Diffusion-Weighted Imaging of Brain Metastasis from Lung Cancer: Correlation of MRI Parameters with the Histologic Type and Gene Mutation Status

Multisequence MRI-based radiomics signature as potential biomarkers for differentiating KRAS mutations in non-small cell lung cancer with brain metastases

Background

Kirsten rat sarcoma virus (KRAS) has evolved from a genotype with predictive value to a therapeutic target recently. The study aimed to establish non-invasive radiomics models based on MRI to discriminate KRAS from epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) mutations in lung cancer patients with brain metastases (BM), then further explore the optimal sequence for prediction.

Methods

This retrospective study involved 317 patients (218 patients in training cohort and 99 patients in testing cohort) who had confirmed of KRAS, EGFR or ALK mutations. Radiomics features were separately extracted from T2WI, T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (T1-CE) sequences. The maximal information coefficient and recursive feature elimination method were used to select informative features. Then we built four radiomics models for differentiating KRAS from EGFR or ALK using random forest classifier. ROC curves were used to validate the capability of the models.

Results

The four radiomics models for discriminating KRAS from EGFR all worked well, especially DWI and T2WI models (AUCs: 0.942, 0.942 in training cohort, 0.949, 0.954 in testing cohort). When KRAS compared to ALK, DWI and T2-FLAIR models showed excellent performance in two cohorts (AUCs: 0.947, 0.917 in training cohort, 0.850, 0.824 in testing cohort).

Conclusions

Radiomics classifiers integrating MRI have potential to discriminate KRAS from EGFR or ALK, which are helpful to guide treatment and facilitate the discovery of new approaches capable of achieving this long-sought goal of cure in lung cancer patients with KRAS.

Differentiation of multiple brain metastases and glioblastoma with multiple foci using MRI criteria

OBJECTIVE

Glioblastoma with multiple foci (mGBM) and multiple brain metastases share several common features on magnetic resonance imaging (MRI). A reliable preoperative diagnosis would be of clinical relevance. The aim of this study was to explore the differences and similarities between mGBM and multiple brain metastases on MRI.

METHODS

We performed a retrospective analysis of 50 patients with mGBM and compared them with a cohort of 50 patients with multiple brain metastases (2-10 lesions) histologically confirmed and treated at our department between 2015 and 2020. The following imaging characteristics were analyzed: lesion location, distribution, morphology, (T2-/FLAIR-weighted) connections between the lesions, patterns of contrast agent uptake, apparent diffusion coefficient (ADC)-values within the lesion, the surrounding T2-hyperintensity, and edema distribution.

RESULTS

A total of 210 brain metastases and 181 mGBM lesions were analyzed. An infratentorial localization was found significantly more often in patients with multiple brain metastases compared to mGBM patients (28 vs. 1.5%, p < 0.001). A T2-connection between the lesions was detected in 63% of mGBM lesions compared to 1% of brain metastases. Cortical edema was only present in mGBM. Perifocal edema with larger areas of diffusion restriction was detected in 31% of mGBM patients, but not in patients with metastases.

CONCLUSION

We identified a set of imaging features which improve preoperative diagnosis. The presence of T2-weighted imaging hyperintensity connection between the lesions and cortical edema with varying ADC-values was typical for mGBM.

Multisequence MRI-based radiomics analysis for early prediction of the risk of T790M resistance in new brain metastases

Background

Predicting whether T790M emerges early is crucial to the adjustment of targeted drugs for non-small cell lung cancer (NSCLC) patients. This study aimed to evaluate the risk of T790M resistance in progressive new brain metastases (BMs) based on multisequence magnetic resonance imaging (MRI) radiomics.

Methods

This retrospective study included 405 consecutive patients (training cohort: 294 patients; testing cohort: 111 patients) with proven NSCLC with disease progression of new BM. The radiomics features were separately extracted from T2-weighted imaging (T2WI), T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (T1-CE) sequence of baseline MRI. Then, we calculated radiomics scores (rad-score) of the 4 sequences respectively and established predictive models (lesion- or patient-level) to evaluate T790M resistance within up to 14 months using random forest classifier. Receiver operating characteristic (ROC) curves and F1 scores were used to validate the performance of two models in both the training and testing cohort.

Results

There were significant differences in rad-scores of the four sequences between T790M-positive and negative groups whether in the training or testing cohort (P<0.05). The lesion-level model consisting of rad-scores showed excellent discrimination, with an area under the curve (AUC) and F1-score of 0.879 and 0.798 in the training cohort, and 0.834 and 0.742 in the testing cohort, respectively. The patient-level model also showed a favorable discriminatory ability with an AUC and F1 score of 0.851 and 0.837, which was confirmed with an AUC and F1 score of 0.734 and 0.716 in the testing cohort.

Conclusions

The MRI-based radiomics signatures may be new markers to identify patients at high risk of developing resistance in the early period.

Follicular carcinoma of the thyroid presenting as metastasis in the wall of an arachnoid cyst

The brain is an uncommon site for metastases of differentiated thyroid carcinoma with the most common location being cerebral hemispheres, followed by cerebellum and pituitary gland. Metastasis in the wall of an arachnoid cyst is exceedingly rare with single case report available in the published literature. Arachnoid cyst metastasis from an extraneuraxial malignancy has not been published until. We present a unique case of thyroid carcinoma metastasizing to the wall of an intracranial arachnoid cyst and the most interesting fact is that it was the first clinical manifestation of her malignancy.

Predicting EGFR T790M Mutation in Brain Metastases Using Multisequence MRI-Based Radiomics Signature

RATIONALE AND OBJECTIVES

Timely identifying T790M mutation for non-small cell lung cancer (NSCLC) patients with brain metastases (BM) is essential to adjust targeted treatment strategies. To develop and validate radiomics models based on multisequence MRI for differentiating patients with T790M resistance from no T790M mutation in BM and explore the optimal sequence for prediction.

MATERIALS AND METHODS

This retrospective study enrolled 233 patients with proven of BM in NSCLC which included 95 with T790M and 138 without T790M from two hospitals as the training cohort and testing cohort separately. Radiomics features extracted from T2WI, T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (T1-CE) sequence respectively. The most predictable features were selected based on the maximal information coefficient and Boruta method. Then four radiomics models were built to characterize T790M mutation by random forest classifier. ROC curves, F1 score and DCA curves were constructed to validate the capability and verify the performance of four models.

RESULTS

The DWI model showed best performance with AUC and F1 score of 0.886 and 0.789 in the training cohort, 0.850 and 0.743 in the testing cohort. DCA curves also showed higher overall net benefit from the DWI model than from the remaining three models in the testing cohort. Other three models also had some classification power whether in the training or testing cohort, especially T2-FLAIR model.

CONCLUSION

Multisequence MRI-based radiomics has potential to predict the emergence of EGFR T790M resistance mutations especially the radiomics signature based on DWI sequence.

Magnetic resonance imaging characteristics of brain metastases in small cell lung cancer

BACKGROUND

Lung is the most common primary site of brain metastases (BMs). For different pathological types of BMs have some similar characteristics, it is still a challenge to confirm the origin based on their characteristics directly. BMs of small cell lung cancer (SCLC) have favorable therapeutic expectations due to their high sensitivity to radiotherapy. This study sought to identify unique characteristics of BMs in SCLC, aiming to assist in clinical decision-making.

METHODS

Patients diagnosed with BMs of lung cancer who received radiotherapy from January 2017 to January 2022 were reviewed (N = 284). Definitive diagnosis of BMs of SCLC was reached for 36 patients. All patients underwent head examination using magnetic resonance imaging. The number, size, location, and signal characteristics of lesions were analyzed.

RESULTS

There were 7 and 29 patients with single focus and non-single focus, respectively. Ten patients had diffuse lesions, and the remaining 26 patients had a total of 90 lesions. These lesions were divided into three groups according to size: <1, 1-3, and >3 cm (43.33%, 53.34%, and 3.33%, respectively). Sixty-six lesions were located in the supratentorial area, primarily including cortical and subcortical lesions (55.56%) and deep brain lesions (20%). Moreover, 22 lesions were located in the infratentorial area. According to diffusion-weighted imaging and T1-weighted contrast enhancement, the imaging characteristics were classified into six patterns. Hyperintensity in diffusion-weighted imaging and homogeneous enhancement was the most common pattern of BMs in SCLC (46.67%), while partial lesions showed hyperintensity in diffusion-weighted imaging without enhancement (7.78%).

CONCLUSIONS

The manifestations of BMs in SCLC were multiple lesions (diameter: 1-3 cm), hyperintensity in diffusion-weighted imaging, and homogeneous enhancement. Interestingly, hyperintensity in diffusion-weighted imaging without enhancement was also one of the characteristics.

Imaging of brain metastasis in non-small-cell lung cancer: indications, protocols, diagnosis, post-therapy imaging, and implications regarding management

Increased survival (due to the use of targeted therapies based on genomic profiling) has resulted in the increased incidence of brain metastasis during the course of disease, and thus, made it essential to have proper imaging guidelines in place for brain metastasis from non-small-cell lung cancer (NSCLC). Brain parenchymal metastases can have varied imaging appearances, and it is pertinent to be aware of the various molecular risk factors for brain metastasis from NSCLC along with their suggestive imaging appearances, so as to identify them early. Leptomeningeal metastasis requires additional imaging of the spine and an early cerebrospinal fluid (CSF) analysis. Differentiation of post-therapy change from recurrence on imaging has a bearing on the management, hence the need for its awareness. This article will provide in-depth literature review of the epidemiology, aetiopathogenesis, screening, detection, diagnosis, post-therapy imaging, and implications regarding the management of brain metastasis from NSCLC. In addition, we will also briefly highlight the role of artificial intelligence (AI) in brain metastasis screening.

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.

Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review

DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.

Differences in apparent diffusion coefficient histogram analysis according to EGFR mutation status in brain metastasis due to lung adenocarcinoma

BACKGROUND

The etiology, clinicopathological features, and prognosis of cancer in cases with EGFR mutations are different from those without mutations.OBJECTİVE:This study aims to evaluate the differences in ADC histogram analysis in brain metastases with EGFR mutation status in lung adenocarcinoma cases and the relationship between ADC histogram analysis differences and overall survival.

METHODS

In this retrospective case-control study, 30 patients (8 EGFR+/22 EGFR-) and 51 brain metastases (15 EGFR+/36 EGFR-) were included. ROI markings are first performed from each section, including metastasis in ADC mapping using FIREVOXEL software. Next, ADC histogram parameters are calculated. Overall survival analysis after brain metastasis (OSBM) is defined as the time from initial brain metastasis diagnosis to the time of death or last follow-up. Patient-based (by evaluating the largest lesion) and lesion-based (by evaluating all measurable lesions) statistical analyses are then performed.

RESULTS

In the lesion-based analysis, skewness values are lower in EGFR+ patients, which is statistically significant (p = 0.012). The two groups have no significant difference regarding other ADC histogram analysis parameters, mortality, and overall survival (p > 0.05). In the ROC analysis, the most appropriate skewness cut-off value is determined as 0.321 to distinguish the EGFR mutation difference, and this value is statistically significant (sensitivity: 66.7%, specificity: 80.6%, AUC: 0.730) (p = 0.006).CONCLUSİON:The findings of this study provide valuable insights into the differences in ADC histogram analysis according to EGFR mutation status in brain metastases due to lung adenocarcinoma. The identified parameters, especially skewness, are potentially non-invasive biomarkers for predicting mutation status. Incorporating these biomarkers into routine clinical practice may aid treatment decision-making and prognostic assessment for patients. Further validation studies and prospective investigations are warranted to confirm the clinical utility of these findings and establish their potential for personalized therapeutic strategies and patient outcomes.

Band-Like Brainstem Lesion in a Patient With a History of Lung Adenocarcinoma

Leptomeningeal metastasis (LM) is a severe complication of primary malignancy that has spread to the leptomeninges and cerebrospinal fluid (CSF). Here, we report a patient whose magnetic resonance imaging (MRI) showed a unique brainstem lesion suspicious of LM. A 72-year-old man presented with dizziness, gait instability, and cognitive decline, primarily object naming. He had a history of lung adenocarcinoma with epidermal growth factor receptor (EGFR) mutation. Brain MRI revealed a band-like lesion surrounding the ventral brainstem with T2 weighted-image/fluid attenuation inversion recovery (FLAIR) imaging and diffusion-weighted imaging (DWI) hyperintensity without gadolinium enhancement. No malignant cells were detected in the CSF. He underwent ventriculoperitoneal shunt two months after the onset, and his gait improved, but his cognitive function declined further. Recent reports suggest similar brainstem lesions as a unique LM pattern, which occurs almost exclusively in patients with lung adenocarcinoma with EGFR mutation. Therefore, if MRI shows this brainstem finding, repeated and appropriate CSF cytology is needed to detect tumor cells. Furthermore, if a patient with lung adenocarcinoma shows a cognitive decline, cerebral LM and auto-antibodies that mainly target neuronal surface antigens should be considered.

Role of apparent diffusion coefficient measurement in differentiating histological subtypes of brain metastasis of lung cancer

OBJECTIVE:

The aim of this study was to investigate the role of apparent diffusion coefficient of diffusion-weighted imaging in differentiating histological subtypes of brain metastasis of lung cancer.

METHODS:

Diffusion-weighted imaging of 158 patients (mean age: 61.2±10.68 years) with brain metastasis of lung cancer (36 small cell lung cancer and 122 non-small cell lung cancer) were retrospectively evaluated. The minimum and mean apparent diffusion coefficient values of the metastasis, apparent diffusion coefficient of edema around the metastasis, and apparent diffusion coefficient of contralateral brain parenchyma were measured. Normalized apparent diffusion coefficient was calculated by proportioning the mean apparent diffusion coefficient of the metastasis to the apparent diffusion coefficient of the contralateral brain parenchyma. Minimum and mean apparent diffusion coefficient of the metastasis, apparent diffusion coefficient of edema around metastasis, and normalized apparent diffusion coefficient were compared between small cell lung cancer and non-small cell lung cancer metastases.

RESULTS:

Minimum apparent diffusion coefficient, mean apparent diffusion coefficient, and normalized apparent diffusion coefficient values of small cell lung cancer metastases (0.43±0.19×10⁻³mm²/s, 0.63±0.20×10⁻³mm²/s, and 0.81 [0.55–1.44], respectively) were significantly lower than those of non-small cell lung cancer metastases (0.71±0.26×10⁻³mm²/s, 0.93±0.29×10⁻³mm²/s, and 1.30 [0.60–3.20], respectively; p<0.001). Mean apparent diffusion coefficient of edema of small cell lung cancer metastases (1.21±0.28×10⁻³mm²/s) was significantly lower than that of non-small cell lung cancer metastases (1.39±0.26×10⁻³mm²/s, p=0.020). The best cutoff values of minimum apparent diffusion coefficient, mean apparent diffusion coefficient, normalized apparent diffusion coefficient, and apparent diffusion coefficient of edema for the differentiation of small cell lung cancer and non-small cell lung cancer were found to be 0.56×10⁻³mm²/s, 0.82×10⁻³mm²/s, 1.085, and 1.21×10⁻³mm²/s, respectively. The area under the receiver operating characteristic curve, sensitivity, and specificity values were, respectively, 0.812, 80.6, and 73.8% for minimum apparent diffusion coefficient; 0.825, 91.7, and 61.5% for mean apparent diffusion coefficient; 0.845, 80.6, and 73.8% for normalized apparent diffusion coefficient; and 0.698, 75.0, and 67.7% for apparent diffusion coefficient of edema.

CONCLUSIONS:

Minimum apparent diffusion coefficient, mean apparent diffusion coefficient, normalized apparent diffusion coefficient, and apparent diffusion coefficient of edema around metastasis can differentiate histological subtypes of brain metastasis of lung cancer.

The Extent of Necrosis in Brain Metastases May Predict Subtypes of Primary Cancer and Overall Survival in Patients Receiving Craniotomy

Although necrosis is common in brain metastasis (BM), its biological and clinical significances remain unknown. We evaluated necrosis extent differences by primary cancer subtype and correlated BM necrosis to overall survival post-craniotomy. We analyzed 145 BMs of patients receiving craniotomy. Necrosis to tumor ratio (NTR) was measured. Patients were divided into two groups by NTR: BMs with sparse necrosis and with abundant necrosis. Clinical features were compared. To investigate factor relevance for BM necrosis, multivariate logistic regression, random forests, and gradient boosting machine analyses were performed. Kaplan−Meier analysis and log-rank tests were performed to evaluate the effect of BM necrosis on overall survival. Lung cancer was a more common origin for BMs with abundant necrosis (42/72, 58.33%) versus sparse necrosis (23/73, 31.51%, p < 0.01). Primary cancer subtype and tumor volume were the most relevant factors for BM necrosis (p < 0.01). BMs harboring moderately abundant necrosis showed longer survival, versus sparse or highly abundant necrosis (p = 0.04). Lung cancer BM may carry larger necrosis than BMs from other cancers. Further, moderately abundant necrosis in BM may predict a good prognosis post-craniotomy.

Branch-like enhancement on contrast enhanced MRI is a specific finding of cerebellar lymphoma compared with other pathologies

Branch-like enhancement (BLE) on contrast-enhanced (CE) magnetic resonance imaging (MRI) was found to be effective in differentiating primary central nervous system lymphoma (PCNSL) from high-grade glioma (HGG) in the cerebellum. However, whether it can be applied to assessments of secondary central nervous system lymphoma (SCNSL), or other cerebellar lesions is unknown. Hence, we retrospectively reviewed cerebellar masses to investigate the use of BLE in differentiating cerebellar lymphoma (CL), both primary and secondary, from other lesions. Two reviewers qualitatively evaluated the presence and degree of BLE on CE-T1 weighted imaging (T1WI). If multiple views were available, we determined the view in which BLE was the most visible. Seventy-five patients with the following pathologies were identified:17 patients with CL, 30 patients with metastasis, 12 patients with hemangioblastoma, 9 patients with HGG, and 7 patients with others. Twelve patients presented with PCNSL and five with SCNSL. Of 17 patients with CL, 15 (88%) had BLE, whereas three (5%) out of 58 patients in the non-CL group showed BLE. In patients who underwent three-dimensional-CE-T1WI, BLE was the most visible on the sagittal image. In conclusion, BLE is a highly specific finding for CL and the sagittal image is important in evaluating this finding.

Advanced MRI to differentiate schwannomas and metastases in the cerebellopontine angle/internal auditory canal

BACKGROUND AND PURPOSE

Differentiating schwannomas and metastases in the cerebellopontine angles (CPA)/internal auditory canals (IAC) can be challenging. This study aimed to assess the role of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) to differentiate schwannomas and metastases in the CPA/IAC.

METHODS

We retrospectively reviewed 368 patients who were diagnosed with schwannomas or metastases in the CPA/IAC between April 2017 and February 2022 in a single academic center. Forty-three patients had pretreatment DWI and DCE-MRI along with conventional MRI. Normalized mean apparent diffusion coefficient ratio (nADCmean) and DCE-MRI parameters of fractional plasma volume (Vp), flux rate constant (Kep), and forward volume transfer constant were compared along with patients' demographics and conventional imaging features between schwannomas and metastases as appropriate. The diagnostic performances and multivariate logistic regression analysis were performed using the significantly different values.

RESULTS

Between 23 schwannomas (15 males; median 48 years) and 20 metastases (9 males; median 61 years), nADCmean (median: 1.69 vs. 1.43; p = .002), Vp (median: 0.05 vs. 0.20; p < .001), and Kep (median: 0.41 vs. 0.81 minute^-1 ; p < .001) were significantly different. The diagnostic performances of nADCmean, Vp, and Kep were 0.77, 0.90, and 0.83 area under the curves, with cutoff values of 1.68, 0.12, and 0.53, respectively. Vp was identified as the most significant parameter for the tumor differentiation in the multivariate logistic regression analysis (p < .001).

CONCLUSIONS

DWI and DCE-MRI can help differentiate CPA/IAC schwannomas and metastases, and Vp is the most significant parameter.

Magnetic Resonance Imaging Segmentation on the Basis of Boundary Tracking Algorithm in Lung Cancer Surgery

This work was to study the guiding value of magnetic resonance imaging (MRI) based on the target region boundary tracking algorithm in lung cancer surgery. In this study, the traditional boundary tracking algorithm was optimized, and the target neighborhood point boundary tracking method was proposed. The iterative method was used to binarize the lung MRI image, which was applied to the MRI images of 50 lung cancer patients in hospital. The patients were divided into two groups as the progression-free survival (PFS) and overall survival (OS) of surgical treatment group (experimental group, n = 25) and nonsurgical treatment group (control group, n = 25). The experimental group received surgical resection, while the control group received systemic chemotherapy. The results showed that the traditional boundary tracking algorithm needed to manually rejudge whether the concave and convex parts of the image were missing. The target boundary tracking algorithm can effectively avoid the leakage of concave and convex parts and accurately locate the target image contour, fast operation, without manual intervention. The PFS time of the experimental group (325 days) was significantly higher than that of the control group (186 days) (P < 0.05). The OS time of the experimental group (697 days) was significantly higher than that of the control group (428 days) (P < 0.05). Fisher exact probability method was used to test the total survival time of patients in the two groups, and the tumor classification and treatment group had significant influence on the OS time (P < 0.05). The target boundary tracking algorithm in this study can effectively locate the contour of the target image, and the operation speed was fast. Surgical resection of lung cancer can improve the PFS and OS of patients.

Clinico-radiological features of brain metastases from thyroid cancer

The brain is an unusual site for distant metastases of thyroid cancer. The radiological features of brain metastases (BMs) have rarely been reported. Hemorrhage is frequently noted in BMs from thyroid cancer. This study aimed to investigate the clinico-radiological features of BMs from thyroid cancer and to determine the risk factors to predict BM hemorrhage.We retrospectively evaluated the MR images of 35 patients with BMs from thyroid cancer at our hospital from 2013 to 2020. The number, size, site, presence of extra-cranial metastasis, presence of perilesional edema, intra-tumoral hemorrhage, enhancement pattern, and presence of diffusion restriction on MRI were described. We further classified the thyroid cancers into hemorrhagic and nonhemorrhagic groups to investigate the factors associated with hemorrhage.54.29% of patients with thyroid BMs (19/35) had neurologic symptoms. 94.29% of patients (33/35) had extra-cranial metastases. The most common histology of primary thyroid cancer was papillary thyroid cancer (71.43%, 25/35), followed by anaplastic thyroid cancer (22.86%, 8/35). Thyroid cancer BMs were located mostly in the supra-tentorium (51.43%, 18/35) or both the supra and infra-tentorium (45.71%, 16/35). 60% of patients (21/35) showed hemorrhage within the BMs. The strongest predictor for BM hemorrhage was tumor size (variable importance: 50).Thyroid cancer BMs exhibit a bleeding tendency. Furthermore, larger BMs are more likely to have an intra-tumoral hemorrhage.

Differentiation of brain metastases originating from lung and breast cancers using apparent diffusion coefficient histogram analysis and the relation of histogram parameters with Ki-67

PURPOSE

A fast, reliable and non-invasive method is required in differentiating brain metastases (BMs) originating from lung cancer (LC) and breast cancer (BC). The aims of this study were to assess the role of histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating BMs originated from LC and BC, and then to investigate further the association of ADC histogram parameters with Ki-67 index in BMs.

METHODS

A total of 55 patients (LC, N = 40; BC, N = 15) with BMs histopathologically confirmed were enrolled in the study. The LC group was divided into small-cell lung cancer (SCLC; N = 15) and non-small-cell lung cancer (NSCLC; N = 25) groups. ADC histogram parameters (ADC_max, ADC_mean, ADC_min, ADC_median, ADC₁₀, ADC₂₅, ADC₇₅ and ADC₉₀, skewness, kurtosis and entropy) were derived from ADC maps. Mann-Whitney U-test, independent samples t-test, receiver operating characteristic (ROC) analysis and Spearman correlation analysis were used for statistical assessment.

RESULTS

ADC histogram parameters did not show significant differences between LC and BC groups (p > 0.05). Subgroup analysis showed that various ADC histogram parameters were found to be statistically lower in the SCLC group compared to the NSCLC and BC groups (p < 0.05). ROC analysis showed that ADC_mean and ADC₁₀ for differentiating SCLC BMs from NSCLC, and ADC₂₅ for differentiating SCLC BMs from BC achieved optimal diagnostic performances. Various histogram parameters were found to be significantly correlated with Ki-67 (p < 0.05).

CONCLUSION

Histogram analysis of ADC maps may reflect tumoural proliferation potential in BMs and can be useful in differentiating SCLC BMs from NSCLC and BC BMs.

Tumor habitat analysis by magnetic resonance imaging distinguishes tumor progression from radiation necrosis in brain metastases after stereotactic radiosurgery

OBJECTIVES

The identification of viable tumor after stereotactic radiosurgery (SRS) is important for future targeted therapy. This study aimed to determine whether tumor habitat on structural and physiologic MRI can distinguish viable tumor from radiation necrosis of brain metastases after SRS.

METHOD

Multiparametric contrast-enhanced T1- and T2-weighted imaging, apparent diffusion coefficient (ADC), and cerebral blood volume (CBV) were obtained from 52 patients with 69 metastases, showing enlarging enhancing masses after SRS. Voxel-wise clustering identified three structural MRI habitats (enhancing, solid low-enhancing, and nonviable) and three physiologic MRI habitats (hypervascular cellular, hypovascular cellular, and nonviable). Habitat-based predictors for viable tumor or radiation necrosis were identified by logistic regression. Performance was validated using the area under the curve (AUC) of the receiver operating characteristics curve in an independent dataset with 24 patients.

RESULTS

None of the physiologic MRI habitats was indicative of viable tumor. Viable tumor was predicted by a high-volume fraction of solid low-enhancing habitat (low T2-weighted and low CE-T1-weighted values; odds ratio [OR] 1.74, p <.001) and a low-volume fraction of nonviable tissue habitat (high T2-weighted and low CE-T1-weighted values; OR 0.55, p <.001). Combined structural MRI habitats yielded good discriminatory ability in both development (AUC 0.85, 95% confidence interval [CI]: 0.77-0.94) and validation sets (AUC 0.86, 95% CI:0.70-0.99), outperforming single ADC (AUC 0.64) and CBV (AUC 0.58) values. The site of progression matched with the solid low-enhancing habitat (72%, 8/11).

CONCLUSION

Solid low-enhancing and nonviable tissue habitats on structural MRI can help to localize viable tumor in patients with brain metastases after SRS.

KEY POINTS

• Structural MRI habitats helped to differentiate viable tumor from radiation necrosis. • Solid low-enhancing habitat was most helpful to find viable tumor. • Providing spatial information, the site of progression matched with solid low-enhancing habitat.

Differentiation of brain metastases from small and non-small lung cancers using apparent diffusion coefficient (ADC) maps

BACKGROUND

Brain metastases are particularly common in patients with small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), with NSCLC showing a less  aggressive clinical course and lower chemo- and radio sensitivity compared to SCLC. Early adequate therapy is highly desirable and depends on a reliable classification of tumor type. The apparent diffusion coefficient is a noninvasive neuroimaging marker with the potential to differentiate between major histological subtypes. Here we determine the sensitivity and specificity of the apparent diffusion coefficient to distinguish between NSCLC and SCLC.

METHODS

We enrolled all NSCLC and SCLC patients diagnosed between 2008 and 2019 at the University Medical Center Göttingen. Cranial MR scans were visually inspected for brain metastases and the ratio of the apparent diffusion coefficient (ADC) was calculated by dividing the ADC measured within the solid part of a metastasis by a reference ADC extracted from an equivalent region in unaffected tissue on the contralateral hemisphere.

RESULTS

Out of 411 enrolled patients, we detected 129 patients (83 NSCLC, 46 SCLC) with sufficiently large brain metastases with histologically classified lung cancer and no hemorrhage. We analyzed 185 brain metastases, 84 of SCLC and 101 of NSCLC. SCLC brain metastases showed an ADC ratio of 0.68 ± 0.12 SD, and NSCLC brain metastases showed an ADC ratio of 1.47 ± 0.31 SD. Receiver operating curve statistics differentiated brain metastases of NSCLC from SCLC with an area under the curve of 0.99 and a 95% CI of 0.98 to 1, p < 0.001. Youden's J cut-point is 0.97 at a sensitivity of 0.989 and a specificity of 0.988.

CONCLUSIONS

In patients with lung cancer and brain metastases with solid tumor parts, ADC ratio enables an ad hoc differentiation of SCLC and NSCLC, easily achieved during routine neuroradiological examination. Non-invasive MR imaging enables an early-individualized management of brain metastases from lung cancer.

TRIAL REGISTRATION

The study was registered in the German Clinical Trials Register (DRKS00023016).

The Utility and Efficiency of Diffusion Tensor Imaging Values to Determine Epidermal Growth Factor Receptor Gene Mutation Status in Brain Metastasis from Lung Adenocarcinoma; A Preliminary Study

BACKGROUND

This study aims to investigate the existence of any Diffusion Tensor Imaging (DTI) value differences in Brain Metastases (BM) due to lung adenocarcinoma based on the Epidermal Growth Factor Receptor (EGFR) gene mutation status.

MATERIAL AND METHODS

17 patients with 32 solid intracranial metastatic lesions from lung adenocarcinoma were included prospectively. Patients were divided according to the EGFR mutation status as EGFR (+) (group 1, n:8) and EGFR wild type (group 2, n:9). The Fractional Anisotropy (FA), apparent diffusion coefficient (ADC), normalized ADC (nADC), Axial Diffusivity (AD), and Radial Diffusivity (RD) values were measured from the solid component of the metastatic lesions and nADC values were calculated. DTI values were compared between group 1 and group 2. The receiver-operating characteristic analysis was used to obtain cut-off values for the parameters presenting a statistical difference between the EGFR gene mutation-positive and wild type group.

RESULTS

There were statistically significant differences in measured ADC, nADC, AD, and RD values between group 1 and group 2. The ADC, nADC, AD, and RD values were significantly lower in group 1. There was no significant difference in FA values between the two groups. Analysis by the ROC curve method revealed a cut-off value of ≤721 x 10-6 mm2/s for ADC (Sensitivity= 72.7, Specificity=85.7); ≤0.820 for nADC (Sensitivity=72.7, Specificity=90.5), ≤ 886 for AD (Sensitivity=81.8, Specificity=81.0), and ≤588 for RD (Sensitivity=63.6, Specificity=90.5) in differentiating EGFR mutation (+) group from wild type group.

CONCLUSION

A combination of the decreased ADC, nADC, AD, and RD values in BM due to lung adenocarcinoma can be important for predicting the EGFR gene mutation status. DTI features of the brain metastases from lung adenocarcinoma may be utilized to provide insight into the EGFR mutation status and guide the clinicians for the initiation of targeted therapy.

Diffusion tensor and postcontrast T1-weighted imaging radiomics to differentiate the epidermal growth factor receptor mutation status of brain metastases from non-small cell lung cancer

PURPOSE

To assess whether the radiomic features of diffusion tensor imaging (DTI) and conventional postcontrast T1-weighted (T1C) images can differentiate the epidermal growth factor receptor (EGFR) mutation status in brain metastases from non-small cell lung cancer (NSCLC).

METHODS

A total of 99 brain metastases in 51 patients who underwent surgery or biopsy with underlying NSCLC and known EGFR mutation statuses (57 from EGFR wild type, 42 from EGFR mutant) were allocated to the training (57 lesions in 31 patients) and test (42 lesions in 20 patients) sets. Radiomic features (n = 526) were extracted from preoperative MR images including T1C and DTI. Radiomics classifiers were constructed by combinations of five feature selectors and four machine learning algorithms. The trained classifiers were validated on the test set, and the classifier performance was assessed by determining the area under the curve (AUC).

RESULTS

EGFR mutation status showed an overall discordance rate of 12% between the primary tumors and corresponding brain metastases. The best performing classifier was a combination of the tree-based feature selection and linear discriminant algorithm and 5 features were selected (1 from ADC, 2 from fractional anisotropy, and 2 from T1C images), resulting in an AUC, accuracy, sensitivity, and specificity of 0.73, 78.6%, 81.3%, and 76.9% in the test set, respectively.

CONCLUSIONS

Radiomics classifiers integrating multiparametric MRI parameters may have potential in differentiating the EGFR mutation status in brain metastases from NSCLC.

Histogram Analysis of ADC Maps for Differentiating Brain Metastases From Different Histological Types of Lung Cancers

PURPOSE

Our study aimed to investigate the role of histogram analysis derived from apparent diffusion coefficient (ADC) maps in brain metastases (BMs) from lung cancer for differentiating histological subtype.

METHODS

A total of 61 BMs (45 non-small cell lung cancer [NSCLC] comprising 32 adenocarcinoma [AC], 13 squamous cell carcinoma [SCC], and 16 small-cell lung cancer [SCLC]) in 50 patients with histopathologically confirmed lung cancer were retrospectively included in this study. Pretreatment cranial diffusion-weighted imaging was performed, and the corresponding ADC maps were generated. Regions of interest were drawn on solid components of the BM on all slices of the ADC maps to obtain parameters, including ADC_max, ADC_mean, ADC_min, ADC_median, ADC_range, skewness, kurtosis, entropy, ADC₁₀, ADC₂₅, ADC₇₅, and ADC₉₀. Apparent diffusion coefficient histogram parameters were compared among histological type groups. Kruskal-Wallis, Mann-Whitney U, chi-square tests, and receiver-operating characteristic (ROC) curve were used for statistical assessment.

RESULTS

ADC_min, ADC₁₀, and ADC₂₅ were found to be significantly different among AC, SCC, and SCLC groups; these parameters were higher for AC group, moderate for SCC group, and significantly lower for SCLC group. Skewness and kurtosis were not significantly different among all groups. The ROC analysis for differentiating BMs of NSCLC from SCLC showed that ADC₂₅ achieved the highest area under the curve at 0.922 with 93.02% sensitivity and 81.25% specificity.

CONCLUSION

Apparent diffusion coefficient histogram analysis of BMs from lung cancer has significant prognostic value in differentiating histological subtypes of lung cancer.

Contrast-enhanced T1-weighted image radiomics of brain metastases may predict EGFR mutation status in primary lung cancer

Identification of EGFR mutations is critical to the treatment of primary lung cancer and brain metastases (BMs). Here, we explored whether radiomic features of contrast-enhanced T1-weighted images (T1WIs) of BMs predict EGFR mutation status in primary lung cancer cases. In total, 1209 features were extracted from the contrast-enhanced T1WIs of 61 patients with 210 measurable BMs. Feature selection and classification were optimized using several machine learning algorithms. Ten-fold cross-validation was applied to the T1WI BM dataset (189 BMs for training and 21 BMs for the test set). Area under receiver operating characteristic curves (AUC), accuracy, sensitivity, and specificity were calculated. Subgroup analyses were also performed according to metastasis size. For all measurable BMs, random forest (RF) classification with RF selection demonstrated the highest diagnostic performance for identifying EGFR mutation (AUC: 86.81). Support vector machine and AdaBoost were comparable to RF classification. Subgroup analyses revealed that small BMs had the highest AUC (89.09). The diagnostic performance for large BMs was lower than that for small BMs (the highest AUC: 78.22). Contrast-enhanced T1-weighted image radiomics of brain metastases predicted the EGFR mutation status of lung cancer BMs with good diagnostic performance. However, further study is necessary to apply this algorithm more widely and to larger BMs.

Patterns of brain metastasis in anaplastic lymphoma kinase - rearranged and epidermal growth factor receptor-mutated lung cancer patients in magnetic resonance imaging

Introduction:

The optimal management of neuroparenchymal lesions in cases of lung cancer is exigent as this frequent yet notorious complication negatively impacts the morbidity and mortality index.

Aims:

This study is aimed at recognizing various patterns of neuroparenchymal metastasis in patients of lung cancer with epidermal growth factor receptor (EGFR)- and anaplastic lymphoma kinase (ALK)-positive mutations.

Material and Methods:

The radiological findings of the neuroparenchymal lesions were analyzed and the statistical data were charted. We identified two groups of patients with neuroparenchymal lesions among a cohort of 340 patients having EGFR-positive (68) and ALK-positive (24) mutations (total: 24 + 68 = 92).

Results:

We observed that among the ALK group, leptomeningeal spread was less compared to EGFR group (2/24 as opposed to 18/68). Morphological heterogeneity and central necrosis in the parenchymal lesion which were associated with unfavorable outcomes were predominant in ALK group (8/24) as opposed to EGFR group (2/68). Ancillary findings but pertinent to survival and morbidity such as presence of perilesional edema, hemorrhage, and hydrocephalus on magnetic resonance imaging were also analyzed. The mutation-specific differential imaging spectrum could be attributed to biological differences between these cancers.

Brain MRI imaging characteristics predict treatment response and outcome in patients with de novo brain metastasis of EGFR-mutated NSCLC

Patients with non-small cell lung cancer (NSCLC) and de novo brain metastasis (BM) have poor prognosis. We aim to investigate the characteristic of brain magnetic resonance (MR) imaging and the association with the treatment response of epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) for lung cancer with BM.EGFR-mutated NSCLC patients with BM from October 2013 to December 2017 in a tertiary referral center were retrospectively analyzed. Patient's age, sex, cell type, EGFR mutation status, treatment, and characteristics of BM were collected. Survival analysis was performed using Kaplan-Meier method. The efficacy of different EGFR-TKIs were also analyzed.Among the 257 eligible patients, 144 patients with Exon 19 deletion or Exon 21 L858R were included for analysis. The erlotinib group had the best progression free survival (PFS) (median PFS 13 months, P = .04). The overall survival (OS) revealed no significant difference between three EGFR-TKI groups. Brain MR imaging features including tumor necrosis, rim enhancement and specific tumor locations (frontal lobe, putamen or cerebellum) were factors associated with poor prognosis. Patients with poor prognostic imaging features, the high-risk group, who received erlotinib had the best PFS (median PFS 12 months, P < .001). However, the OS revealed no significant difference between 3 EGFR-TKI groups. The low risk group patients had similar PFS and OS treated with three different EGFR-TKIs.In NSCLC patients with common EGFR mutation and de novo BM, those with poor prognostic brain MR characteristics, erlotinib provided better PFS than afatinib or gefitinib.

Diffusion kurtosis imaging-derived histogram metrics for prediction of KRAS mutation in rectal adenocarcinoma: Preliminary findings

BACKGROUND

Although histological examination is the standard method for assessing genetic status, the development of a noninvasive method, which can display the heterogeneity of the whole tumor to supplement genotype analysis, might be important for personalized treatment strategies.

PURPOSE

To evaluate the potential role of diffusion kurtosis imaging (DKI)-derived parameters using histogram analysis derived from whole-tumor volumes for prediction of the status of KRAS mutations in patients with rectal adenocarcinoma.

STUDY TYPE

Retrospective.

SUBJECTS

In all, 148 consecutive patients with histologically confirmed rectal adenocarcinoma who were treated at our institution.

SEQUENCE

DKI was performed with a 3.0 T MRI system using a single-shot echo-planar imaging sequence with b values of 0, 700, 1400, and 2100 sec/mm² .

ASSESSMENT

D, K, and apparent diffusion coefficient (ADC) values were measured using whole-tumor volume histogram analysis and were compared between different KRAS mutations status.

STATISTICAL TESTS

Student's t-test or Mann-Whitney U-test, and receiver operating characteristic (ROC) curves were used for statistical analysis.

RESULTS

All the percentile metrics of ADC and D values were significantly lower in the mutated group than those in the wildtype group (all P < 0.05), except for the minimum value of ADC and D (both P > 0.05), while K-related percentile metrics were higher in the mutated group compared with those in the wildtype group (all P < 0.05). Regarding the comparison of the diagnostic performance of all the histogram metrics, K_75th showed the highest AUC value of 0.871, and the corresponding values for sensitivity, specificity, positive predictive value, and negative predictive value were 81.43%, 78.21%, 77.03%, and 82.43%, respectively.

DATA CONCLUSION

DKI metrics with whole-tumor volume histogram analysis is associated with KRAS mutations, and thus may be useful for predicting the KRAS status of rectal cancers for guiding targeted therapy.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:930-939.

Apparent diffusion coefficient histogram in breast cancer brain metastases may predict their biological subtype and progression

Our aims for this study were to investigate the relationship between diffusion weighted image (DWI) parameters of brain metastases (BMs) and biological markers of breast cancer, and moreover, to assess whether DWI parameters accurately predict patient outcomes. DWI data for 34 patients with BMs from breast cancer were retrospectively reviewed. Apparent diffusion coefficient (ADC) histogram parameters were calculated from all measurable BMs. Two region of interest (ROI) methods are used for the analysis: from the largest BM or from all measurable BMs per one patient. ADC histogram parameters were compared between positive and negative groups depending on ER/PR and HER2 statuses. Overall survival analysis after BM (OSBM) and BM-specific progression-free survival (BMPFS) was analyzed with ADC parameters. Regardless of ROI methods, 25th percentile of ADC histogram was significantly lower in the ER/PR-positive group than in the ER/PR-negative group (P < 0.05). Using ROIs from all measurable BMs, Peak location, 50th percentile, 75th percentile, and mean value of ADC histogram were also significantly lower in the ER/PR-positive group than in the ER/PR-negative group (P < 0.05). However, there was no significant difference between HER2-postive and negative group. On univariate analysis, using ROIs from all measurable BMs, lower 25th percentile, 50th percentile and mean of ADC were significant predictors for poor BMPFS. ADC histogram analysis may have a prognostic value over ER/PR status as well as BMPFS.