Prognostic Value of MR Imaging Texture Analysis in Brain Non-Small Cell Lung Cancer Oligo-Metastases Undergoing Stereotactic Irradiation

MR-based radiomics predictive modelling of EGFR mutation and HER2 overexpression in metastatic brain adenocarcinoma: a two-centre study

OBJECTIVES

Magnetic resonance (MR)-based radiomics features of brain metastases are utilised to predict epidermal growth factor receptor (EGFR) mutation and human epidermal growth factor receptor 2 (HER2) overexpression in adenocarcinoma, with the aim to identify the most predictive MR sequence.

METHODS

A retrospective inclusion of 268 individuals with brain metastases from adenocarcinoma across two institutions was conducted. Utilising T1-weighted imaging (T1 contrast-enhanced [T1-CE]) and T2 fluid-attenuated inversion recovery (T2-FLAIR) sequences, 1,409 radiomics features were extracted. These sequences were randomly divided into training and test sets at a 7:3 ratio. The selection of relevant features was done using the least absolute shrinkage selection operator, and the training cohort's support vector classifier model was employed to generate the predictive model. The performance of the radiomics features was evaluated using a separate test set.

RESULTS

For contrast-enhanced T1-CE cohorts, the radiomics features based on 19 selected characteristics exhibited excellent discrimination. No significant differences in age, sex, and time to metastasis were observed between the groups with EGFR mutations or HER2 + and those with wild-type EGFR or HER2 (p > 0.05). Radiomics feature analysis for T1-CE revealed an area under the curve (AUC) of 0.98, classification accuracy of 0.93, sensitivity of 0.92, and specificity of 0.93 in the training cohort. In the test set, the AUC was 0.82. The 19 radiomics features for the T2-FLAIR sequence showed AUCs of 0.86 in the training set and 0.70 in the test set.

CONCLUSIONS

This study developed a T1-CE signature that could serve as a non-invasive adjunctive tool to determine the presence of EGFR mutations and HER2 + status in adenocarcinoma, aiding in the direction of treatment plans.

CLINICAL RELEVANCE STATEMENT

We propose radiomics features based on T1-CE brain MR sequences that are both evidence-based and non-invasive. These can be employed to guide clinical treatment planning in patients with brain metastases from adenocarcinoma.

Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging to predict extramural venous invasion in rectal cancer

BACKGROUND

To explore the potential of histogram analysis (HA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the identification of extramural venous invasion (EMVI) in rectal cancer patients.

METHODS

This retrospective study included preoperative images of 194 rectal cancer patients at our hospital between May 2019 and April 2022. The postoperative histopathological examination served as the reference standard. The mean values of DCE-MRI quantitative perfusion parameters (Ktrans, Kep and Ve) and other HA features calculated from these parameters were compared between the pathological EMVI-positive and EMVI-negative groups. Multivariate logistic regression analysis was performed to establish the prediction model for pathological EMVI-positive status. Diagnostic performance was assessed and compared using the receiver operating characteristic (ROC) curve. The clinical usefulness of the best prediction model was further measured with patients with indeterminate MRI-defined EMVI (mrEMVI) score 2(possibly negative) and score 3 (probably positive).

RESULTS

The mean values of Ktrans and Ve in the EMVI-positive group were significantly higher than those in the EMVI-negative group (P = 0.013 and 0.025, respectively). Significant differences in Ktrans skewness, Ktrans entropy, Ktrans kurtosis, and Ve maximum were observed between the two groups (P = 0.001,0.002, 0.000, and 0.033, respectively). The Ktrans kurtosis and Ktrans entropy were identified as independent predictors for pathological EMVI. The combined prediction model had the highest area under the curve (AUC) at 0.926 for predicting pathological EMVI status and further reached the AUC of 0.867 in subpopulations with indeterminate mrEMVI scores.

CONCLUSIONS

Histogram Analysis of DCE-MRI Ktrans maps may be useful in preoperative identification of EMVI in rectal cancer, particularly in patients with indeterminate mrEMVI scores.

Characterizing Various Posterior Fossa Tumors in Children and Adults With Diffusion-Weighted Imaging and Spectroscopy

Background The posterior fossa is situated between the tentorium cerebelli above and the foramen magnum below. Vital structures like the cerebellum, the pons, and the medulla are situated within it; hence, tumors within the posterior fossa are considered one of the most critical brain lesions. Children are more likely to develop posterior fossa tumors than adults. Diffusion-weighted imaging (DWI) and magnetic resonance spectroscopy (MRS) sequences along with the conventional MRI help in providing additional information in the characterization of the various posterior fossa tumors. We hereby present a series of 30 patients with clinically suspected posterior fossa masses who underwent preoperative MRI. Objectives This study aims to differentiate the neoplastic from non-neoplastic posterior fossa mass by evaluating the diffusion restriction pattern on DWI, quantifying the apparent diffusion coefficient (ADC) map in various posterior fossa tumors, and comparing the different metabolites of various posterior fossa tumors on MRS. Results Out of the 30 patients with posterior fossa lesions, 18 were males and 12 were females. Eight of them were in the pediatric age group, while twenty-two of them were adults. Metastasis was the most common posterior fossa lesion in our study sample and was found in six patients (20%), followed by vestibular schwannomas (17%) and arachnoid cysts (13%), meningiomas, medulloblastoma, and pilocytic astrocytoma (10% each) and epidermoid, ependymoma, and hemangioblastoma (7% each). The mean ADC value of benign tumors was higher than that of malignant tumors, and this difference was found to be significant (p = 0.012). The cut-off ADC value 1.21x 10^-3mm²/s had a sensitivity of 81.82% and specificity of 80.47%. MRS metabolites played an additional role in differentiating benign from malignant tumors. Conclusion A combination of conventional MRI, DWI, ADC values, and MRS metabolites showed good diagnostic accuracy to differentiate between the various posterior fossa neoplastic tumors both in adults and children.

The emerging potential of quantitative MRI biomarkers for the early prediction of brain metastasis response after stereotactic radiosurgery: a scoping review

Background

At present, the simple prognostic models based on clinical information for predicting the treatment outcomes of brain metastases (BMs) are subjective and delayed. Thus, we performed this systematic review of multiple studies to assess the potential of quantitative magnetic resonance imaging (MRI) biomarkers for the early prediction of treatment outcomes of brain metastases with stereotactic radiosurgery (SRS).

Methods

We systematically searched the PubMed, Embase, Cochrane, Web of Science, and Clinical Trials.gov databases for articles published between February 1, 1991, and April 11, 2022, with no language restrictions. We included studies involving patients with BMs receiving SRS; the included patients were required to have definite pathology of a primary tumor and complete imaging data (pre- and post-SRS). We excluded the articles that included patients who had undergone previous surgery and those that did not include regular follow-up or corresponding MRI scans.

Results

We identified 2,162 studies, of which 26 were included in our analysis, involving a total of 1,362 participants. All 26 studies explored the relevant MRI parameters to predict the prognosis of patients with BMs who received SRS. The outcomes were generalized according to the relationships between the anatomical/morphological, microstructural, vascular, and metabolic changes and SRS. Generally, with traditional MRI, there are several quantitative prognostic models based on preradiosurgical radiomics that predict the outcome of SRS treatment in local BM control. With the implementation of advanced MRI, the relative apparent diffusion coefficient (ADC), perfusion fraction (f), relative cerebral blood volume (rCBV), relative regional cerebral blood flow (rrCBF), interstitial fluid pressure (IFP), quadratic of time-dependent leakage (K_trans ²), extracellular extravascular volume (v_e), choline/creatine (Cho/Cr), nuclear Overhauser effect (NOE) peak, and intraextracellular water exchange rate constant (k_IE ) were confirmed to be indicative of the therapeutic effect of SRS for BMs.

Conclusions

Quantitative MRI biomarkers extracted from traditional or advanced MRI at different time points, which can represent the anatomical/morphological, microstructural, vascular, and metabolic changes, respectively, have been proposed as promising markers for the early prediction of SRS response in those with BMs. There are some limitations in this review, including the risk of selection bias, the limited number of study objects, the incomparability of the total data, and the subjectivity of the review process.

Analysing the possibility of utilizing CBCT radiomics as an independent modality: a phantom study

Aim:

To verify if computed tomography (CT) radiomics were reproducible by cone beam CT (CBCT) radiomics by using Catphan^® 504.

Materials and Methods:

Catphan^® 504 was imaged using the default IGRT OBI CBCT imaging protocols and CT scanner. Seven known density image regions of the phantom were segmented and image feature was extracted by Imaging Biomarker Explorer (IBEX) software. The 49 selected features from four feature categories were analyzed by considering each region of interest (ROI) segment as individual image set. Correlation was studies using interclass correlation coefficient (ICC) and Pearson’s correlation coefficient.

Results:

The ICC of the three feature categories, namely intensity, GLCM, and GLRLM was significant (p-value<0.05) in comparison with CT, while the ICC of the fourth feature category, NID, was no significant. The average absolute Pearson’s correlation coefficient from the features of the images was as follows: CT: r=0.679±0.257, CBC_Thead: r=0.707±0.231, CBCT_thorax: r=0.643±0.260, and CBCT_pelvis: r=0.594±0.276.

Conclusion:

It seems that the various densities of Catphan^® 504 ROI image segments of the CT radiomics are reproducible with CBCT radiomics and CBCT radiomics can be used as an independent modality.

Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer

Background: Brain metastases are associated with poor survival. Molecular genetic testing informs on targeted therapy and survival. The purpose of this study was to perform a MR imaging-based radiomic analysis of brain metastases from non-small cell lung cancer (NSCLC) to identify radiomic features that were important for predicting survival duration.

Methods: We retrospectively identified our study cohort via an institutional database search for patients with brain metastases from EGFR, ALK, and/or KRAS mutation-positive NSCLC. We segmented the brain metastatic tumors on the brain MR images, extracted radiomic features, constructed radiomic scores from significant radiomic features based on multivariate Cox regression analysis (p < 0.05), and built predictive models for survival duration.

Result: Of the 110 patients in the cohort (mean age 57.51 ± 12.32 years; range: 22–85 years, M:F = 37:73), 75, 26, and 15 had NSCLC with EGFR, ALK, and KRAS mutations, respectively. Predictive modeling of survival duration using both clinical and radiomic features yielded areas under the receiver operative characteristic curve of 0.977, 0.905, and 0.947 for the EGFR, ALK, and KRAS mutation-positive groups, respectively. Radiomic scores enabled the separation of each mutation-positive group into two subgroups with significantly different survival durations, i.e., shorter vs. longer duration when comparing to the median survival duration of the group.

Conclusion: Our data supports the use of radiomic scores, based on MR imaging of brain metastases from NSCLC, as non-invasive biomarkers for survival duration. Future research with a larger sample size and external cohorts is needed to validate our results.

MRI Texture Analysis for the Prediction of Stereotactic Radiosurgery Outcomes in Brain Metastases from Lung Cancer

This study aims to evaluate the utility of texture analysis in predicting the outcome of stereotactic radiosurgery (SRS) for brain metastases from lung cancer. From 83 patients with lung cancer who underwent SRS for brain metastasis, a total of 118 metastatic lesions were included. Two neuroradiologists independently performed magnetic resonance imaging (MRI)-based texture analysis using the Imaging Biomarker Explorer software. Inter-reader reliability as well as univariable and multivariable analyses were performed for texture features and clinical parameters to determine independent predictors for local progression-free survival (PFS) and overall survival (OS). Furthermore, Harrell’s concordance index (C-index) was used to assess the performance of the independent texture features. The primary tumor histology of small cell lung cancer (SCLC) was the only clinical parameter significantly associated with local PFS in multivariable analysis. Run-length non-uniformity (RLN) and short-run emphasis were the independent texture features associated with local PFS. In the non-SCLC (NSCLC) subgroup analysis, RLN and local range mean were associated with local PFS. The C-index of independent texture features was 0.79 for the all-patients group and 0.73 for the NSCLC subgroup. In conclusion, texture analysis on pre-treatment MRI of lung cancer patients with brain metastases may have a role in predicting SRS response.

Role of diffusion-weighted imaging in differentiation between posterior fossa brain tumors

Background

Diffusion-weighted imaging (DWI) is an imaging modality using multi-section single-shot spin echo planar imaging (EPI) sequence which is extremely sensitive for detection of water motion within intra, extra, and transcellular regions. This character is important to differentiate between brain tumors either low (benign) or highly (malignant) cellular tumors.

Objective

To evaluate the role of DWI and apparent diffusion coefficient (ADC) in evaluation and differentiation between different brain posterior fossa tumors in children and adults.

Patients and methods

The study included 34 patients with different brain posterior fossa tumors for evaluation by conventional MRI (using 1.5 T MRI PHILIPS Achieva 2.1 Best Netherland) and DWI.

Results

Our study showed that mean ADC values were significantly different between the four groups of posterior fossa tumors in children: juvenile pilocytic astrocytoma (JPA), medulloblastoma, ependymoma, and brain stem glioma while mean ADC values were not significantly different between posterior fossa tumors in the adult group. Regions of interest were manually positioned, and all values were automatically calculated and expressed in 10−3 mm2/s.

Conclusion

DWI is an ideal additional imaging technique, which is a rapid, easy, non-invasive imaging modality, with no contrast injection needed. It has been widely applied in the differentiation between posterior fossa brain tumors and in the diagnosis of various intracranial diseases.

Radiomic prediction of mutation status based on MR imaging of lung cancer brain metastases

Lung cancer metastases comprise most of all brain metastases in adults and most brain metastases are diagnosed by magnetic resonance (MR) scans. The purpose of this study was to conduct an MR imaging-based radiomic analysis of brain metastatic lesions from patients with primary lung cancer to classify mutational status of the metastatic disease. We retrospectively identified lung cancer patients with brain metastases treated at our institution between 2009 and 2017 who underwent genotype testing of their primary lung cancer. Brain MR Images were used for segmentation of enhancing tumors and peritumoral edema, and for radiomic feature extraction. The most relevant radiomic features were identified and used with clinical data to train random forest classifiers to classify the mutation status. Of 110 patients in the study cohort (mean age 57.51 ± 12.32 years; M: F = 37:73), 75 had an EGFR mutation, 21 had an ALK translocation, and 15 had a KRAS mutation. One patient had both ALK translocation and EGFR mutation. Majority of radiomic features most relevant for mutation classification were textural. Model building using both radiomic features and clinical data yielded more accurate classifications than using either alone. For classification of EGFR, ALK, and KRAS mutation status, the model built with both radiomic features and clinical data resulted in area-under-the-curve (AUC) values based on cross-validation of 0.912, 0.915, and 0.985, respectively. Our study demonstrated that MR imaging-based radiomic analysis of brain metastases in patients with primary lung cancer may be used to classify mutation status. This approach may be useful for devising treatment strategies and informing prognosis.

Combined FET PET/MRI radiomics differentiates radiation injury from recurrent brain metastasis

Background

The aim of this study was to investigate the potential of combined textural feature analysis of contrast-enhanced MRI (CE-MRI) and static O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (FET) PET for the differentiation between local recurrent brain metastasis and radiation injury since CE-MRI often remains inconclusive.

Methods

Fifty-two patients with new or progressive contrast-enhancing brain lesions on MRI after radiotherapy (predominantly stereotactic radiosurgery) of brain metastases were additionally investigated using FET PET. Based on histology (n = 19) or clinicoradiological follow-up (n = 33), local recurrent brain metastases were diagnosed in 21 patients (40%) and radiation injury in 31 patients (60%). Forty-two textural features were calculated on both unfiltered and filtered CE-MRI and summed FET PET images (20–40 min p.i.), using the software LIFEx. After feature selection, logistic regression models using a maximum of five features to avoid overfitting were calculated for each imaging modality separately and for the combined FET PET/MRI features. The resulting models were validated using cross-validation. Diagnostic accuracies were calculated for each imaging modality separately as well as for the combined model.

Results

For the differentiation between radiation injury and recurrence of brain metastasis, textural features extracted from CE-MRI had a diagnostic accuracy of 81% (sensitivity, 67%; specificity, 90%). FET PET textural features revealed a slightly higher diagnostic accuracy of 83% (sensitivity, 88%; specificity, 75%). However, the highest diagnostic accuracy was obtained when combining CE-MRI and FET PET features (accuracy, 89%; sensitivity, 85%; specificity, 96%).

Conclusions

Our findings suggest that combined FET PET/CE-MRI radiomics using textural feature analysis offers a great potential to contribute significantly to the management of patients with brain metastases.

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Differentiation between brain metastasis recurrence and radiation injury is of high clinical importance.

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Differentiation based on contrast-enhanced conventional MRI is often inconclusive.

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Radiomics and hybrid amino acid PET/MR imaging are increasingly gaining attention in Neuro-Oncology.

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We investigated the potential of combined PET/MRI radiomics analysis using MRI and FET PET in patients with brain metastases.

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Combined PET/MRI radiomics allows the differentiation of brain metastasis recurrence from radiation injury with high accuracy.

3D bone texture analysis as a potential predictor of radiation-induced insufficiency fractures

Background

The aim of our work is to assess the potential role of texture analysis (TA), applied to computed tomography (CT) simulation scans, in relation to the development of insufficiency fractures (IFs) in patients undergoing radiation therapy (RT) for pelvic malignancies.

Methods

We analyzed patients undergoing pelvic RT from Jan-2010 to Dec-2016, 31 of whom had developed IFs of the pelvis. We analyzed CT simulation scans using LifeX Software^©, and in particular we selected three regions of interest (ROI): L5 body, the sacrum and both the femoral heads. The ROI were automatically contoured using the treatment planning software Raystation^©. TA parameters included parameters from the gray-level histogram, indices from sphericity and from the matrix of GLCM (gray level co-occurrence matrix). The IFs patients were matched (1:1 ratio) with control patients who had not developed IFs, and were matched for age, sex, type of tumor, menopausal status, RT dose and use of chemotherapy. Univariate and multivariate analyses (logistic regression) were used for statistical analysis.

Results

Significant TA parameters on univariate analysis included both parameters from the histogram distribution, as well from the matrix of GLCM. On logistic regression analysis the significant parameters were L5-energy [P=0.033, odds ratio (OR): 1.997, 95% CI: 1.059-3.767] and FH-Skewness (P=0.014, OR: 2.338, 95% CI: 1.191-4.591), with a R2: 0.268. A ROC curve was generated from the binary logistic regression, and the AUC was 0.741 (95% CI: 0.627-0.855, P=0.001, S.E.: 0.058).

Conclusions

In our experience, 3D-bone CT TA can be used to stratify the risk of the patients to develop radiation-induced IFs. A prospective study will be conducted to validate these findings.

Lack of robustness of textural measures obtained from 3D brain tumor MRIs impose a need for standardization

Purpose

Textural measures have been widely explored as imaging biomarkers in cancer. However, their robustness under dynamic range and spatial resolution changes in brain 3D magnetic resonance images (MRI) has not been assessed. The aim of this work was to study potential variations of textural measures due to changes in MRI protocols.

Materials and methods

Twenty patients harboring glioblastoma with pretreatment 3D T1-weighted MRIs were included in the study. Four different spatial resolution combinations and three dynamic ranges were studied for each patient. Sixteen three-dimensional textural heterogeneity measures were computed for each patient and configuration including co-occurrence matrices (CM) features and run-length matrices (RLM) features. The coefficient of variation was used to assess the robustness of the measures in two series of experiments corresponding to (i) changing the dynamic range and (ii) changing the matrix size.

Results

No textural measures were robust under dynamic range changes. Entropy was the only textural feature robust under spatial resolution changes (coefficient of variation under 10% in all cases).

Conclusion

Textural measures of three-dimensional brain tumor images are not robust neither under dynamic range nor under matrix size changes. Standards should be harmonized to use textural features as imaging biomarkers in radiomic-based studies. The implications of this work go beyond the specific tumor type studied here and pose the need for standardization in textural feature calculation of oncological images.

Texture analysis of medical images for radiotherapy applications

The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.