Dynamic contrast-enhanced magnetic resonance imaging in cervical cancer: correlation between quantitative parameters and molecular markers hypoxia-inducible factors-1-alpha, vascular endothelial growth factor, and Ki-67

AIM

To investigate whether dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has the potential to non-invasively detect microenvironmental condition by quantitatively measuring blood perfusion, vessel wall permeability, and vascularity, and to elucidate the possible correlations between DCE-MRI quantitative parameters and the expression level of hypoxia, vascularity, and cell proliferation related molecular biomarkers.

MATERIALS AND METHODS

In this prospective single center clinical study, 58 patients diagnosed with cervical cancer underwent DCE-MRI before anticancer treatment were enrolled. Ktrans, Kep, Ve, and Vp were generated from Extended Toft's model. Then patients conducted colposcopy biopsy within 1 week after DCE-MRI. Pretreatment expression levels of HIF-1α, VEGF and Ki-67 were assessed and scored by immunohistochemistry on colposcopy obtained tumor specimens.

RESULTS

In HIF-1α low-expression group, Ktrans (p=0.031) and Kep (p=0.012) values were significantly higher than the high-expression group. In VEGF high-expression group, Ktrans (p=0.044) and Ve values (p=0.021) were significantly higher than the low-expression group. In Ki-67 high-expression group, Ktrans (p=0.026) and Kep (p=0.033) were significantly higher than the low-expression group. Multiple linear regression analyses and Pearson correlation revealed that Ktrans independently negatively correlated with HIF-1α expression, Ve independently positively correlated with VEGF, and Kep independently positively correlated with Ki-67. The area under the ROC curves of Ktrans for HIF-1α, Ve for VEGF, and Kep for Ki-67 were 0.728, 0.743, 0.730, respectively.

CONCLUSION

Our results suggest that DCE-MRI quantitative parameters could be potentially used as imaging markers for non-invasively detecting microenvironmental hypoxia, vascularity and proliferation in cervical cancer patients.

Combined radiomics of primary tumour and bone metastasis improve the prediction of EGFR mutation status and response to EGFR-TKI therapy for NSCLC

PURPOSE

To develop radiomics models of primary tumour and spinal metastases to predict epidermal growth factor receptor (EGFR) mutations and therapeutic response to EGFR-tyrosine kinase inhibitor (TKI) in patients with metastatic non-small-cell lung cancer (NSCLC).

METHODS

We enrolled 203 patients with spinal metastases between December 2017 and September 2021, classified as patients with the EGFR mutation or EGFR wild-type. All patients underwent thoracic CT and spinal MRI scans before any treatment. Radiomics analysis was performed to extract features from primary tumour and metastases images and identify predictive features with the least absolute shrinkage and selection operator. Radiomics signatures (RS) were constructed based on primary tumour (RS-Pri), metastases (RS-Met), and in combination (RS-Com) to predict EGFR mutation status and response to EGFR-TKI. Receiver operating characteristic (ROC) curve analysis with 10-fold cross-validation was applied to assess the performance of the models.

RESULTS

To predict the EGFR mutation status, the RS based on the combination of primary tumour and metastases improved the prediction AUCs compared to those based on the primary tumour or metastasis alone in the training (RS-Com-EGFR: 0.927) and validation (RS-Com-EGFR: 0.812) cohorts. To predict response to EGFR-TKI, the developed RS based on combined primary tumour and metastasis generated the highest AUCs in the training (RS-Com-TKI: 0.880) and validation (RS-Com-TKI: 0.798) cohort.

CONCLUSIONS

Primary NSCLC and spinal metastases can provide complementary information to predict the EGFR mutation status and response to EGFR-TKI. The developed models that integrate primary lesions and metastases may be potential imaging markers to guide individual treatment decisions.

MRI features and whole-lesion apparent diffusion coefficient histogram analysis of brain metastasis from non-small cell lung cancer for differentiating epidermal growth factor receptor mutation status

AIM

To explore the utility of magnetic resonance imaging (MRI) characteristics and whole-lesion apparent diffusion coefficient histogram analysis of brain metastasis from non-small cell lung cancer (NSCLC) in the differentiation of epidermal growth factor receptor (EGFR) mutation status.

MATERIALS AND METHODS

Forty-eight patients with brain metastases from NSCLC were enrolled in this retrospective study. Patients were subtyped into EGFR mutation (23 cases) and wild-type (25 cases) groups. Whole-lesion histogram metrics were derived from the apparent diffusion coefficient (ADC) maps, and imaging features were evaluated according to conventional MRI. Student's t-test or Mann-Whitney U-test, chi-squared test, and receiver operating characteristic (ROC) curve analysis were performed to discriminate the two groups and to determine the diagnostic efficacy of ADC histogram parameters.

RESULTS

EGFR mutation group had more multiple brain metastases, less peritumoural brain oedema (PTBO), and lower peritumoural brain oedema index (PTBO-I) than EGFR wild-type group (all p<0.05). In addition, 90th and 75th percentiles of ADC and maximum ADC in the EGFR mutation group were significantly higher than in the EGFR wild-type group (all p<0.05). Ninetieth percentile of ADC had the highest area under the curve (AUC; 0.711), and it was found to outperform 75th percentile of ADC (AUC, 0.662; p=0.039) and maximum ADC (AUC, 0.681).

CONCLUSIONS

Whole-lesion ADC histogram analysis and MRI features of brain metastasis from NSCLC are expected to be potential biomarkers to non-invasively differentiate the EGFR mutation status.

Development and validation of MRI-based radiomics signatures as new markers for preoperative assessment of EGFR mutation and subtypes from bone metastases

Background

This study aimed to develop and externally validate contrast-enhanced (CE) T1-weighted MRI-based radiomics for the identification of epidermal growth factor receptor (EGFR) mutation, exon-19 deletion and exon-21 L858R mutation from MR imaging of spinal bone metastasis from primary lung adenocarcinoma.

Methods

A total of 159 patients from our hospital between January 2017 and September 2021 formed a primary set, and 24 patients from another center between January 2017 and October 2021 formed an independent validation set. Radiomics features were extracted from the CET1 MRI using the Pyradiomics method. The least absolute shrinkage and selection operator (LASSO) regression was applied for selecting the most predictive features. Radiomics signatures (RSs) were developed based on the primary training set to predict EGFR mutations and differentiate between exon-19 deletion and exon-21 L858R. The RSs were validated on the internal and external validation sets using the Receiver Operating Characteristic (ROC) curve analysis.

Results

Eight, three, and five most predictive features were selected to build RS-EGFR, RS-19, and RS-21 for predicting EGFR mutation, exon-19 deletion and exon-21 L858R, respectively. The RSs generated favorable prediction efficacies for the primary (AUCs, RS-EGFR vs. RS-19 vs. RS-21, 0.851 vs. 0.816 vs. 0.814) and external validation (AUCs, RS-EGFR vs. RS-19 vs. RS-21, 0.807 vs. 0.742 vs. 0.792) sets.

Conclusions

Radiomics features from the CE MRI could be used to detect the EGFR mutation, increasing the certainty of identifying exon-19 deletion and exon-21 L858R mutations based on spinal metastasis MR imaging.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09985-4.

DCE-MRI quantitative transport mapping for noninvasively detecting hypoxia inducible factor-1α, epidermal growth factor receptor overexpression, and Ki-67 in nasopharyngeal carcinoma patients

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has the potential to noninvasively detect expression of hypoxia inducible factor-1-alpha (HIF-1α), epidermal growth factor receptor (EGFR), and Ki-67 in nasopharyngeal carcinoma (NPC) by quantitatively measuring tumor blood flow, vascularity, and permeability.

PURPOSE

We aim to explore the utility of DCE-MRI in detecting HIF-1α, EGFR, and Ki-67 expression levels using traditional Kety's/Tofts' modeling and quantitative transport mapping (QTM).

MATERIALS AND METHODS

Eighty-nine NPC patients underwent DCE-MRI before treatment were enrolled. DCE-MRI was processed to generate the following kinetic parameters: |u| and D from the QTM model, tumor blood flow (TBF) from Kety's model, and K^trans, V_e, and K_ep from Tofts' model. Pretreatment levels of HIF-1α, EGFR, and Ki-67 were assessed by immunohistochemistry and classified into low and high expression groups.

RESULTS

|u| (p < 0.001) and TBF (p = 0.015) values were significantly higher in the HIF-1α high-expression group compared to low-expression group. Only K^trans (p = 0.016) was significantly higher in the EGFR high-expression group. Only |u| (p < 0.001) values were significantly higher in the Ki-67 high-expression group compared to low-expression group. Multiple linear regression analyses showed that |u| independently correlated with HIF-1α and Ki-67 expression, and K^trans independently correlated with EGFR. The areas under the ROC curves of |u| for HIF-1α and Ki-67, and K^trans for EGFR were 0.83, 0.74, and 0.70, respectively.

CONCLUSION

|u| and K^trans derived from DCE-MRI may be considered as noninvasive imaging markers for detecting hypoxia and proliferation in NPC patients.

MRI-based radiomics analysis for predicting the EGFR mutation based on thoracic spinal metastases in lung adenocarcinoma patients

PURPOSE

This study aims to develop and evaluate multi-parametric MRI-based radiomics for preoperative identification of epidermal growth factor receptor (EGFR) mutation, which is important in treatment planning for patients with thoracic spinal metastases from primary lung adenocarcinoma.

METHODS

A total of 110 patients were enrolled between January 2016 and March 2019 as a primary cohort. A time-independent validation cohort was conducted containing 52 patients consecutively enrolled from July 2019 to April 2021. The patients were pathologically diagnosed with thoracic spinal metastases from primary lung adenocarcinoma; all underwent T1-weighted (T1W), T2-weighted (T2W), and T2-weighted fat-suppressed (T2FS) MRI scans of the thoracic spinal. Handcrafted and deep learning-based features were extracted and selected from each MRI modality, and used to build the radiomics signature. Various machine learning classifiers were developed and compared. A clinical-radiomics nomogram integrating the combined rad signature and the most important clinical factor was constructed with receiver operating characteristic (ROC), calibration, and decision curves analysis (DCA) to evaluate the prediction performance.

RESULTS

The combined radiomics signature derived from the joint of three modalities can effectively classify EGFR mutation and EGFR wild-type patients, with an area under the ROC curve (AUC) of 0.886 (95% confidence interval [CI]: 0.826-0.947, SEN =0.935, SPE =0.688) in the training group and 0.803 (95% CI: 0.682-0.924, SEN = 0.700, SPE = 0.818) in the time-independent validation group. The nomogram incorporating the combined radiomics signature and smoking status achieved the best prediction performance in the training (AUC = 0.888, 95% CI: 0.849-0.958, SEN = 0.839, SPE = 0.792) and time-independent validation (AUC = 0.821, 95% CI: 0.692-0.929, SEN = 0.667, SPE = 0.909) cohorts. The DCA confirmed potential clinical usefulness of our nomogram.

CONCLUSION

Our study demonstrated the potential of multi-parametric MRI-based radiomics on preoperatively predicting the EGFR mutation. The proposed nomogram model can be considered as a new biomarker to guide the selection of individual treatment strategies for patients with thoracic spinal metastases from primary lung adenocarcinoma.

Diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging after radiation therapy for bone metastases in patients with hepatocellular carcinoma

The objectives of this study were to assess changes in apparent diffusion coefficient (ADC) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters after radiation therapy (RT) for bone metastases from hepatocellular carcinoma (HCC) and to evaluate their prognostic value. This prospective study was approved by the Institutional Review Board. Fourteen patients with HCC underwent RT (30 Gy in 10 fractions once daily) for bone metastases. The ADC and DCE-MRI parameters and the volume of the target lesions were measured before (baseline) and one month after RT (post-RT). The Wilcoxon signed-rank test was used to compare the parameters between the baseline and post-RT MRI. The parameters were compared between patients with or without disease progression in RT fields using the Mann–Whitney test. Intraclass correlation coefficients were used to evaluate the interobserver agreement. The medians of the ADC, rate constant [k_ep], and volume fraction of the extravascular extracellular matrix [v_e] in the baseline and post-RT MRI were 0.67 (range 0.61–0.72) and 0.75 (range 0.63–1.43) (× 10^–3 mm²/s) (P = 0.027), 836.33 (range 301.41–1082.32) and 335.80 (range 21.86–741.87) (× 10^–3/min) (P = 0.002), and 161.54 (range 128.38–410.13) and 273.99 (range 181.39–1216.95) (× 10^–3) (P = 0.027), respectively. The medians of the percent change in the ADC of post-RT MRI in patients with progressive disease and patients without progressive disease were − 1.35 (range − 6.16 to 6.79) and + 46.71 (range 7.71–112.81) (%) (P = 0.011), respectively. The interobserver agreements for all MRI parameters were excellent (intraclass correlation coefficients > 0.8). In conclusion, the ADC, k_ep, and v_e of bone metastases changed significantly after RT. The percentage change in the ADC was closely related to local tumor progression.

Multiparametric MRI-Based Radiomics Approaches for Preoperative Prediction of EGFR Mutation Status in Spinal Bone Metastases in Patients with Lung Adenocarcinoma

BACKGROUND

Preoperative prediction of epidermal growth factor receptor (EGFR) mutation status in patients with spinal bone metastases (SBM) from primary lung adenocarcinoma is potentially important for treatment decisions.

PURPOSE

To develop and validate multiparametric magnetic resonance imaging (MRI)-based radiomics methods for preoperative prediction of EGFR mutation based on MRI of SBM.

STUDY TYPE

Retrospective.

POPULATION

A total of 97 preoperative patients with lumbar SBM from lung adenocarcinoma (77 in training set and 20 in validation set).

FIELD STRENGTH/SEQUENCE

T1-weighted, T2-weighted, and T2-weighted fat-suppressed fast spin echo sequences at 3.0 T.

ASSESSMENT

Radiomics handcrafted and deep learning-based features were extracted and selected from each MRI sequence. The abilities of the features to predict EGFR mutation status were analyzed and compared. A radiomics nomogram was constructed integrating the selected features.

STATISTICAL TESTS

The Mann-Whitney U test and χ² test were employed for evaluating associations between clinical characteristics and EGFR mutation status for continuous and discrete variables, respectively. Least absolute shrinkage and selection operator was used for selection of predictive features. Sensitivity (SEN), specificity (SPE), and area under the receiver operating characteristic curve (AUC) were used to evaluate the ability of radiomics models to predict the EGFR mutation. Calibration and decision curve analysis (DCA) were performed to assess and validate nomogram results.

RESULTS

The radiomics signature comprised five handcrafted and one deep learning-based features and achieved good performance for predicting EGFR mutation status, with AUCs of 0.891 (95% confidence interval [CI], 0.820-0.962, SEN = 0.913, SPE = 0.710) in the training group and 0.771 (95% CI, 0.551-0.991, SEN = 0.750, SPE = 0.875) in the validation group. DCA confirmed the potential clinical usefulness of the radiomics models.

DATA CONCLUSION

Multiparametric MRI-based radiomics is potentially clinical valuable for predicting EGFR mutation status in patients with SBM from lung adenocarcinoma.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: 2.

Differentiation of bone metastases from prostate cancer and benign red marrow depositions of the pelvic bone with multiparametric MRI

PURPOSE

To investigate the diagnostic utilities of imaging parameters derived from T1-weighted imaging (T1WI), diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to differentiate bone metastases from prostate cancer and benign red marrow depositions of the pelvic bone.

MATERIALS AND METHODS

Thirty-six lesions from 36 patients with prostate cancer were analyzed with T1WI, DWI, and DCE-MRI. The lesions were classified in the bone metastases (n = 22) and benign red marrow depositions (n = 14). Lesion-muscle ratio (LMR), apparent diffusion coefficient (ADC), volume transfer constant (K^trans), reflux rate (Kep), and volume fraction of the extravascular extracellular matrix (Ve) values were obtained from the lesions. The imaging parameters of the both groups were compared using the Mann-Whitney U test, receiver operating characteristics (ROC) curves were analyzed. For the ROC curves, area under the curves (AUCs) were compared.

RESULTS

The ADC, K^trans, K_ep, and V_e values of bone metastases were significantly higher than those of benign red marrow depositions (Mann-Whitney U test, p < 0.05). However, there was no significant difference in LMR between the two groups (Mann-Whitney U test, p = 0.360). The AUCs of K^trans_, K_ep, ADC, V_e, and LMR were 0.896, 0.844, 0.812, 0.724, and 0.448, respectively. In the pairwise comparison of ROC curves, the AUCs of K^trans and K_ep was significantly higher than LMR.

CONCLUSIONS

K^trans, K_ep, V_e, and ADC values can be used as imaging tools to differentiate bone metastases from prostate cancer and benign red marrow depositions of the pelvic bone.

MRI biomarkers in osseous tumors

Although radiography continues to play a critical role in osseous tumor assessment, there have been remarkable advances in cross-sectional imaging. MRI has taken a lead in this assessment due to high tissue contrast and spatial resolution, which are well suited for bone lesion assessment. More recently, although somewhat lagging other organ systems, quantitative parameters have shown promising potential as biomarkers for osseous tumors. Among these sequences are chemical shift imaging (CSI), apparent diffusion coefficient (ADC), and intravoxel incoherent motion (IVIM) from diffusion-weighted imaging (DWI), quantitative dynamic contrast enhanced (DCE)-MRI, and magnetic resonance spectroscopy (MRS). In this article, we review the background and recent roles of these quantitative MRI biomarkers for osseous tumors. Level of Evidence: 3 Technical Efficacy Stage: 3 J. MAGN. RESON. IMAGING 2019. J. Magn. Reson. Imaging 2019;50:702-718.

Functional and Hybrid Imaging of Bone Metastases

Bone metastases are common, cause significant morbidity, and impact on healthcare resources. Although radiography, computed tomography (CT), magnetic resonance imaging (MRI), and bone scintigraphy have frequently been used for staging the skeleton, these methods are insensitive and nonspecific for monitoring treatment response in a clinically relevant time frame. We summarize several recent reports on new functional and hybrid imaging methods including single photon emission CT/CT, positron emission tomography/CT, and whole-body MRI with diffusion-weighted imaging. These modalities generally show improvements in diagnostic accuracy for staging and response assessment over standard imaging methods, with the ability to quantify biological processes related to the bone microenvironment as well as tumor cells. As some of these methods are now being adopted into routine clinical practice and clinical trials, further evaluation with comparative studies is required to guide optimal and cost-effective clinical management of patients with skeletal metastases. © 2018 American Society for Bone and Mineral Research.

Bone metastases from lung cancer: A paradigm for multidisciplinary onco-rheumatology management

Bone is the third metastatic site after liver and lungs. Bone metastases occur in one out of three lung cancers and are usually of osteolytic aspect. Osteolytic bone metastases are responsible of long bone and vertebral fractures leading to restricted mobility, surgery and medullar compression that severely alter quality of life and that have a huge medico-economic impact. In the recent years, Bone Metastatic Multidisciplinary Tumour Board (BM²TB) have been developed to optimize bone metastases management for each patient in harmony with oncology program. In this review, we will go through all the different aspects of bone metastases management including diagnosis and evaluation (CT scan, Tc 99m-MDP bone scan, ¹⁸FDG-PET scan and biopsy for molecular diagnosis), systemic bone treatments (zoledronic acid and denosumab) and local treatments (interventional radiology and radiotherapy). Surgical strategies will be discussed elsewhere. Based on the last 2017-Lung Cancer South East French Guidelines, we present a practical decision tree to help the physicians for decision making in order to reach a personalized locomotor strategy for every patient.