong Li and Heng Chen have contributed equally to this work.

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    Yin, G., Li, C., Chen, H. et al. Predicting brain metastases for non-small cell lung cancer based on magnetic resonance imaging. Clin Exp Metastasis 34, 115–124 (2017). https://doi.org/10.1007/s10585-016-9833-7

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    • DOI: https://doi.org/10.1007/s10585-016-9833-7

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