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

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1263
Author(s):  
Samy Ammari ◽  
Raoul Sallé de Chou ◽  
Tarek Assi ◽  
Mehdi Touat ◽  
Emilie Chouzenoux ◽  
...  
Keyword(s):  
Machine Learning ◽  
Therapeutic Option ◽  
Binary Classification ◽  
Progression Free Survival ◽  
Recurrent Glioblastoma ◽  
Machine Learning Algorithms ◽  
Survival Prediction ◽  
Classification Models ◽  
Angiogenic Therapy ◽  
Recurrent Gbm

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.

scholarly journals CTIM-03. PHASE II STUDY OF REGORAFENIB IN BEVACIZUMAB REFRACTORY RECURRENT GLIOBLASTOMA

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii33-ii33
Author(s):  
Yasmeen Rauf ◽  
Cathy Schilero ◽  
David Peereboom ◽  
Manmeet Ahluwalia
Keyword(s):  
Dose Escalation ◽  
Therapeutic Option ◽  
Objective Response ◽  
Progression Free Survival ◽  
Recurrent Glioblastoma ◽  
Weekly Dose ◽  
Cellular Functions ◽  
Molecule Inhibitor ◽  
Secondary Endpoints ◽  
Magnetic Resonance Imaging Mri

Abstract BACKGROUND Most patients with glioblastoma (GBM) receive bevacizumab as part of their treatment. There is no good therapeutic option after bevacizumab failure. Regorafenib has potent preclinical antitumor activity and long-lasting anti-angiogenic activity as measured by dynamic contrast enhanced (DCE) – magnetic resonance imaging (MRI). Regorafenib is a small molecule inhibitor of multiple membrane-bound and intracellular kinases involved in normal cellular functions and in pathologic processes such as oncogenesis, tumor angiogenesis, and maintenance of the tumor microenvironment. METHODS Patients with progression of GBM after treatment with Bevacizumab will be eligible for the study. Oral administration of Regorafenib at 160 mg once daily will be administered for 3 weeks on /1 week off. Weekly dose escalation of regorafenib from 80 mg to 160 mg/day will be employed as per the Redos strategy. Patients start the treatment 80 mg/day in week 1, with weekly dose escalation to 120 mg in week 2, then 160 mg week in 3 if no significant drug-related toxicities are observed. They will be continued on treatment with Regorafenib 160 md /day till tumor progression or toxicity. They will get MRI brain every 4 weeks during the study. RESULTS Primary endpoint is median Overall survival. Secondary endpoints include progression free survival at 6 months and the median time to progression and objective response rate using the modified RANO criteria. The overall safety and tolerability of regorafenib by CTCAE version 5.0. will also be reported. CONCLUSION This is an ongoing clinical trial.

scholarly journals SURG-13. LASER INTERSTITIAL THERMAL THERAPY FOR RECURRENT GLIOBLASTOMA: A REVIEW OF SURVIVAL OUTCOMES COMPARED TO A MATCHED SURGICAL COHORT

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii206-ii206
Author(s):  
Hassan Fadel ◽  
Sameah Haider ◽  
Jacob Pawloski ◽  
Hesham Zakaria ◽  
Farhan Chaudhry ◽  
...  
Keyword(s):  
Surgical Resection ◽  
Subgroup Analysis ◽  
Tumor Volume ◽  
Progression Free Survival ◽  
Recurrent Glioblastoma ◽  
Thermal Therapy ◽  
Survival Outcomes ◽  
Repeat Surgery ◽  
Laser Interstitial Thermal Therapy ◽  
Recurrent Gbm

Abstract INTRODUCTION Glioblastoma (GBM) is uniformly associated with a poor prognosis and inevitable recurrence. Management of recurrent GBM remains unclear, with repeat surgery often employed with varying degrees of success. We evaluated the efficacy of Laser Interstitial Thermal Therapy (LITT) for recurrent GBM when compared to a carefully matched cohort of patients treated with repeat surgical resection. METHODS A retrospective single-institution database was used to identify patients who underwent LITT or surgical resection of recurrent GBM between 2014-2019. LITT patients were matched with surgical resection patients according to baseline demographics, comorbidities, tumor location, and eloquence. Subgroup analysis matching similar patients for tumor volume was also completed. Overall survival (OS) and progression-free survival (PFS) were the primary endpoints. RESULTS A LITT cohort of 20 patients was matched to 50 similar patients who underwent repeat surgical resection. Baseline characteristics were similar between both cohorts apart from tumor volume, which was larger in the surgical cohort (17.5 cc vs. 4.7 cc, p< 0.01). On long-term follow-up, there was no difference in OS (HR, 0.72; 95%CI, 0.36-1.45) or PFS (HR, 0.67; 95%CI, 0.29-1.53) between the LITT and surgical cohorts when controlling for tumor volume. Subgroup analysis of 23 LITT patients matched according to tumor volume with 23 surgical patients with similar clinical characteristics also found no difference in OS (HR, 0.66; 95%CI, 0.33-1.30) or PFS (HR, 0.58; 95%CI, 0.90-1.05) between the cohorts. LITT patients had shorter length of stays (1 vs. 4 days, p< 0.001) and a higher rate of home discharge (84% vs. 67%, p=0.172) compared to the surgical cohort. CONCLUSION After matching for demographic, clinical, and tumor characteristics, there was no difference in outcomes between patients undergoing LITT compared to surgical resection for recurrent GBM. LITT patients had similar survival outcomes yet shorter hospital stays and more favorable dispositions, potentially mitigating post-treatment complications.

scholarly journals Multivariate Analysis for the Classification of Chocolate According to its Percentage of Cocoa by Using Terahertz Time-Domain Spectroscopy (THz-TDS)

Proceedings ◽  
2020 ◽  
Vol 70 (1) ◽  
pp. 109
Author(s):  
Jimy Oblitas ◽  
Jorge Ruiz
Keyword(s):  
Machine Learning ◽  
Time Domain ◽  
Electromagnetic Pulse ◽  
Machine Learning Algorithms ◽  
Classification Models ◽  
Terahertz Time Domain Spectroscopy ◽  
Time Domain Spectroscopy ◽  
Svm Algorithm ◽  
Classification Of Images

Terahertz time-domain spectroscopy is a useful technique for determining some physical characteristics of materials, and is based on selective frequency absorption of a broad-spectrum electromagnetic pulse. In order to investigate the potential of this technology to classify cocoa percentages in chocolates, the terahertz spectra (0.5–10 THz) of five chocolate samples (50%, 60%, 70%, 80% and 90% of cocoa) were examined. The acquired data matrices were analyzed with the MATLAB 2019b application, from which the dielectric function was obtained along with the absorbance curves, and were classified by using 24 mathematical classification models, achieving differentiations of around 93% obtained by the Gaussian SVM algorithm model with a kernel scale of 0.35 and a one-against-one multiclass method. It was concluded that the combined processing and classification of images obtained from the terahertz time-domain spectroscopy and the use of machine learning algorithms can be used to successfully classify chocolates with different percentages of cocoa.

scholarly journals A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 804
Author(s):  
Jasminka Hasic Telalovic ◽  
Serena Pillozzi ◽  
Rachele Fabbri ◽  
Alice Laffi ◽  
Daniele Lavacchi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Agent ◽  
Predictive Biomarkers ◽  
Progression Free Survival ◽  
Primary Site ◽  
Somatostatin Analog ◽  
Support Vector ◽  
Classification Models ◽  
First Line Therapy ◽  
Bayes Algorithm

The application of machine learning (ML) techniques could facilitate the identification of predictive biomarkers of somatostatin analog (SSA) efficacy in patients with neuroendocrine tumors (NETs). We collected data from 74 patients with a pancreatic or gastrointestinal NET who received SSA as first-line therapy. We developed three classification models to predict whether the patient would experience a progressive disease (PD) after 12 or 18 months based on clinic-pathological factors at the baseline. The dataset included 70 samples and 15 features. We initially developed three classification models with accuracy ranging from 55% to 70%. We then compared ten different ML algorithms. In all but one case, the performance of the Multinomial Naïve Bayes algorithm (80%) was the highest. The support vector machine classifier (SVC) had a higher performance for the recall metric of the progression-free outcome (97% vs. 94%). Overall, for the first time, we documented that the factors that mainly influenced progression-free survival (PFS) included age, the number of metastatic sites and the primary site. In addition, the following factors were also isolated as important: adverse events G3–G4, sex, Ki67, metastatic site (liver), functioning NET, the primary site and the stage. In patients with advanced NETs, ML provides a predictive model that could potentially be used to differentiate prognostic groups and to identify patients for whom SSA therapy as a single agent may not be sufficient to achieve a long-lasting PFS.

A Literature Review on Thyroid Hormonal Problems in Women Using Data Science and Analytics

2021 ◽  
pp. 416-428
Author(s):  
R. Suganya ◽  
Rajaram S. ◽  
Kameswari M.
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Data Science ◽  
Learning Algorithms ◽  
Research Literature ◽  
Machine Learning Algorithms ◽  
Thyroid Disorder ◽  
Classification Models ◽  
Indian Women ◽  
Using Data

Currently, thyroid disorders are more common and widespread among women worldwide. In India, seven out of ten women are suffering from thyroid problems. Various research literature studies predict that about 35% of Indian women are examined with prevalent goiter. It is very necessary to take preventive measures at its early stages, otherwise it causes infertility problem among women. The recent review discusses various analytics models that are used to handle different types of thyroid problems in women. This chapter is planned to analyze and compare different classification models, both machine learning algorithms and deep leaning algorithms, to classify different thyroid problems. Literature from both machine learning and deep learning algorithms is considered. This literature review on thyroid problems will help to analyze the reason and characteristics of thyroid disorder. The dataset used to build and to validate the algorithms was provided by UCI machine learning repository.

scholarly journals A Bayesian adaptive randomized phase II multicenter trial of bevacizumab with or without vorinostat in adults with recurrent glioblastoma

2020 ◽  
Vol 22 (10) ◽  
pp. 1505-1515 ◽  
Author(s):  
Vinay K Puduvalli ◽  
Jing Wu ◽  
Ying Yuan ◽  
Terri S Armstrong ◽  
Elizabeth Vera ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Phase Ii ◽  
Clinical Benefit ◽  
Adaptive Design ◽  
Acquired Resistance ◽  
Wound Dehiscence ◽  
Progression Free Survival ◽  
Recurrent Glioblastoma ◽  
Bayesian Adaptive Design ◽  
Recurrent Gbm

Abstract Background Bevacizumab has promising activity against recurrent glioblastoma (GBM). However, acquired resistance to this agent results in tumor recurrence. We hypothesized that vorinostat, a histone deacetylase (HDAC) inhibitor with anti-angiogenic effects, would prevent acquired resistance to bevacizumab. Methods This multicenter phase II trial used a Bayesian adaptive design to randomize patients with recurrent GBM to bevacizumab alone or bevacizumab plus vorinostat with the primary endpoint of progression-free survival (PFS) and secondary endpoints of overall survival (OS) and clinical outcomes assessment (MD Anderson Symptom Inventory Brain Tumor module [MDASI-BT]). Eligible patients were adults (≥18 y) with histologically confirmed GBM recurrent after prior radiation therapy, with adequate organ function, KPS ≥60, and no prior bevacizumab or HDAC inhibitors. Results Ninety patients (bevacizumab + vorinostat: 49, bevacizumab: 41) were enrolled, of whom 74 were evaluable for PFS (bevacizumab + vorinostat: 44, bevacizumab: 30). Median PFS (3.7 vs 3.9 mo, P = 0.94, hazard ratio [HR] 0.63 [95% CI: 0.38, 1.06, P = 0.08]), median OS (7.8 vs 9.3 mo, P = 0.64, HR 0.93 [95% CI: 0.5, 1.6, P = 0.79]) and clinical benefit were similar between the 2 arms. Toxicity (grade ≥3) in 85 evaluable patients included hypertension (n = 37), neurological changes (n = 2), anorexia (n = 2), infections (n = 9), wound dehiscence (n = 2), deep vein thrombosis/pulmonary embolism (n = 2), and colonic perforation (n = 1). Conclusions Bevacizumab combined with vorinostat did not yield improvement in PFS or OS or clinical benefit compared with bevacizumab alone or a clinical benefit in adults with recurrent GBM. This trial is the first to test a Bayesian adaptive design with adaptive randomization and Bayesian continuous monitoring in patients with primary brain tumor and demonstrates the feasibility of using complex Bayesian adaptive design in a multicenter setting.

scholarly journals Days gained response discriminates treatment response in patients with recurrent glioblastoma receiving bevacizumab-based therapies

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Kyle W Singleton ◽  
Alyx B Porter ◽  
Leland S Hu ◽  
Sandra K Johnston ◽  
Kamila M Bond ◽  
...  
Keyword(s):  
Treatment Response ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Progression Free Survival ◽  
Cox Proportional Hazards ◽  
Recurrent Glioblastoma ◽  
Response To Therapy ◽  
Cytotoxic Therapy ◽  
Kaplan Meier ◽  
Recurrent Gbm

Abstract Background Accurate assessments of patient response to therapy are a critical component of personalized medicine. In glioblastoma (GBM), the most aggressive form of brain cancer, tumor growth dynamics are heterogenous across patients, complicating assessment of treatment response. This study aimed to analyze days gained (DG), a burgeoning model-based dynamic metric, for response assessment in patients with recurrent GBM who received bevacizumab-based therapies. Methods DG response scores were calculated using volumetric tumor segmentations for patients receiving bevacizumab with and without concurrent cytotoxic therapy (N = 62). Kaplan–Meier and Cox proportional hazards analyses were implemented to examine DG prognostic relationship to overall (OS) and progression-free survival (PFS) from the onset of treatment for recurrent GBM. Results In patients receiving concurrent bevacizumab and cytotoxic therapy, Kaplan–Meier analysis showed significant differences in OS and PFS at DG cutoffs consistent with previously identified values from newly diagnosed GBM using T1-weighted gadolinium-enhanced magnetic resonance imaging (T1Gd). DG scores for bevacizumab monotherapy patients only approached significance for PFS. Cox regression showed that increases of 25 DG on T1Gd imaging were significantly associated with a 12.5% reduction in OS hazard for concurrent therapy patients and a 4.4% reduction in PFS hazard for bevacizumab monotherapy patients. Conclusion DG has significant meaning in recurrent therapy as a metric of treatment response, even in the context of anti-angiogenic therapies. This provides further evidence supporting the use of DG as an adjunct response metric that quantitatively connects treatment response and clinical outcomes.

Effect of antiangiogenic therapy on tumor-associated macrophages in recurrent glioblastoma.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 2010-2010 ◽  
Author(s):  
Christine Lu-Emerson ◽  
Matija Snuderl ◽  
Christian Davidson ◽  
Nathaniel D. Kirkpatrick ◽  
Yuhui Huang ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Massachusetts General Hospital ◽  
Antiangiogenic Therapy ◽  
Clinical Information ◽  
Recurrent Glioblastoma ◽  
Tumor Associated Macrophages ◽  
Angiogenic Therapy ◽  
History Of ◽  
Autopsy Specimens ◽  
Recurrent Gbm

2010 Background: Antiangiogenic therapy is associated with increased radiographic responses in glioblastoma (GBM), but tumors invariably recur. Tumor associated macrophages (TAMs) have been proposed as a mechanism of resistance to anti-angiogenic therapy in preclinical models. To examine the role of TAMs in recurrent GBM, we analyzed autopsy specimens from patients with or without history of antiangiogenic therapy. Methods: We compared autopsy brain specimens from 17 recurrent GBM patients who received anti-angiogenic treatment and chemoradiation (AAT+) to 7 patients who received chemotherapy and/or radiotherapy without anti-angiogenic therapy, or no treatment (AAT-). TAMs were morphologically and phenotypically identified with flow cytometry and immunohistochemistry (IHC) with CD68, CD11b, CD14, and CD163 markers. All specimens were obtained from the Department of Pathology at Massachusetts General Hospital and clinical information gained through review of the patients’ records. Results: Using flow cytometry, we observed an increase in CD11b+CD14+ cells in the AAT+ patients compared to AAT- patients. Using IHC analysis, we observed a significant increase in CD68+ macrophages in the tumor bulk (p<0.01) and infiltrative areas (p<0.05) in AAT+ versus AAT- patients. We also observed a significant increase in CD11b+ myeloid cells in the tumor bulk (p<0.01) and a significant increase in CD163+ cells in the infiltrative areas (p<0.05) in the AAT+ group. Finally, we noted a trend toward an increase in CD163+ cells in the tumor bulk (p=0.087) in the AAT+ versus the AAT- patients. Conclusions: Patients with recurrent GBM after antiangiogenic therapy showed a significant increase in CD68+ TAMs and in CD11b+ cells in the tumor bulk. Additionally, antiangiogenic treatment induced an increase in CD68+ and CD163+ TAMs in the infiltrative region. These data indicate that TAMs may participate in escape from antiangiogenic therapy and may represent a future therapeutic target in recurrent GBM.

Nivolumab for patients with recurrent glioblastoma progressing on bevacizumab: A retrospective case series.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e13538-e13538
Author(s):  
Marc C. Chamberlain ◽  
Bryan T. Kim
Keyword(s):  
Unmet Need ◽  
Case Series ◽  
Progression Free Survival ◽  
Recurrent Glioblastoma ◽  
Retrospective Case ◽  
Free Survival ◽  
Entire Cohort ◽  
First Recurrence ◽  
Grade 3 ◽  
Recurrent Gbm

e13538 Objective: A single institution retrospective evaluation of nivolumab following disease progression on bevacizumab in adults with recurrent glioblastoma (GBM) with an objective of determining progression free survival (PFS). Background: There is no accepted therapy for recurrent GBM after failure of bevacizumab. Methods: 16 adults, ages 52-72 years (median 62), with recurrent GBM were treated. All patients had previously been treated with surgery, concurrent radiotherapy and temozolomide, and post-radiotherapy temozolomide. Bevacizumab (with or without lomustine) was administered to all patients at first recurrence. Patients were treated with nivolumab only (3mg/kg) once every 2 weeks at second recurrence. One cycle of nivolumab was defined as 2 treatments. Neurological evaluation was performed bi-weekly and neuroradiographic assessment every 4 weeks. Results: A total of 37 treatment cycles (median 2) were administered of nivolumab in which there were 14 Grade 2 adverse events (AEs) and Grade 3 AEs in 2 patients. No Grade 4 or 5 AEs were seen. Following 1 month of nivolumab, 7 patients’ demonstrated progressive disease and discontinued therapy. No patient demonstrated a response though 9 patients demonstrated neuroradiographic stable response. Survival in the entire cohort ranged from 2 - 6 months with a median of 3.5 months (CI: 2.8, 4.2). Median and 6-month PFS at 6 months was 2.0 months (range 1-5 months; CI: 1.3, 2.7) and 0% respectively. Conclusions: Nivolumab salvage therapy demonstrated no survival advantage in patients with recurrent bevacizumab refractory GBM emphasizing a continued unmet need in neuro-oncology.

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