MRI T2 hypointensity of metastatic brain tumors from gastric and colonic cancers

Radiomics based on brain-to-tumor interface enables prediction of metastatic tumor type of brain metastasis: a proof-of-concept study

BACKGROUND

Early and accurate identification of the metastatic tumor types of brain metastasis (BM) is essential for appropriate treatment and management.

METHODS

A total of 450 patients were enrolled from two centers as a primary cohort who carry 764 BMs originated from non-small cell lung cancer (NSCLC, patient = 173, lesion = 187), small cell lung cancer (SCLC, patient = 84, lesion = 196), breast cancer (BC, patient = 119, lesion = 200), and gastrointestinal cancer (GIC, patient = 74, lesion = 181). A third center enrolled 28 patients who carry 67 BMs (NSCLC = 24, SCLC = 22, BC = 10, and GIC = 11) to form an external test cohort. All patients received contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) MRI scans at 3.0 T before treatment. Radiomics features were calculated from BM and brain-to-tumor interface (BTI) region in the MRI image and screened using least absolute shrinkage and selection operator (LASSO) to construct the radiomics signature (RS). Volume of peritumor edema (VPE) was calculated and combined with RS to create a joint model. Performance of the models was assessed by receiver operating characteristic (ROC).

RESULTS

The BTI-based RS showed better performance compared to BM-based RS. The combined models integrating BTI features and VPE can improve identification performance in AUCs in the training (LC/NLC vs. SCLC/NSCLC vs. BC/GIC, 0.803 vs. 0.949 vs. 0.918), internal validation (LC/NLC vs. SCLC/NSCLC vs. BC/GIC, 0.717 vs. 0.854 vs. 0.840), and external test (LC/NLC vs. SCLC/NSCLC vs. BC/GIC, 0.744 vs. 0.839 vs. 0.800) cohorts.

CONCLUSION

This study indicated that BTI-based radiomics features and VPE are associated with the metastatic tumor types of BM.

Deep Learning With an Attention Mechanism for Differentiating the Origin of Brain Metastasis Using MR images

BACKGROUND

Brain metastasis (BM) is a serious neurological complication of cancer of different origins. The value of deep learning (DL) to identify multiple types of primary origins remains unclear.

PURPOSE

To distinguish primary site of BM and identify the best DL models.

STUDY TYPE

Retrospective.

POPULATION

A total of 449 BM derived from 214 patients (49.5% for female, mean age 58 years) (100 from small cell lung cancer [SCLC], 125 from non-small cell lung cancer [NSCLC], 116 from breast cancer [BC], and 108 from gastrointestinal cancer [GIC]) were included.

FIELD STRENGTH/SEQUENCE

A 3-T, T1 turbo spin echo (T1-TSE), T2-TSE, T2FLAIR-TSE, DWI echo-planar imaging (DWI-EPI) and contrast-enhanced T1-TSE (CE T1-TSE).

ASSESSMENT

Lesions were divided into training (n = 285, 153 patients), testing (n = 122, 93 patients), and independent testing cohorts (n = 42, 34 patients). Three-dimensional residual network (3D-ResNet), named 3D ResNet6 and 3D ResNet 18, was proposed for identifying the four origins based on single MRI and combined MRI (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI, CE-T1WI + T2WI + DWI). DL model was used to distinguish lung cancer from non-lung cancer; then SCLC vs. NSCLC for lung cancer classification and BC vs. GIC for non-lung cancer classification was performed. A subjective visual analysis was implemented and compared with DL models. Gradient-weighted class activation mapping (Grad-CAM) was used to visualize the model by heatmaps.

STATISTICAL TESTS

The area under the receiver operating characteristics curve (AUC) assess each classification performance.

RESULTS

3D ResNet18 with Grad-CAM and AIC showed better performance than 3DResNet6, 3DResNet18 and the radiologist for distinguishing lung cancer from non-lung cancer, SCLC from NSCLC, and BC from GIC. For single MRI sequence, T1WI, DWI, and CE-T1WI performed best for lung cancer vs. non-lung cancer, SCLC vs. NSCLC, and BC vs. GIC classifications. The AUC ranged from 0.675 to 0.876 and from 0.684 to 0.800 regarding the testing and independent testing datasets, respectively. For combined MRI sequences, the combination of CE-T1WI + T2WI + DWI performed better for BC vs. GIC (AUCs of 0.788 and 0.848 on testing and independent testing datasets, respectively), while the combined MRI approach (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI) could not achieve higher AUCs for lung cancer vs. non-lung cancer, SCLC vs. NSCLC. Grad-CAM helped for model visualization by heatmaps that focused on tumor regions.

DATA CONCLUSION

DL models may help to distinguish the origins of BM based on MRI data.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

MR imaging profile and histopathological characteristics of tumour vasculature, cell density and proliferation rate define two distinct growth patterns of human brain metastases from lung cancer

PURPOSE

Non-invasive prediction of the tumour of origin giving rise to brain metastases (BMs) using MRI measurements obtained in radiological routine and elucidating the biological basis by matched histopathological analysis.

METHODS

Preoperative MRI and histological parameters of 95 BM patients (female, 50; mean age 59.6 ± 11.5 years) suffering from different primary tumours were retrospectively analysed. MR features were assessed by region of interest (ROI) measurements of signal intensities on unenhanced T1-, T2-, diffusion-weighted imaging and apparent diffusion coefficient (ADC) normalised to an internal reference ROI. Furthermore, we assessed BM size and oedema as well as cell density, proliferation rate, microvessel density and vessel area as histopathological parameters.

RESULTS

Applying recursive partitioning conditional inference trees, only histopathological parameters could stratify the primary tumour entities. We identified two distinct BM growth patterns depending on their proliferative status: Ki67high BMs were larger (p = 0.02), showed less peritumoural oedema (p = 0.02) and showed a trend towards higher cell density (p = 0.05). Furthermore, Ki67high BMs were associated with higher DWI signals (p = 0.03) and reduced ADC values (p = 0.004). Vessel density was strongly reduced in Ki67high BM (p < 0.001). These features differentiated between lung cancer BM entities (p ≤ 0.03 for all features) with SCLCs representing predominantly the Ki67high group, while NSCLCs rather matching with Ki67low features.

CONCLUSION

Interpretable and easy to obtain MRI features may not be sufficient to predict directly the primary tumour entity of BM but seem to have the potential to aid differentiating high- and low-proliferative BMs, such as SCLC and NSCLC.

Brain metastasis from gastric adenocarcinoma: A large comprehensive population-based cohort study on risk factors and prognosis

Aims

Although brain metastasis from gastric adenocarcinoma (GaC) is rare, it may significantly affect survival and quality of life. The aim of this large, comprehensive, population-based cohort investigation was to investigate factors that were associated with brain metastasis from GaC and to explore the prognostic factors and time-dependent cumulative mortalities among cases with GaC and brain involvement.

Methods

Population-based information on cases with GaC diagnosed from 2010 to 2016 was obtained from a large-scale database. Factors that were associated with brain metastasis were investigated utilizing multivariable logistic regression. Time-dependent tumor-specific mortalities of cases with GaC and brain involvement were then computed utilizing the cumulative incidence functions (CIFs), and mortalities were compared between subgroups utilizing Gray's test. Factors that were associated with death were further evaluated utilizing multivariable Fine-Gray subdistribution hazard regression.

Results

Together, 28,736 eligible cases were included, which comprised 231 (1%) cases with brain metastasis and 10,801 (38%) with metastasis to other sites, encompassing a follow-up of 39,168 person-years. Brain metastasis occurred more often among younger patients (within overall cancers), in cases with stomach cardia tumors, within cases with signet-ring cell carcinoma (within overall cancers), and within cases with positive lymph nodes (within overall tumors); it was less often detected among black people. Brain involvement was associated with more lung and bone metastases. The median survival time of cases having brain metastasis was only 3 months; the 6- and 12-month tumor-specific cumulative mortalities were 57% and 71%, respectively. Among cases with GaC and brain metastasis, those with gastric cardia cancers (when receiving radiotherapy), those undergoing resection, and those receiving chemotherapy had lower mortality risks, while younger patients (when receiving chemotherapy or radiotherapy) and people with positive lymph nodes (when receiving radiotherapy) had higher death hazards.

Conclusion

Among patients with GaC, brain metastasis was correlated with several clinical and pathological variables, including ethnicity, age, cancer histology, location, lymph node involvement, and metastases to other sites. Cases having brain metastasis had poor survival that was correlated with age, cancer location, lymph node metastasis, and management. These findings offer vital clues for individualized patient care and future mechanistic explorations.

Imaging diagnosis of basal ganglia germ cell tumors: subtype features subtype imaging features of GCTs

OBJECTIVES

To evaluate the subtype imaging features of basal ganglia germ cell tumors (GCTs).

METHODS

Clinical and imaging data of 33 basal ganglia GCTs were retrospectively analyzed, including 17 germinomas and 16 mixed germ cell tumors (MGCTs).

RESULTS

The cyst/mass ratio of germinomas (0.53 ± 0.32) was higher than that of MGCTs (0.28 ± 0.19, p = 0.030). CT density of the solid part of germinomas (41.47 ± 5.22 Hu) was significantly higher than that of MGCTs (33.64 ± 3.75 Hu, p < 0.001), while apparent diffusion coefficients (ADC, ×^10-3 mm²/s) value of the solid part was significantly lower in geminomas (0.86 ± 0.27 ×^10-3 mm²/s) than in MGCTs (1.42 ± 0.39 ×^10-3 mm²/s, p < 0.001). MGCTs were more common with intratumoral hemorrhage (68.75% vs 11.76%, p = 0.01), T1 hyperintense foci (68.75% vs 5.88%, p < 0.001) and calcification (64.29% vs 20.00%, p = 0.025) than germinomas. There was no significant difference in internal capsule involvement between the two subtypes (p = 0.303), but Wallerian degeneration was more common in germinomas than in MGCTs (70.59% vs 25.00%, p = 0.015).

CONCLUSION

The subtypes of GCT have different imaging features. Tumoral cystic-solidity, heterogeneity, ADC value, CT density, and Wallerian degeneration are helpful to differentiate germinomas and MGCTs in basal ganglia.

ADVANCES IN KNOWLEDGE

The subtypes of GCT have different histological characteristics, leading to various imaging findings. The imaging features of GCT subtypes in basal ganglia may aid clinical diagnosis and treatment.

Emerging strategies for delivering antiangiogenic therapies to primary and metastatic brain tumors

Five-year survival rates have not increased appreciably for patients with primary and metastatic brain tumors. Nearly 17,000 patients die from primary brain tumors, whereas approximately 200,000 cases are diagnosed with brain metastasis every year in the US alone. At the same time, with improved control of systemic disease, the incidence of brain metastasis is increasing. Thus, novel approaches for improving the treatment outcome for these uniformly fatal diseases are needed urgently. In the review, we summarize the challenges in the treatment of these diseases using antiangiogenic therapies alone or in combination with radio-, chemo- and immuno-therapies. We also discuss the emerging strategies to improve the treatment outcome using both pharmacological approaches to normalize the tumor microenvironment and physical approaches (e.g., focused ultrasound) to modulate the blood-tumor-barrier, along with limitations of each approach. Finally, we offer some new avenues of future research.

Neuroimaging diagnosis of pineal region tumors-quest for pathognomonic finding of germinoma

INTRODUCTION

Our study aimed to elucidate the imaging features for the differentiation of pineal germinoma and other pineal region tumors.

METHODS

Image data sets of computed tomographic (CT) scan and magnetic resonance imaging (MRI) data of 93 pineal region tumors including 33 germinomas, 30 nongerminomatous germ cell tumors (NGGCTs), 20 pineal parenchymal tumors (PPTs), and 10 miscellaneous tumors of pineal region were reviewed. Imaging features on CT and MRI were qualitatively assessed by three readers. To know the reasons for morphological differences between germinomas and NGGCTs, histological investigation was done.

RESULTS

Localized calcification was seen in more than 70 % of germ cells tumors (GCTs: germinomas and NGGCTs) while it was scattered in more than half of PPTs. Cystic components in tumors were most frequent in NGGCTs (62 %). Multiplicity of lesion was restricted to GCTs: 39.4 % in germinoma and 10.0 % in NGGCTs. Thick peritumoral edema was more frequent in germinoma than in NGGCT: 40.6 vs. 14.8 % (p=0.0433, Fisher's test). Bithalamic extension of tumor was seen in 78.8 % of germinomas. It was significantly rare in other groups of tumors (p<0.0001, Fisher's test). The relative collagen amount per unit area was significantly lower in germinoma than in NGGCTs.

CONCLUSION

By paying attention to characteristic features as bithalamic extension, thick peritumoral edema, calcification pattern, multiplicity, and their combination, the preoperative differential diagnosis of pineal germinoma will become more accurate.