A Radiomics Model for Predicting the Response to Bevacizumab in Brain Necrosis after Radiotherapy

Cluster-based radiomics reveal spatial heterogeneity of bevacizumab response for treatment of radiotherapy-induced cerebral necrosis

Background

Bevacizumab is used in the treatment of radiation necrosis (RN), which is a debilitating toxicity following head and neck radiotherapy. However, there is no biomarker to predict if a patient would respond to bevacizumab.

Purpose

We aimed to develop a cluster-based radiomics approach to characterize the spatial heterogeneity of RN and map their responses to bevacizumab.

Methods

118 consecutive nasopharyngeal carcinoma patients diagnosed with RN were enrolled. We divided 152 lesions from the patients into 101 for training, and 51 for validation. We extracted voxel-level radiomics features from each lesion segmented on T1-weighted+contrast and T2 FLAIR sequences of pre- and post-bevacizumab magnetic resonance images, followed by a three-step analysis involving individual- and population-level clustering, before delta-radiomics to derive five radiomics clusters within the lesions. We tested the association of each cluster with response to bevacizumab and developed a clinico-radiomics model using clinical predictors and cluster-specific features.

Results

71 (70.3%) and 34 (66.7%) lesions had responded to bevacizumab in the training and validation datasets, respectively. Two radiomics clusters were spatially mapped to the edema region, and the volume changes were significantly associated with bevacizumab response (OR:11.12 [95% CI: 2.54-73.47], P = 0.004; and 1.63[1.07-2.78], P = 0.042). The combined clinico-radiomics model based on textural features extracted from the most significant cluster improved the prediction of bevacizumab response, compared with a clinical-only model (AUC:0.755 [0.645-0.865] to 0.852 [0.764-0.940], training; 0.708 [0.554-0.861] to 0.816 [0.699-0.933], validation).

Conclusion

Our radiomics approach yielded intralesional resolution, enabling a more refined feature selection for predicting bevacizumab efficacy in the treatment of RN.

Sexual-biased necroinflammation is revealed as a predictor of bevacizumab benefit in glioblastoma

BACKGROUND

Glioblastoma (GBM) is a highly malignant brain tumor that affects men more often than women. In addition, the former shows a poorer survival prognosis. To date, the reason for this sex-specific aggressiveness remains unclear. Therefore, the aim of this study is to investigate tumor processes that explain these sex differences.

METHODS

This was a retrospective study of GBM patients which was stratified according to sex. A cohort with 73 tumors was analyzed with immunohistochemistry, RNA-seq and RT-qPCR to characterize differences in vascular and immunological profiles. Transcriptomic profiling, gene set enrichment analysis, and pathway enrichment analysis were used for discovering molecular pathways predominant in each group. We further investigated the therapeutic effect of bevacizumab (vascular endothelial growth factor A (VEGFA) blocking antibody) in a retrospective GBM cohort (36 tumors) based on sex differences.

RESULTS

We found that under hypoxic tumor conditions, 2 distinct tumor immuno-angiogenic ecosystems develop linked to sex differences and ESR1 expression is generated. One of these subgroups, which includes male patients with low ESR1 expression, is characterized by vascular fragility associated with the appearance of regions of necrosis and high inflammation (called necroinflamed tumors). This male-specific tumor subtype shows high inflammation related to myeloid-derived suppressor cells infiltration. Using this stratification, we identified a possible group of patients who could respond to bevacizumab (BVZ) and revealed a genetic signature that may find clinical applications as a predictor of those who may benefit most from this treatment.

CONCLUSIONS

This study provides a stratification based on the sexual differences in GBM, which associates the poor prognosis with the presence of immunosuppressive myeloid cells in the necrotic areas. This new stratification could change the current prognosis of GBM and identifies those who respond to BVZ treatment.

Predicting response to chemotherapy in brain tumor patients based on MRI features

Chemotherapy in brain tumors is tailored based on tumor type, grade, and molecular markers, which are crucial for predicting responses and survival outcomes. This review summarizes the role of chemotherapy in gliomas, glioneuronal and neuronal tumors, ependymomas, choroid plexus tumors, medulloblastomas, and meningiomas, discussing standard treatment protocols and recent developments in targeted therapies.Furthermore, the studies reporting the integration of MRI-based radiomics and deep learning models for predicting treatment outcomes are reviewed. Advances in MRI-based radiomics and deep learning models have significantly enhanced the prediction of chemotherapeutic benefits, survival prediction following chemotherapy, and differentiating tumor progression with psuedoprogression. These non-invasive techniques offer valuable insights into tumor characteristics and treatment responses, facilitating personalized therapeutic strategies. Further research is warranted to refine these models and expand their applicability across different brain tumor types.

MRI-based radiomics feature combined with tumor markers to predict TN staging of rectal cancer

The aim of this study is to evaluate the predictive ability of MRI-based radiomics combined with tumor markers for TN staging in patients with rectal cancer and to develop a prediction model for TN staging. A total of 190 patients with rectal adenocarcinoma who underwent total mesorectal excision at the First Affiliated Hospital of the Air Force Medical University between January 2016 and December 2020 were included in the study. An additional 54 patients from a prospective validation cohort were included between August 2022 and August 2023. Preoperative tumor markers and MRI imaging data were collected from all enrolled patients. The 190 patients were divided into a training cohort (n = 133) and a validation cohort (n = 57). Radiomics features were extracted by outlining the region of interest (ROI) on T2WI sequence images. Feature selection and radiomics score (Rad-score) construction were performed using least absolute shrinkage and selection operator regression analysis (LASSO). The postoperative pathology TNM stage was used to differentiate locally advanced rectal cancer (T3/4 or N1/2) from locally early rectal cancer (T1/2, N0). Logistic regression was used to construct separate prediction models for T stage and N stage. The models' predictive performance was evaluated using DCA curves and calibration curves. The T staging model showed that Rad-score, based on 8 radiomics features, was an independent predictor of T staging. When combined with CEA, tumor diameter, mesoretal fascia (MRF), and extramural venous invasion (EMVI), it effectively differentiated between T1/2 and T3/4 stage rectal cancers in the training cohort (AUC 0.87 [95% CI: 0.81-0.93]). The N-staging model found that Rad-score, based on 10 radiomics features, was an independent predictor of N-staging. When combined with CA19.9, degree of differentiation, and EMVI, it effectively differentiated between N0 and N1/2 stage rectal cancers. The training cohort had an AUC of 0.84 (95% CI: 0.77-0.91). The calibration curves demonstrated good precision between the predicted and actual results. The DCA curves indicated that both sets of predictive models could provide net clinical benefits for diagnosis. MRI-based radiomics features are independent predictors of T staging and N staging. When combined with tumor markers, they have good predictive efficacy for TN staging of rectal cancer.

Radiomic features of the hippocampal based on magnetic resonance imaging in the menopausal mouse model linked to neuronal damage and cognitive deficits

Estrogen deficiency in the early postmenopausal phase is associated with an increased long-term risk of cognitive decline or dementia. Non-invasive characterization of the pathological features of the pathological hallmarks in the brain associated with postmenopausal women (PMW) could enhance patient management and the development of therapeutic strategies. Radiomics is a means to quantify the radiographic phenotype of a diseased tissue via the high-throughput extraction and mining of quantitative features from images acquired from modalities such as CT and magnetic resonance imaging (MRI). This study set out to explore the correlation between radiomics features based on MRI and pathological features of the hippocampus and cognitive function in the PMW mouse model. Ovariectomized (OVX) mice were used as PWM models. MRI scans were performed two months after surgery. The brain's hippocampal region was manually annotated, and the radiomic features were extracted with PyRadiomics. Chemiluminescence was used to evaluate the peripheral blood estrogen level of mice, and the Morris water maze test was used to evaluate the cognitive ability of mice. Nissl staining and immunofluorescence were used to quantify neuronal damage and COX1 expression in brain sections of mice. The OVX mice exhibited marked cognitive decline, brain neuronal damage, and increased expression of mitochondrial complex IV subunit COX1, which are pathological phenomena commonly observed in the brains of AD patients, and these phenotypes were significantly correlated with radiomics features (p < 0.05, |r|>0.5), including Original_firstorder_Interquartile Range, Original_glcm_Difference Average, Original_glcm_Difference Average and Wavelet-LHH_glszm_Small Area Emphasis. Meanwhile, the above radiomics features were significantly different between the sham-operated and OVX groups (p < 0.01) and were associated with decreased serum estrogen levels (p < 0.05, |r|>0.5). This initial study indicates that the above radiomics features may have a role in the assessment of the pathology of brain damage caused by estrogen deficiency using routinely acquired structural MR images.

Non-invasive differential diagnosis of teratomas from other intracranial germ cell tumours using MRI-based fractal and radiomic analyses

OBJECTIVES

The histologic subtype of intracranial germ cell tumours (IGCTs) is an important factor in deciding the treatment strategy, especially for teratomas. In this study, we aimed to non-invasively diagnose teratomas based on fractal and radiomic features.

MATERIALS AND METHODS

This retrospective study included 330 IGCT patients, including a discovery set (n = 296) and an independent validation set (n = 34). Fractal and radiomic features were extracted from T1-weighted, T2-weighted, and post-contrast T1-weighted images. Five classifiers, including logistic regression, random forests, support vector machines, K-nearest neighbours, and XGBoost, were compared for our task. Based on the optimal classifier, we compared the performance of clinical, fractal, and radiomic models and the model combining these features in predicting teratomas.

RESULTS

Among the diagnostic models, the fractal and radiomic models performed better than the clinical model. The final model that combined all the features showed the best performance, with an area under the curve, precision, sensitivity, and specificity of 0.946 [95% confidence interval (CI): 0.882-0.994], 95.65% (95% CI: 88.64-100%), 88.00% (95% CI: 77.78-96.36%), and 91.67% (95% CI: 78.26-100%), respectively, in the test set of the discovery set, and 0.944 (95% CI: 0.855-1.000), 85.71% (95% CI: 68.18-100%), 94.74% (95% CI: 83.33-100%), and 80.00% (95% CI: 58.33-100%), respectively, in the independent validation set. SHapley Additive exPlanations indicated that two fractal features, two radiomic features, and age were the top five features highly associated with the presence of teratomas.

CONCLUSION

The predictive model including image and clinical features could help guide treatment strategies for IGCTs.

CLINICAL RELEVANCE STATEMENT

Our machine learning model including image and clinical features can non-invasively predict teratoma components, which could help guide treatment strategies for intracranial germ cell tumours (IGCT).

KEY POINTS

• Fractals and radiomics can quantitatively evaluate imaging characteristics of intracranial germ cell tumours. • Model combing imaging and clinical features had the best predictive performance. • The diagnostic model could guide treatment strategies for intracranial germ cell tumours.

Clinical features and prognostic factors of brain metastases from colorectal cancer: a single center experience

PURPOSE

The study aimed to investigate the clinical characteristics, prognostic factors, survival times, and therapy outcomes of brain metastases (BM) from colorectal cancer (CRC).

METHODS

The clinical characteristics of 25 patients with BM from CRC were retrospectively analyzed. The time of the occurrence of BM after diagnosis of CRC was recorded. Meanwhile, the time from the occurrence of lung, bone, liver, and other extracranial metastases to the occurrence of BM was also recorded. We evaluate the time factors affecting the length of the occurrence of BM and the potential prognostic factors after BM diagnosis. The influences of patients undergoing surgery-based comprehensive treatment, radiotherapy-based comprehensive treatment, and co-medication were also assessed.

RESULTS

In patients with BM from CRC, lung metastases (13/25) occurred at a higher frequency than liver metastases (8/25) and bone metastases (6/25). The median time to the development of BM was much shorter (3.7 vs. 25.3 months, p = 0.027), with the brain being the origin site for the metastasis. The median overall survival reached 9.9 months. The interval between diagnosis of BM and bone, liver, and lung metastasis remains 3, 6.5, and 11 months, respectively. The brain lesions of patients with BM alone had higher rates in supratentorial (88.9%), while those with extracranial metastasis had a 62.5% incidence of infratentorial metastasis. The difference was statistically significant (p < 0.05). The time of occurrence of BM in patients aged 67 years and younger was 16.1 and 30.1 months, respectively. The differences between them were statistically significant (p = 0.043). The BM time for left- and right-sided colon cancer were 26.5 and 7.8 months, representing a statistically significant difference (p = 0.015). The time to onset of BM for patients with and without the resection of primary lesions was 25.4 and 4.5 months. Statistically significant differences are shown (p = 0.007). Univariate analysis demonstrated that the prognosis of patients was related to the KPS score, the number of BM, the treatment methods, and the occurrence of lung metastasis (p < 0.05). The multivariate analysis revealed that the treatment modality and lung metastasis were independent prognostic factors for CRC patients with BM. Right-sided CRC patients with BM have a poor prognosis (8.1 vs. 10.2 months, p = 0.31). Although median survival time was not significantly different between patients with and without bevacizumab combination therapy, bevacizumab therapy is associated with a better survival time (9.9 vs. 7.1 months, p = 0.27).

CONCLUSION

Patients with left-sided CRC, especially those with lung metastases, are prone to brain metastases, and patients with brain metastases as the first metastatic site have a higher rate of supratentorial metastases. Young patients with right hemicolon cancer and patients who have not undergone primary lesion resection have a shorter time for the occurrence of BM. Patients with colorectal lung metastases, especially those young with right-sided CRC, require close imaging surveillance of BM. The prognosis of CRC patients with BM and lung metastases is poor, and comprehensive treatment based on surgery could significantly prolong patients' survival time.

Amifostine-Loaded Nanocarrier Traverses the Blood-Brain Barrier and Prevents Radiation-Induced Brain Injury

Radiation-induced brain injury (RIBI) is a severe, irreversible, or even life-threatening cerebral complication of radiotherapy in patients with head and neck tumors, and there is no satisfying prevention and effective treatment available for these patients. Amifostine (AMF) is a well-known free radical scavenger with demonstrated effectiveness in preventing radiation-induced toxicity. However, the limited permeability of AMF across the blood-brain barrier (BBB) when administered intravenously reduces the effectiveness of AMF in preventing RIBI. Herein, we construct a nanoparticle (NP) platform for BBB delivery of AMF. AMF is conjugated with 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine-n-[poly(ethylene glycol)]-hydroxy succinamide [DSPE-PEG-NHS, PEG M 2000], and the product is DSPE-PEG-AMF. Then, the nanoparticles (DAPP NPs) were formed by self-assembly of poly(lactic-co-glycolic acid) (PLGA), DSPE-PEG-AMF, and polysorbate 80 (PS 80). PEG shields the nanoparticles from blood clearance by the reticuloendothelial system and lengthens the drug circulation time. PS 80 is used to encapsulate nanoparticles for medication delivery to the brain. The results of our study showed that DAPP NPs were able to effectively penetrate the blood-brain barrier (BBB) in healthy C57BL/6 mice. Furthermore, in a well-established mouse model of X-knife-induced brain injury, treatment with DAPP NPs (corresponding to 250 mg/kg AMF) was found to significantly reduce the volume of brain necrosis compared to mice treated with AMF (250 mg/kg). Importantly, the use of DAPP NPs was also shown to significantly mitigate the effects of radiation-induced neuronal damage and glial activation. This work presents a convenient brain-targeted AMF delivery system to achieve effective radioprotection for the brain, providing a promising strategy with tremendous clinical translation potential.

A phase 2 study of thalidomide for the treatment of radiation-induced blood-brain barrier injury

Radiation-induced brain injury (RIBI) is a debilitating sequela after radiotherapy to treat head and neck cancer, and 20 to 30% of patients with RIBI fail to respond to or have contraindications to the first-line treatments of bevacizumab and corticosteroids. Here, we reported a Simon's minmax two-stage, single-arm, phase 2 clinical trial (NCT03208413) to assess the efficacy of thalidomide in patients with RIBI who were unresponsive to or had contraindications to bevacizumab and corticosteroid therapies. The trial met its primary endpoint, with 27 of 58 patients enrolled showing ≥25% reduction in the volume of cerebral edema on fluid-attenuated inversion recovery-magnetic resonance imaging (FLAIR-MRI) after treatment (overall response rate, 46.6%; 95% CI, 33.3 to 60.1%). Twenty-five (43.1%) patients demonstrated a clinical improvement based on the Late Effects Normal Tissues-Subjective, Objective, Management, Analytic (LENT/SOMA) scale, and 36 (62.1%) experienced cognitive improvement based on the Montreal Cognitive Assessment (MoCA) scores. In a mouse model of RIBI, thalidomide restored the blood-brain barrier and cerebral perfusion, which were attributed to the functional rescue of pericytes secondary to elevation of platelet-derived growth factor receptor β (PDGFRβ) expression by thalidomide. Our data thus demonstrate the therapeutic potential of thalidomide for the treatment of radiation-induced cerebral vasculature impairment.

Deep learning for predicting the risk of immune checkpoint inhibitor-related pneumonitis in lung cancer

AIM

To develop and validate a nomogram model that combines computed tomography (CT)-based radiological factors extracted from deep-learning and clinical factors for the early predictions of immune checkpoint inhibitor-related pneumonitis (ICI-P).

MATERIALS AND METHODS

Forty ICI-P patients and 101 patients without ICI-P were divided randomly into the training (n=113) and test (n=28) sets. The convolution neural network (CNN) algorithm was used to extract the CT-based radiological features of predictable ICI-P and calculated the CT score of each patient. A nomogram model to predict the risk of ICI-P was developed by logistic regression.

RESULTS

CT score was calculated from five radiological features extracted by the residual neural network-50-V2 with feature pyramid networks. Four predictors of ICI-P in the nomogram model included a clinical feature (pre-existing lung diseases), two serum markers (absolute lymphocyte count and lactate dehydrogenase), and a CT score. The area under curve of the nomogram model in the training (0.910 versus 0.871 versus 0.778) and test (0.900 versus 0.856 versus 0.869) sets was better than the radiological and clinical models. The nomogram model showed good consistency and better clinical practicability.

CONCLUSION

The nomogram model that combined CT-based radiological factors and clinical factors can be used as a new non-invasive tool for the early prediction of ICI-P in lung cancer patients after immunotherapy with low cost and low manual input.

Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer

By breaking the traditional medical image analysis framework, precision medicine–radiomics has attracted much attention in the past decade. The use of various mathematical algorithms offers radiomics the ability to extract vast amounts of detailed features from medical images for quantitative analysis and analyzes the confidential information related to the tumor in the image, which can establish valuable disease diagnosis and prognosis models to support personalized clinical decisions. This article summarizes the application of radiomics and dosiomics in radiation oncology. We focus on the application of radiomics in locally advanced rectal cancer and also summarize the latest research progress of dosiomics in radiation tumors to provide ideas for the treatment of future related diseases, especially ¹²⁵I CT-guided radioactive seed implant brachytherapy.

Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging

Despite decades of advancement in the diagnosis and therapy of gliomas, the most malignant primary brain tumors, the overall survival rate is still dismal, and their post-treatment imaging appearance remains very challenging to interpret. Since the limitations of conventional magnetic resonance imaging (MRI) in the distinction between recurrence and treatment effect have been recognized, a variety of advanced MR and functional imaging techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS), as well as a variety of radiotracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) have been investigated for this indication along with voxel-based and more quantitative analytical methods in recent years. Machine learning and radiomics approaches in recent years have shown promise in distinguishing between recurrence and treatment effect as well as improving prognostication in a malignancy with a very short life expectancy. This review provides a comprehensive overview of the conventional and advanced imaging techniques with the potential to differentiate recurrence from treatment effect and includes updates in the state-of-the-art in advanced imaging with a brief overview of emerging experimental techniques. A series of representative cases are provided to illustrate the synthesis of conventional and advanced imaging with the clinical context which informs the radiologic evaluation of gliomas in the post-treatment setting.

Radiation myelopathy following stereotactic body radiation therapy for spine metastases

PURPOSE

Stereotactic body radiation therapy (SBRT) is now considered a standard of care treatment option in the management of spine metastases. One of the most feared complications of spine SBRT is radiation myelopathy (RM).

METHODS

We provided a narrative review of RM following spine SBRT based on review of the published literature, including data on spinal cord dose constraints associated with the risk of RM, strategies to mitigate the risk, and management options for RM.

RESULTS

There are limited published data of cases of RM following spine SBRT with detailed spinal cord dosimetry. The HyTEC report provided recommendations for the point maximal dose (Dmax) for the spinal cord that is associated with a < 5% risk of RM for 1-5 fractions spine SBRT. In the setting of spine SBRT reirradiation after previous conventional external beam radiation therapy (cEBRT), factors associated with RM are: SBRT spinal cord Dmax, cumulative spinal cord Dmax, and the time interval between previous RT and SBRT reirradiation. There are various strategies to mitigate the risk of RM, including accurate delineation of the spinal cord (or thecal sac), strict adherence to the recommended spinal cord dose constraints, and robust treatment immobilisation set-up and delivery. Limited effective treatment options are available for patients who develop RM, and these include corticosteroids, hyperbaric oxygen, and bevacizumab; however, none have been supported by high quality evidence.

CONCLUSION

RM is a rare but devastating complication following SBRT for spine metastases. There are strategies to minimise the risk of RM to ensure safe delivery of spine SBRT.

Building reliable radiomic models using image perturbation

Radiomic model reliability is a central premise for its clinical translation. Presently, it is assessed using test–retest or external data, which, unfortunately, is often scarce in reality. Therefore, we aimed to develop a novel image perturbation-based method (IPBM) for the first of its kind toward building a reliable radiomic model. We first developed a radiomic prognostic model for head-and-neck cancer patients on a training (70%) and evaluated on a testing (30%) cohort using C-index. Subsequently, we applied the IPBM to CT images of both cohorts (Perturbed-Train and Perturbed-Test cohort) to generate 60 additional samples for both cohorts. Model reliability was assessed using intra-class correlation coefficient (ICC) to quantify consistency of the C-index among the 60 samples in the Perturbed-Train and Perturbed-Test cohorts. Besides, we re-trained the radiomic model using reliable RFs exclusively (ICC > 0.75) to validate the IPBM. Results showed moderate model reliability in Perturbed-Train (ICC: 0.565, 95%CI 0.518–0.615) and Perturbed-Test (ICC: 0.596, 95%CI 0.527–0.670) cohorts. An enhanced reliability of the re-trained model was observed in Perturbed-Train (ICC: 0.782, 95%CI 0.759–0.815) and Perturbed-Test (ICC: 0.825, 95%CI 0.782–0.867) cohorts, indicating validity of the IPBM. To conclude, we demonstrated capability of the IPBM toward building reliable radiomic models, providing community with a novel model reliability assessment strategy prior to prospective evaluation.

Ultrasound-based radiomics nomogram for differentiation of triple-negative breast cancer from fibroadenoma

OBJECTIVE

This study aimed to develop a radiomics nomogram that incorporates radiomics, conventional ultrasound (US) and clinical features in order to differentiate triple-negative breast cancer (TNBC) from fibroadenoma.

METHODS

A total of 182 pathology-proven fibroadenomas and 178 pathology-proven TNBCs, which underwent preoperative US examination, were involved and randomly divided into training (n = 253) and validation cohorts (n = 107). The radiomics features were extracted from the regions of interest of all lesions, which were delineated on the basis of preoperative US examination. The least absolute shrinkage and selection operator model and the maximum relevance minimum redundancy algorithm were established for the selection of tumor status-related features and construction of radiomics signature (Rad-score). Then, multivariate logistic regression analyses were utilized to develop a radiomics model by incorporating the radiomics signature and clinical findings. Finally, the usefulness of the combined nomogram was assessed by using the receiver operator characteristic curve, calibration curve, and decision curve analysis (DCA).

RESULTS

The radiomics signature, composed of 12 selected features, achieved good diagnostic performance. The nomogram incorporated with radiomics signature and clinical data showed favorable diagnostic efficacy in the training cohort (AUC 0.986, 95% CI, 0.975-0.997) and validation cohort (AUC 0.977, 95% CI, 0.953-1.000). The radiomics nomogram outperformed the Rad-score and clinical models (p < 0.05). The calibration curve and DCA demonstrated the good clinical utility of the combined radiomics nomogram.

CONCLUSION

The radiomics signature is a potential predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models.

ADVANCES IN KNOWLEDGE

Recent advances in radiomics-based US are increasingly showing potential for improved diagnosis, assessment of therapeutic response and disease prediction in oncology. Rad-score is an independent predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models.

Efficacy and safety of apatinib in radiation-induced brain injury among head and neck cancer: an open-label, single-arm, phase 2 study

BACKGROUND

The treatment of radiation-induced brain injury (RI) caused by radiotherapy for head and neck cancer is challenging. Antiangiogenic therapy is a promising treatment. Apatinib is an oral tyrosine kinase inhibitor (TKI) that selectively inhibits vascular endothelial growth factor receptor (VEGFR) 2. We aimed to assess the efficacy and safety of apatinib in patients with RI.

PATIENTS AND METHODS

In this phase 2, open-label, single-arm, prospective study, we recruited patients aged 35-80 years with prior radiotherapy history for head and neck cancer who had newly diagnosed RI at the Sun Yat-sen Memorial Hospital, China. Apatinib was administered at a dosage of 250 mg once daily orally for 4 weeks. A Simon's mini-max two-stage design was performed. The primary outcome was the proportion of patients with an overall clinical efficacy defined as radiographic response ≥ 25% reduction in baseline brain edema volume on magnetic resonance (MR) fluid attenuated inversion recovery (FLAIR) images at week 4. Secondary endpoints were overall improvement rate of brain necrosis, neurological function, and safety.

RESULTS

We screened 37 patients, 36 of whom were enrolled between October 17, 2019 and August 3, 2020. At the cutoff date, 36 patients were assessed for efficacy and safety (19 to stage 1 and 17 to stage 2). Of the 36 patients evaluated for overall clinical efficacy, 22 patients (61.1%; 95%CI 43.5-76.9%) achieved the primary endpoint at week 4. Among the 31 patients with brain necrosis lesions, 19 patients (61.3%; 95%CI 42.2%-78.2%) showed improvement of brain necrosis. The most common grade 1 to 2 adverse events were hand-foot syndrome, fatigue and hypertension There were no treatment-related grade 4-5 toxicities.

CONCLUSION

Oral apatinib shows promising efficacy and is well-tolerated in patients with RI. Further randomized controlled studies are warranted.

TRIAL REGISTRATION

ClinicalTrials.gov (ID: NCT04152681).

Radiomics for precision medicine in glioblastoma

INTRODUCTION

Being the most common primary brain tumor, glioblastoma presents as an extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying molecular epidemiology of glioblastoma between patients and intra-tumoral heterogeneity explains the failure of current one-size-fits-all treatment modalities. Radiomics uses machine learning to identify salient features of the tumor on brain imaging and promises patient-specific management in glioblastoma patients.

METHODS

We performed a comprehensive review of the available literature on studies investigating the role of radiomics and radiogenomics models for the diagnosis, stratification, prognostication as well as treatment planning and monitoring of glioblastoma.

RESULTS

Classifiers based on a combination of various MRI sequences, genetic information and clinical data can predict non-invasive tumor diagnosis, overall survival and treatment response with reasonable accuracy. However, the use of radiomics for glioblastoma treatment remains in infancy as larger sample sizes, standardized image acquisition and data extraction techniques are needed to develop machine learning models that can be translated effectively into clinical practice.

CONCLUSION

Radiomics has the potential to transform the scope of glioblastoma management through personalized medicine.

How Machine Learning is Powering Neuroimaging to Improve Brain Health

This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.

Multiparametric radiomic tissue signature and machine learning for distinguishing radiation necrosis from tumor progression after stereotactic radiosurgery

Background

Stereotactic radiosurgery (SRS) may cause radiation necrosis (RN) that is difficult to distinguish from tumor progression (TP) by conventional MRI. We hypothesize that MRI-based multiparametric radiomics (mpRad) and machine learning (ML) can differentiate TP from RN in a multi-institutional cohort.

Methods

Patients with growing brain metastases after SRS at 2 institutions underwent surgery, and RN or TP were confirmed by histopathology. A radiomic tissue signature (RTS) was selected from mpRad, as well as single T₁ post-contrast (T₁c) and T₂ fluid-attenuated inversion recovery (T₂-FLAIR) radiomic features. Feature selection and supervised ML were performed in a randomly selected training cohort (N = 95) and validated in the remaining cases (N = 40) using surgical pathology as the gold standard.

Results

One hundred and thirty-five discrete lesions (37 RN, 98 TP) from 109 patients were included. Radiographic diagnoses by an experienced neuroradiologist were concordant with histopathology in 67% of cases (sensitivity 69%, specificity 59% for TP). Radiomic analysis indicated institutional origin as a significant confounding factor for diagnosis. A random forest model incorporating 1 mpRad, 4 T₁c, and 4 T₂-FLAIR features had an AUC of 0.77 (95% confidence interval [CI]: 0.66–0.88), sensitivity of 67% and specificity of 86% in the training cohort, and AUC of 0.71 (95% CI: 0.51–0.91), sensitivity of 52% and specificity of 90% in the validation cohort.

Conclusions

MRI-based mpRad and ML can distinguish TP from RN with high specificity, which may facilitate the triage of patients with growing brain metastases after SRS for repeat radiation versus surgical intervention.

Current Advances and Challenges in Radiomics of Brain Tumors

Imaging diagnosis is crucial for early detection and monitoring of brain tumors. Radiomics enable the extraction of a large mass of quantitative features from complex clinical imaging arrays, and then transform them into high-dimensional data which can subsequently be mined to find their relevance with the tumor's histological features, which reflect underlying genetic mutations and malignancy, along with grade, progression, therapeutic effect, or even overall survival (OS). Compared to traditional brain imaging, radiomics provides quantitative information linked to meaningful biologic characteristics and application of deep learning which sheds light on the full automation of imaging diagnosis. Recent studies have shown that radiomics' application is broad in identifying primary tumor, differential diagnosis, grading, evaluation of mutation status and aggression, prediction of treatment response and recurrence in pituitary tumors, gliomas, and brain metastases. In this descriptive review, besides establishing a general understanding among protocols, results, and clinical significance of these studies, we further discuss the current limitations along with future development of radiomics.

Bevacizumab Combined with Corticosteroids Does Not Improve the Clinical Outcome of Nasopharyngeal Carcinoma Patients With Radiation-Induced Brain Necrosis

Objective

Our aim was to compare the clinical outcomes of patients treated with bevacizumab combined with corticosteroids and those with bevacizumab monotherapy from a radiation-induced brain necrosis (RN) registry cohort (NCT03908502).

Methods

We utilized clinical data from a prospective RN registry cohort (NCT03908502) from July 2017 to June 2020. Patients were considered eligible if they had symptomatic RN after radiotherapy for nasopharyngeal carcinoma (NPC) and received bevacizumab (5 mg/kg, two to four cycles) with a minimum follow-up time of 3 months. The primary outcome was a 2-month response rate determined by MRI and clinical symptoms. Secondary outcomes included quality of life [evaluated by the abbreviated World Health Organization Quality of Life (WHOQOL-BREF) questionnaire] and cognitive function (evaluated by the Montreal Cognitive Assessment scale) at 2 months, RN recurrence during follow-up, and adverse events.

Results

A total of 123 patients (34 in the combined therapy group and 89 in the monotherapy group) were enrolled in our study with a median follow-up time of 0.97 year [interquartile range (IQR) = 0.35-2.60 years]. The clinical efficacy of RN did not differ significantly between patients in these two groups [odds ratio (OR) = 1.642, 95%CI = 0.584-4.614, p = 0.347]. Furthermore, bevacizumab combined with corticosteroids did not reduce recurrence compared with bevacizumab monotherapy [hazard ratio (HR) = 1.329, 95%CI = 0.849-2.079, p = 0.213]. The most common adverse events of bevacizumab were hypertension (17.89%), followed by nosebleed (8.13%) and fatigue (8.13%). There was no difference in grade 2 or more severe adverse events between the two groups (p = 0.811).

Interpretation

Our results showed that the treatment strategy of combining bevacizumab with corticosteroids did not lead to better clinical outcomes for RN patients with a background of radiotherapy for nasopharyngeal carcinoma.

Analysis of EPID Transmission Fluence Maps Using Machine Learning Models and CNN for Identifying Position Errors in the Treatment of GO Patients

Purpose

To find a suitable method for analyzing electronic portal imaging device (EPID) transmission fluence maps for the identification of position errors in the in vivo dose monitoring of patients with Graves' ophthalmopathy (GO).

Methods

Position errors combining 0-, 2-, and 4-mm errors in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions in the delivery of 40 GO patient radiotherapy plans to a human head phantom were simulated and EPID transmission fluence maps were acquired. Dose difference (DD) and structural similarity (SSIM) maps were calculated to quantify changes in the fluence maps. Three types of machine learning (ML) models that utilize radiomics features of the DD maps (ML 1 models), features of the SSIM maps (ML 2 models), and features of both DD and SSIM maps (ML 3 models) as inputs were used to perform three types of position error classification, namely a binary classification of the isocenter error (type 1), three binary classifications of LR, SI, and AP direction errors (type 2), and an eight-element classification of the combined LR, SI, and AP direction errors (type 3). Convolutional neural network (CNN) was also used to classify position errors using the DD and SSIM maps as input.

Results

The best-performing ML 1 model was XGBoost, which achieved accuracies of 0.889, 0.755, 0.778, 0.833, and 0.532 in the type 1, type 2-LR, type 2-AP, type 2-SI, and type 3 classification, respectively. The best ML 2 model was XGBoost, which achieved accuracies of 0.856, 0.731, 0.736, 0.949, and 0.491, respectively. The best ML 3 model was linear discriminant classifier (LDC), which achieved accuracies of 0.903, 0.792, 0.870, 0.931, and 0.671, respectively. The CNN achieved classification accuracies of 0.925, 0.833, 0.875, 0.949, and 0.689, respectively.

Conclusion

ML models and CNN using combined DD and SSIM maps can analyze EPID transmission fluence maps to identify position errors in the treatment of GO patients. Further studies with large sample sizes are needed to improve the accuracy of CNN.

Machine Learning-Based Radiomics of the Optic Chiasm Predict Visual Outcome Following Pituitary Adenoma Surgery

Preoperative prediction of visual recovery after pituitary adenoma surgery remains a challenge. We aimed to investigate the value of MRI-based radiomics of the optic chiasm in predicting postoperative visual field outcome using machine learning technology. A total of 131 pituitary adenoma patients were retrospectively enrolled and divided into the recovery group (N = 79) and the non-recovery group (N = 52) according to visual field outcome following surgical chiasmal decompression. Radiomic features were extracted from the optic chiasm on preoperative coronal T2-weighted imaging. Least absolute shrinkage and selection operator regression were first used to select optimal features. Then, three machine learning algorithms were employed to develop radiomic models to predict visual recovery, including support vector machine (SVM), random forest and linear discriminant analysis. The prognostic performances of models were evaluated via five-fold cross-validation. The results showed that radiomic models using different machine learning algorithms all achieved area under the curve (AUC) over 0.750. The SVM-based model represented the best predictive performance for visual field recovery, with the highest AUC of 0.824. In conclusion, machine learning-based radiomics of the optic chiasm on routine MR imaging could potentially serve as a novel approach to preoperatively predict visual recovery and allow personalized counseling for individual pituitary adenoma patients.

A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging

Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumours of the head and neck, and improving the efficiency of its diagnosis and treatment strategies is an important goal. With the development of the combination of artificial intelligence (AI) technology and medical imaging in recent years, an increasing number of studies have been conducted on image analysis of NPC using AI tools, especially radiomics and artificial neural network methods. In this review, we present a comprehensive overview of NPC imaging research based on radiomics and deep learning. These studies depict a promising prospect for the diagnosis and treatment of NPC. The deficiencies of the current studies and the potential of radiomics and deep learning for NPC imaging are discussed. We conclude that future research should establish a large-scale labelled dataset of NPC images and that studies focused on screening for NPC using AI are necessary.

Reverse Engineering Glioma Radiomics to Conventional Neuroimaging

A novel radiological research field pursuing comprehensive quantitative image, namely “Radiomics,” gained traction along with the advancement of computational technology and artificial intelligence. This novel concept for analyzing medical images brought extensive interest to the neuro-oncology and neuroradiology research community to build a diagnostic workflow to detect clinically relevant genetic alteration of gliomas noninvasively. Although quite a few promising results were published regarding MRI-based diagnosis of isocitrate dehydrogenase (IDH) mutation in gliomas, it has become clear that an ample amount of effort is still needed to render this technology clinically applicable. At the same time, many significant insights were discovered through this research project, some of which could be “reverse engineered” to improve conventional non-radiomic MR image acquisition. In this review article, the authors aim to discuss the recent advancements and encountering issues of radiomics, how we can apply the knowledge provided by radiomics to standard clinical images, and further expected technological advances in the realm of radiomics and glioma.

Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab

Anti-angiogenic therapy with bevacizumab is a widely used therapeutic option for recurrent glioblastoma (GBM). Nevertheless, the therapeutic response remains highly heterogeneous among GBM patients with discordant outcomes. Recent data have shown that radiomics, an advanced recent imaging analysis method, can help to predict both prognosis and therapy in a multitude of solid tumours. The objective of this study was to identify novel biomarkers, extracted from MRI and clinical data, which could predict overall survival (OS) and progression-free survival (PFS) in GBM patients treated with bevacizumab using machine-learning algorithms. In a cohort of 194 recurrent GBM patients (age range 18-80), radiomics data from pre-treatment T2 FLAIR and gadolinium-injected MRI images along with clinical features were analysed. Binary classification models for OS at 9, 12, and 15 months were evaluated. Our classification models successfully stratified the OS. The AUCs were equal to 0.78, 0.85, and 0.76 on the test sets (0.79, 0.82, and 0.87 on the training sets) for the 9-, 12-, and 15-month endpoints, respectively. Regressions yielded a C-index of 0.64 (0.74) for OS and 0.57 (0.69) for PFS. These results suggest that radiomics could assist in the elaboration of a predictive model for treatment selection in recurrent GBM patients.

Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods-A Critical Review of Literature

Esophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide novel quantitative imaging markers in medical imaging. Radiomics approaches that could quantify medical images into high-dimensional data have been shown to improve the imaging-based classification system in characterizing the heterogeneity of primary tumors and lymph nodes in EC patients. In this review, we aim to provide a comprehensive summary of the evidence of the most recent developments in ML application in imaging pertinent to EC patient care. According to the published results, ML models evaluating treatment response and lymph node metastasis achieve reliable predictions, ranging from acceptable to outstanding in their validation groups. Patients stratified by ML models in different risk groups have a significant or borderline significant difference in survival outcomes. Prospective large multi-center studies are suggested to improve the generalizability of ML techniques with standardized imaging protocols and harmonization between different centers.