scholarly journals Proliferation Saturation Index in an adaptive Bayesian approach to predict patient-specific radiotherapy responses

2018 ◽  
Author(s):  
Enakshi D. Sunassee ◽  
Dean Tan ◽  
Tianlin Ji ◽  
Renee Brady ◽  
Eduardo G. Moros ◽  
...  
Keyword(s):  
Treatment Response ◽  
Bayesian Approach ◽  
Saturation Index ◽  
Treatment Protocol ◽  
Tumor Grade ◽  
High Accuracy ◽  
Radiation Response ◽  
Dose Fractionation ◽  
Patient Specific ◽  
Multiple Variables

AbstractPurposeRadiotherapy prescription dose and dose fractionation protocols vary little between individual patients having the same tumor grade and stage. To personalize radiotherapy a predictive model is needed to simulate radiation response. Previous modeling attempts with multiple variables and parameters have been shown to yield excellent data fits at the cost of nonidentifiability and clinically unrealistic results.Materials and MethodsWe develop a mathematical model based on a proliferation saturation index (PSI) that is a measurement of pre-treatment tumor volume-to-carrying capacity ratio that modulates intrinsic tumor growth and radiation response rates. In an adaptive Bayesian approach, we utilize an increasing number of data points for individual patients for predicting response to subsequent radiation doses.ResultsModel analysis shows that using PSI as the only patient-specific parameter, model simulations can fit longitudinal clinical data with high accuracy (R2=0.84). By analyzing tumor response to radiation using daily CT scans early in the treatment, response to the remaining treatment fractions can be predicted after two weeks with high accuracy (c-index=0.89).ConclusionThe PSI model may be suited to forecast treatment response for individual patients and offer actionable decision points for mid-treatment protocol adaptation. The presented work provides an actionable image-derived biomarker prior to and during therapy to personalize and adapt radiotherapy.

scholarly journals Proliferation saturation index in an adaptive Bayesian approach to predict patient-specific radiotherapy responses

2019 ◽  
Vol 95 (10) ◽  
pp. 1421-1426 ◽  
Author(s):  
Enakshi D. Sunassee ◽  
Dean Tan ◽  
Nathan Ji ◽  
Renee Brady ◽  
Eduardo G. Moros ◽  
...  
Keyword(s):  
Bayesian Approach ◽  
Saturation Index ◽  
Patient Specific

scholarly journals Predicting patient-specific radiotherapy responses in head and neck cancer to personalize radiation dose fractionation

2019 ◽  
Author(s):  
Heiko Enderling ◽  
Enakshi Sunassee ◽  
Jimmy J. Caudell
Keyword(s):  
Mathematical Modeling ◽  
Head And Neck Cancer ◽  
Saturation Index ◽  
Clinical Stage ◽  
Standard Of Care ◽  
Dose Fractionation ◽  
Patient Specific ◽  
Molecular Profile ◽  
Prospective Evaluation ◽  
Radiation Dose Fractionation

AbstractHuman papillomavirus (HPV) related oropharyngeal cancer (OPC) is one of the few types of cancers increasing in incidence. HPV+ OPC treatment with radiotherapy (RT) provides 75-95% five-year locoregional control (LRC). Why some but not all patients with similar clinical stage and molecular profile are controlled remains unknown. We propose the proliferation saturation index, PSI, as a mathematical modeling biomarker of tumor growth and RT response. The model predicts that patients with PSI<0.75 are likely to be cured by radiation, and that hyperfractionated radiation could improve response rates for patients with higher PSI that are predicted to fail standard of care RT. Prospective evaluation is currently ongoing.

scholarly journals Genetic biomarkers in the VEGF pathway predicting response to anti-VEGF therapy in age-related macular degeneration

2019 ◽  
Vol 4 (1) ◽  
pp. e000273
Author(s):  
Irina Balikova ◽  
Laurence Postelmans ◽  
Brigitte Pasteels ◽  
Pascale Coquelet ◽  
Janet Catherine ◽  
...  
Keyword(s):  
Visual Acuity ◽  
Macular Degeneration ◽  
Treatment Response ◽  
Multiple Testing ◽  
Age Related Macular Degeneration ◽  
Patient Specific ◽  
Multiple Testing Correction ◽  
Anti Vegf ◽  
Age Related ◽  
Vegf Pathway

ObjectiveAge-related macular degeneration (ARMD) is a leading cause of visual impairment. Intravitreal injections of anti-vascular endothelial growth factor (VEGF) are the standard treatment for wet ARMD. There is however, variability in patient responses, suggesting patient-specific factors influencing drug efficacy. We tested whether single nucleotide polymorphisms (SNPs) in genes encoding VEGF pathway members contribute to therapy response.Methods and analysisA retrospective cohort of 281 European wet ARMD patients treated with anti-VEGF was genotyped for 138 tagging SNPs in the VEGF pathway. Per patient, we collected best corrected visual acuity at baseline, after three loading injections and at 12 months. We also registered the injection number and changes in retinal morphology after three loading injections (central foveal thickness (CFT), intraretinal cysts and serous neuroepithelium detachment). Changes in CFT after 3 months were our primary outcome measure. Association of SNPs to response was assessed by binomial logistic regression. Replication was attempted by associating visual acuity changes to genotypes in an independent Japanese cohort.ResultsAssociation with treatment response was detected for seven SNPs, including in FLT4 (rs55667289: OR=0.746, 95% CI 0.63 to 0.88, p=0.0005) and KDR (rs7691507: OR=1.056, 95% CI 1.02 to 1.10, p=0.005; and rs2305945: OR=0.963, 95% CI 0.93 to 1.00, p=0.0472). Only association with rs55667289 in FLT4 survived multiple testing correction. This SNP was unavailable for testing in the replication cohort. Of six SNPs tested for replication, one was significant although not after multiple testing correction.ConclusionIdentifying genetic variants that define treatment response can help to develop individualised therapeutic approaches for wet ARMD patients and may point towards new targets in non-responders.

scholarly journals Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Michelle Przedborski ◽  
Munisha Smalley ◽  
Saravanan Thiyagarajan ◽  
Aaron Goldman ◽  
Mohammad Kohandel
Keyword(s):  
Neural Networks ◽  
Systems Biology ◽  
Clinical Data ◽  
Treatment Protocol ◽  
Response Prediction ◽  
Clinical Findings ◽  
Patient Specific ◽  
Patient Response ◽  
Triple Combination Therapy ◽  
Parameter Values

AbstractAnti-PD-1 immunotherapy has recently shown tremendous success for the treatment of several aggressive cancers. However, variability and unpredictability in treatment outcome have been observed, and are thought to be driven by patient-specific biology and interactions of the patient’s immune system with the tumor. Here we develop an integrative systems biology and machine learning approach, built around clinical data, to predict patient response to anti-PD-1 immunotherapy and to improve the response rate. Using this approach, we determine biomarkers of patient response and identify potential mechanisms of drug resistance. We develop systems biology informed neural networks (SBINN) to calculate patient-specific kinetic parameter values and to predict clinical outcome. We show how transfer learning can be leveraged with simulated clinical data to significantly improve the response prediction accuracy of the SBINN. Further, we identify novel drug combinations and optimize the treatment protocol for triple combination therapy consisting of IL-6 inhibition, recombinant IL-12, and anti-PD-1 immunotherapy in order to maximize patient response. We also find unexpected differences in protein expression levels between response phenotypes which complement recent clinical findings. Our approach has the potential to aid in the development of targeted experiments for patient drug screening as well as identify novel therapeutic targets.

scholarly journals Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Angela M. Jarrett ◽  
David A. Hormuth ◽  
Vikram Adhikarla ◽  
Prativa Sahoo ◽  
Daniel Abler ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Targeted Therapy ◽  
Treatment Response ◽  
Growth Factor Receptor ◽  
Patient Specific ◽  
Mathematical Framework ◽  
Breast Cancer Patients ◽  
Data Types ◽  
Her2 Breast Cancer ◽  
Mri Scans

AbstractWhile targeted therapies exist for human epidermal growth factor receptor 2 positive (HER2 +) breast cancer, HER2 + patients do not always respond to therapy. We present the results of utilizing a biophysical mathematical model to predict tumor response for two HER2 + breast cancer patients treated with the same therapeutic regimen but who achieved different treatment outcomes. Quantitative data from magnetic resonance imaging (MRI) and 64Cu-DOTA-trastuzumab positron emission tomography (PET) are used to estimate tumor density, perfusion, and distribution of HER2-targeted antibodies for each individual patient. MRI and PET data are collected prior to therapy, and follow-up MRI scans are acquired at a midpoint in therapy. Given these data types, we align the data sets to a common image space to enable model calibration. Once the model is parameterized with these data, we forecast treatment response with and without HER2-targeted therapy. By incorporating targeted therapy into the model, the resulting predictions are able to distinguish between the two different patient responses, increasing the difference in tumor volume change between the two patients by > 40%. This work provides a proof-of-concept strategy for processing and integrating PET and MRI modalities into a predictive, clinical-mathematical framework to provide patient-specific predictions of HER2 + treatment response.

Incorporation of Treatment Response, Tumor Grade and Receptor Status Improves Staging Quality in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy

2017 ◽  
Vol 24 (12) ◽  
pp. 3510-3517 ◽  
Author(s):  
John R. Bergquist ◽  
Brittany L. Murphy ◽  
Curtis B. Storlie ◽  
Elizabeth B. Habermann ◽  
Judy C. Boughey
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Treatment Response ◽  
Cancer Patients ◽  
Tumor Grade ◽  
Breast Cancer Patients ◽  
Receptor Status

Determining Peritoneal Dialysis Prescriptions by Employing a Patient -Specific Protocol

1993 ◽  
Vol 13 (3) ◽  
pp. 189-193 ◽  
Author(s):  
Paul M. Zabetakis ◽  
Randi Krapf ◽  
Maria v. DeVita ◽  
Gilbert W. Gleim ◽  
Michael F. Michelis
Keyword(s):  
New York ◽  
Peritoneal Dialysis ◽  
Creatinine Clearance ◽  
Treatment Protocol ◽  
Specific Treatment ◽  
Simple Calculation ◽  
Patient Specific ◽  
Specific Protocol ◽  
Continuous Cycling ◽  
Dialysate Volume

Objective To develop a formula that would permit a rapid and simple calculation of required dialysate volume needed to provide a predetermined daily creatinine clearance. Design Prospective study of peritoneal dialysis patients followed for 6 months. Setting A primary care teaching hospital in New York. Patients Twenty-six patients beginning peritoneal dialysis entered and completed the study. Intervention By employing each patient's measured peritoneal equilibration test (PET) and a standard clearance formula, a patient-specific treatment protocol (PSP) was calculated. The PET 2-hour DIP croat was used for continuous cycling peritoneal dialysis (CCPD) and the 4hour DIP patients on continuous ambulatory peritcornoeal dialysis (CAPD) to determine a PSP that would provide a minimum of 6 L of creatinine clearance daily. Main Outcome Measures Patients were followed for 6 months to assess the ability of this approach of maintaining acceptable levels of blood urea nitrogen, creatinine, albumin, and hematocrit over the 6–month period of observation. Results Our study of 26 patients revealed that only 6 patients (23%) could be treated with the standard prescription of 8 L/day on CAPD. The remaining 77% of our patients required 9–13 L/day for CAPD and 12–21 L/day for CCPD. All patients were free of uremic symptoms and demonstrated acceptable biochemical parameters over a 3–6 month period of observation. Conclusions A patient-specific protocol utilizing individually derived PET data provides an acceptable and easy to calculate initial treatment prescription for each patient that avoids the necessity for trial and error that has heretofore been employed.

Compliance with evidence-based guidelines for radiation of vertebral metastases.

2013 ◽  
Vol 31 (31_suppl) ◽  
pp. 36-36
Author(s):  
Heather A Curry ◽  
Arlene A. Forastiere ◽  
Reshma Jagsi ◽  
M. Lou Palladino
Keyword(s):  
Radiation Treatment ◽  
Peer To Peer ◽  
Patterns Of Care ◽  
Dose Fractionation ◽  
Patient Specific ◽  
Evidence Based ◽  
Vertebral Metastases ◽  
Case Review ◽  
Treatment Plans ◽  
Evidence Based Guidelines

36 Background: Evidence based guidelines pertaining to the management of bony metastases have been published. However, up to 30% of oncology treatments deviate from evidence based standards and widespread variations in clinical practice continue to exist. To explore patterns of care in the treatment of vertebral metastases in a group of working age, insured patients, we assessed treatment plans submitted for preauthorization through eviti Connect. Methods: Eviti Connect is a web-based application that enables oncology providers to obtain automated precertification for patients. The platform evaluates treatment plans for consistency with EBM and compliance with payer policies and plan language. All requests for radiation treatment submitted during a two year period from 6/1/11-5/31/13 were reviewed. Peer to peer discussions were conducted in cases that deviated from EBM. Results: A total of 229 cases for the treatment of vertebral metastases were submitted. 46/229 plans (19.8%) did not meet EBM standards. Some cases displayed more than one deviation. Reasons for non-compliance included atypical treatment schedules (8.69%), SRS/SBRT (36.9%), IMRT (32.6%), and IGRT (58.7%). In 26/46 cases (56.5%) the treating physician provided a medical rationale for the deviation. In 9 cases the physician altered the plan to be compliant; in 5 cases the physician did not agree to a change. The most common dose fractionation schedules were 30 Gy/10 fractions (48.9%) and 37.5 Gy/15 fractions (20.5%). 17 cases were treated using 20 Gy/5 fractions and only 2 cases were treated using 8 Gy X 1. Conclusions: Radiation of vertebral metastases was prescribed in accordance with EBM in the majority of cases. The main reasons for deviation were patient-specific issues that justified the medical necessity of the variance. Case review and peer to peer discussion contributed to understanding the rationale for treatment deviation from guidelines and allowed providers to bring plans into compliance with EBM. Overall only 5% of plans were non-evidence based or lacked a medical justification for deviation. Consistent with patterns of care across the US, within this group of patients, single fraction and hypofractionated radiation regimens were underutilized.

Sign in / Sign up

Export Citation Format

Share Document