ORCID Profile
0000-0002-2179-3498
Current Organisation
CentraleSupelec
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Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22489975.V1
Abstract: Radiological feature impact on survival. Radiological features include progression of target lesions 20%, progression of non-target lesions 50% and the emergence of new lesions.
Publisher: IOP Publishing
Date: 17-10-2022
Abstract: Objective. Dose-rate effects in Gamma Knife radiosurgery treatments can lead to varying biologically effective dose (BED) levels for the same physical dose. The non-convex BED model depends on the delivery sequence and creates a non-trivial treatment planning problem. We investigate the feasibility of employing inverse planning methods to generate treatment plans exhibiting desirable BED characteristics using the per iso-centre beam-on times and delivery sequence. Approach. We implement two dedicated optimisation algorithms. One approach relies on mixed-integer linear programming (MILP) using a purposely developed convex underestimator for the BED to mitigate local minima issues at the cost of computational complexity. The second approach (local optimisation) is faster and potentially usable in a clinical setting but more prone to local minima issues. It sequentially executes the beam-on time (quasi-Newton method) and sequence optimisation (local search algorithm). We investigate the trade-off between time to convergence and solution quality by evaluating the resulting treatment plans’ objective function values and clinical parameters. We also study the treatment time dependence of the initial and optimised plans using BED 95 (BED delivered to 95% of the target volume) values. Main results. When optimising the beam-on times and delivery sequence, the local optimisation approach converges several orders of magnitude faster than the MILP approach (minutes versus hours–days) while typically reaching within 1.2% (0.02–2.08%) of the final objective function value. The quality parameters of the resulting treatment plans show no meaningful difference between the local and MILP optimisation approaches. The presented optimisation approaches remove the treatment time dependence observed in the original treatment plans, and the chosen objectives successfully promote more conformal treatments. Significance. We demonstrate the feasibility of using an inverse planning approach within a reasonable time frame to ensure BED-based objectives are achieved across varying treatment times and highlight the prospect of further improvements in treatment plan quality.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2019
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22489969.V1
Abstract: InterpretML Overall Importance using Explainable Boosting Machines (EBM). The mean absolute score reflects the overall importance assigned by the model to predict each patient's category. All features appear important, with the number of organs affected by metastasis and the emergence of new lesions as the most important ones
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633111
Abstract: Log Hazard ratio (HR) of each parameter obtained from a cox model fitted to predict survival.
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633114
Abstract: Organ involvement's impact on survival. The organs selected were the ones most affected in our study's patients. The tests are for the survival of patients whether an organ has a tumor or not. The organs were: A) Liver B) Lung C) Subdiaphragm lymph nodes D) Supra Diaphragm lymph nodes E) Peritoneal Carcinosis and F) Bone.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22489981
Abstract: Organ involvement's impact on survival. The organs selected were the ones most affected in our study's patients. The tests are for the survival of patients whether an organ has a tumor or not. The organs were: A) Liver B) Lung C) Subdiaphragm lymph nodes D) Supra Diaphragm lymph nodes E) Peritoneal Carcinosis and F) Bone.
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633108.V1
Abstract: Radiological feature impact on survival. Radiological features include progression of target lesions 20%, progression of non-target lesions 50% and the emergence of new lesions.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22489981.V1
Abstract: Organ involvement's impact on survival. The organs selected were the ones most affected in our study's patients. The tests are for the survival of patients whether an organ has a tumor or not. The organs were: A) Liver B) Lung C) Subdiaphragm lymph nodes D) Supra Diaphragm lymph nodes E) Peritoneal Carcinosis and F) Bone.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22489984
Abstract: Correlation tables of our criteria. Bottom left are spearman coefficients, top right are Phi_K coefficients.
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633117
Abstract: Correlation tables of our criteria. Bottom left are spearman coefficients, top right are Phi_K coefficients.
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633120.V1
Abstract: Log hazard ratios (HR) computed by cox model. The variables studied were: GRIm, RMH, NLR BL & the iPD score. The only significant variable was the iPD score (HR = 2, p 0.005).
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22489987
Abstract: Log hazard ratios (HR) computed by cox model. The variables studied were: GRIm, RMH, NLR BL & the iPD score. The only significant variable was the iPD score (HR = 2, p 0.005).
Publisher: Elsevier BV
Date: 03-2020
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22489978
Abstract: Log Hazard ratio (HR) of each parameter obtained from a cox model fitted to predict survival.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.C.6533077.V1
Abstract: AbstractPurpose: The objective of the study is to propose the immunotherapy progression decision (iPD) score, a practical tool based on patient features that are available at the first evaluation of immunotherapy treatment, to help oncologists decide whether to continue the treatment or switch rapidly to another therapeutic line when facing a progressive disease patient at the first evaluation. Experimental Design: This retrospective study included 107 patients with progressive disease at first evaluation according to RECIST 1.1. Clinical, radiological, and biological data at baseline and first evaluation were analyzed. An external validation set consisting of 31 patients with similar baseline characteristics was used for the validation of the score. Results: Variables were analyzed in a univariate study. The iPD score was constructed using only independent variables, each considered as a worsening factor for the survival of patients. The patients were stratified in three groups: good prognosis (GP), poor prognosis (PP), and critical prognosis (CP). Each group showed significantly different survivals (GP: 11.4, PP: 4.4, CP: 2.3 months median overall survival, i P /i 0.001, log-rank test). Moreover, the iPD score was able to detect the pseudoprogressors better than other scores. On the validation set, CP patients had significantly worse survival than PP and GP patients ( i P /i 0.05, log-rank test). Conclusions: The iPD score provides oncologists with a new evaluation, computable at first progression, to decide whether treatment should be continued (for the GP group), or immediately changed for the PP and CP groups. Further validation on larger cohorts is needed to prove its efficacy in clinical practice. /
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.C.6533077.V2
Abstract: AbstractPurpose: The objective of the study is to propose the immunotherapy progression decision (iPD) score, a practical tool based on patient features that are available at the first evaluation of immunotherapy treatment, to help oncologists decide whether to continue the treatment or switch rapidly to another therapeutic line when facing a progressive disease patient at the first evaluation. Experimental Design: This retrospective study included 107 patients with progressive disease at first evaluation according to RECIST 1.1. Clinical, radiological, and biological data at baseline and first evaluation were analyzed. An external validation set consisting of 31 patients with similar baseline characteristics was used for the validation of the score. Results: Variables were analyzed in a univariate study. The iPD score was constructed using only independent variables, each considered as a worsening factor for the survival of patients. The patients were stratified in three groups: good prognosis (GP), poor prognosis (PP), and critical prognosis (CP). Each group showed significantly different survivals (GP: 11.4, PP: 4.4, CP: 2.3 months median overall survival, i P /i 0.001, log-rank test). Moreover, the iPD score was able to detect the pseudoprogressors better than other scores. On the validation set, CP patients had significantly worse survival than PP and GP patients ( i P /i 0.05, log-rank test). Conclusions: The iPD score provides oncologists with a new evaluation, computable at first progression, to decide whether treatment should be continued (for the GP group), or immediately changed for the PP and CP groups. Further validation on larger cohorts is needed to prove its efficacy in clinical practice. /
Publisher: American Association for Cancer Research (AACR)
Date: 04-2202
DOI: 10.1158/1078-0432.22489990.V1
Abstract: Distribution of primary cancer types in the development set (n=107).
Publisher: MDPI AG
Date: 07-12-2020
DOI: 10.3390/MI11121084
Abstract: High accuracy measurement of size is essential in physical and biomedical sciences. Various sizing techniques have been widely used in sorting colloidal materials, analyzing bioparticles and monitoring the qualities of food and atmosphere. Most imaging-free methods such as light scattering measure the averaged size of particles and have difficulties in determining non-spherical particles. Imaging acquisition using camera is capable of observing in idual nanoparticles in real time, but the accuracy is compromised by the image defocusing and instrumental calibration. In this work, a machine learning-based pipeline is developed to facilitate a high accuracy imaging-based particle sizing. The pipeline consists of an image segmentation module for cell identification and a machine learning model for accurate pixel-to-size conversion. The results manifest a significantly improved accuracy, showing great potential for a wide range of applications in environmental sensing, biomedical diagnostical, and material characterization.
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633099.V1
Abstract: Effect of the removal of each criterion on the accuracy of a multinomial logistic regression fit to predict the category of each patient. The minimal losses are underlined, and the maximum are written in bold.
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633114.V1
Abstract: Organ involvement's impact on survival. The organs selected were the ones most affected in our study's patients. The tests are for the survival of patients whether an organ has a tumor or not. The organs were: A) Liver B) Lung C) Subdiaphragm lymph nodes D) Supra Diaphragm lymph nodes E) Peritoneal Carcinosis and F) Bone.
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633120
Abstract: Log hazard ratios (HR) computed by cox model. The variables studied were: GRIm, RMH, NLR BL & the iPD score. The only significant variable was the iPD score (HR = 2, p 0.005).
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633123
Abstract: Distribution of primary cancer types in the development set (n=107).
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22489978.V1
Abstract: Log Hazard ratio (HR) of each parameter obtained from a cox model fitted to predict survival.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22489972
Abstract: InterpretML Overall Importance using Explainable Boosting Machines (EBM). The mean absolute score reflects the overall importance assigned by the model to predict each patient's category. All features appear important, with the number of organs affected by metastasis and the emergence of new lesions as the most important ones.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22489975
Abstract: Radiological feature impact on survival. Radiological features include progression of target lesions 20%, progression of non-target lesions 50% and the emergence of new lesions.
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633123.V1
Abstract: Distribution of primary cancer types in the development set (n=107).
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22489969
Abstract: InterpretML Overall Importance using Explainable Boosting Machines (EBM). The mean absolute score reflects the overall importance assigned by the model to predict each patient's category. All features appear important, with the number of organs affected by metastasis and the emergence of new lesions as the most important ones
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22489963.V1
Abstract: Survival of pseudo-PD vs confirmed PD in our cohort.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22489984.V1
Abstract: Correlation tables of our criteria. Bottom left are spearman coefficients, top right are Phi_K coefficients.
Publisher: Springer International Publishing
Date: 2021
Publisher: MDPI AG
Date: 04-03-2022
Abstract: Purpose: The objective of our study is to propose fast, cost-effective, convenient, and effective biomarkers using the perfusion parameters from dynamic contrast-enhanced ultrasound (DCE-US) for the evaluation of immune checkpoint inhibitors (ICI) early response. Methods: The retrospective cohort used in this study included 63 patients with metastatic cancer eligible for immunotherapy. DCE-US was performed at baseline, day 8 (D8), and day 21 (D21) after treatment onset. A tumor perfusion curve was modeled on these three dates, and change in the seven perfusion parameters was measured between baseline, D8, and D21. These perfusion parameters were studied to show the impact of their variation on the overall survival (OS). Results: After the removal of missing or suboptimal DCE-US, the Baseline-D8, the Baseline-D21, and the D8-D21 groups included 37, 53, and 33 patients, respectively. A decrease of more than 45% in the area under the perfusion curve (AUC) between baseline and D21 was significantly associated with better OS (p = 0.0114). A decrease of any amount in the AUC between D8 and D21 was also significantly associated with better OS (p = 0.0370). Conclusion: AUC from DCE-US looks to be a promising new biomarker for fast, effective, and convenient immunotherapy response evaluation.
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633117.V1
Abstract: Correlation tables of our criteria. Bottom left are spearman coefficients, top right are Phi_K coefficients.
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633096
Abstract: Survival of pseudo-PD vs confirmed PD in our cohort.
Publisher: Elsevier BV
Date: 07-2023
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633099
Abstract: Effect of the removal of each criterion on the accuracy of a multinomial logistic regression fit to predict the category of each patient. The minimal losses are underlined, and the maximum are written in bold.
Publisher: Springer International Publishing
Date: 2020
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22489963
Abstract: Survival of pseudo-PD vs confirmed PD in our cohort.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22489966
Abstract: Effect of the removal of each criterion on the accuracy of a multinomial logistic regression fit to predict the category of each patient. The minimal losses are underlined, and the maximum are written in bold.
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633102.V1
Abstract: InterpretML Overall Importance using Explainable Boosting Machines (EBM). The mean absolute score reflects the overall importance assigned by the model to predict each patient's category. All features appear important, with the number of organs affected by metastasis and the emergence of new lesions as the most important ones
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22489987.V1
Abstract: Log hazard ratios (HR) computed by cox model. The variables studied were: GRIm, RMH, NLR BL & the iPD score. The only significant variable was the iPD score (HR = 2, p 0.005).
Publisher: Springer Science and Business Media LLC
Date: 13-09-2020
Publisher: American Association for Cancer Research (AACR)
Date: 31-01-2023
DOI: 10.1158/1078-0432.CCR-22-0890
Abstract: The objective of the study is to propose the immunotherapy progression decision (iPD) score, a practical tool based on patient features that are available at the first evaluation of immunotherapy treatment, to help oncologists decide whether to continue the treatment or switch rapidly to another therapeutic line when facing a progressive disease patient at the first evaluation. This retrospective study included 107 patients with progressive disease at first evaluation according to RECIST 1.1. Clinical, radiological, and biological data at baseline and first evaluation were analyzed. An external validation set consisting of 31 patients with similar baseline characteristics was used for the validation of the score. Variables were analyzed in a univariate study. The iPD score was constructed using only independent variables, each considered as a worsening factor for the survival of patients. The patients were stratified in three groups: good prognosis (GP), poor prognosis (PP), and critical prognosis (CP). Each group showed significantly different survivals (GP: 11.4, PP: 4.4, CP: 2.3 months median overall survival, P & 0.001, log-rank test). Moreover, the iPD score was able to detect the pseudoprogressors better than other scores. On the validation set, CP patients had significantly worse survival than PP and GP patients (P & 0.05, log-rank test). The iPD score provides oncologists with a new evaluation, computable at first progression, to decide whether treatment should be continued (for the GP group), or immediately changed for the PP and CP groups. Further validation on larger cohorts is needed to prove its efficacy in clinical practice.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22489972.V1
Abstract: InterpretML Overall Importance using Explainable Boosting Machines (EBM). The mean absolute score reflects the overall importance assigned by the model to predict each patient's category. All features appear important, with the number of organs affected by metastasis and the emergence of new lesions as the most important ones.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2200
DOI: 10.1158/1078-0432.22489990
Abstract: Distribution of primary cancer types in the development set (n=107).
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.C.6533077
Abstract: AbstractPurpose: The objective of the study is to propose the immunotherapy progression decision (iPD) score, a practical tool based on patient features that are available at the first evaluation of immunotherapy treatment, to help oncologists decide whether to continue the treatment or switch rapidly to another therapeutic line when facing a progressive disease patient at the first evaluation. Experimental Design: This retrospective study included 107 patients with progressive disease at first evaluation according to RECIST 1.1. Clinical, radiological, and biological data at baseline and first evaluation were analyzed. An external validation set consisting of 31 patients with similar baseline characteristics was used for the validation of the score. Results: Variables were analyzed in a univariate study. The iPD score was constructed using only independent variables, each considered as a worsening factor for the survival of patients. The patients were stratified in three groups: good prognosis (GP), poor prognosis (PP), and critical prognosis (CP). Each group showed significantly different survivals (GP: 11.4, PP: 4.4, CP: 2.3 months median overall survival, i P /i 0.001, log-rank test). Moreover, the iPD score was able to detect the pseudoprogressors better than other scores. On the validation set, CP patients had significantly worse survival than PP and GP patients ( i P /i 0.05, log-rank test). Conclusions: The iPD score provides oncologists with a new evaluation, computable at first progression, to decide whether treatment should be continued (for the GP group), or immediately changed for the PP and CP groups. Further validation on larger cohorts is needed to prove its efficacy in clinical practice. /
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633102
Abstract: InterpretML Overall Importance using Explainable Boosting Machines (EBM). The mean absolute score reflects the overall importance assigned by the model to predict each patient's category. All features appear important, with the number of organs affected by metastasis and the emergence of new lesions as the most important ones
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633105
Abstract: InterpretML Overall Importance using Explainable Boosting Machines (EBM). The mean absolute score reflects the overall importance assigned by the model to predict each patient's category. All features appear important, with the number of organs affected by metastasis and the emergence of new lesions as the most important ones.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22489966.V1
Abstract: Effect of the removal of each criterion on the accuracy of a multinomial logistic regression fit to predict the category of each patient. The minimal losses are underlined, and the maximum are written in bold.
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633105.V1
Abstract: InterpretML Overall Importance using Explainable Boosting Machines (EBM). The mean absolute score reflects the overall importance assigned by the model to predict each patient's category. All features appear important, with the number of organs affected by metastasis and the emergence of new lesions as the most important ones.
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633108
Abstract: Radiological feature impact on survival. Radiological features include progression of target lesions 20%, progression of non-target lesions 50% and the emergence of new lesions.
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633096.V1
Abstract: Survival of pseudo-PD vs confirmed PD in our cohort.
Publisher: American Association for Cancer Research (AACR)
Date: 14-04-2023
DOI: 10.1158/1078-0432.22633111.V1
Abstract: Log Hazard ratio (HR) of each parameter obtained from a cox model fitted to predict survival.
Location: United States of America
No related grants have been discovered for Hugues Talbot.