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2. Multi-task learning for predicting quality-of-life and independence in activities of daily living after stroke: a proof-of-concept study.

Author

Nguyen, Thi Nguyet Que, García-Rudolph, Alejandro, Saurí, Joan, and Kelleher, John D.

Abstract

A health-related (HR) profile is a set of multiple health-related items recording the status of the patient at different follow-up times post-stroke. In order to support clinicians in designing rehabilitation treatment programs, we propose a novel multi-task learning (MTL) strategy for predicting post-stroke patient HR profiles. The HR profile in this study is measured by the Barthel index (BI) assessment or by the EQ-5D-3L questionnaire. Three datasets are used in this work and for each dataset six neural network architectures are developed and tested. Results indicate that an MTL architecture combining a pre-trained network for all tasks with a concatenation strategy conditioned by a task grouping method is a promising approach for predicting the HR profile of a patient with stroke at different phases of the patient journey. These models obtained a mean F1-score of 0.434 (standard deviation 0.022, confidence interval at 95% [0.428, 0.44]) calculated across all the items when predicting BI at 3 months after stroke (MaS), 0.388 (standard deviation 0.029, confidence interval at 95% [0.38, 0.397]) when predicting EQ-5D-3L at 6MaS, and 0.462 (standard deviation 0.029, confidence interval at 95% [0.454, 0.47]) when predicting the EQ-5D-3L at 18MaS. Furthermore, our MTL architecture outperforms the reference single-task learning models and the classic MTL of all tasks in 8 out of 10 tasks when predicting BI at 3MaS and has better prediction performance than the reference models on all tasks when predicting EQ-5D-3L at 6 and 18MaS. The models we present in this paper are the first models to predict the components of the BI or the EQ-5D-3L, and our results demonstrate the potential benefits of using MTL in a health context to predict patient profiles. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Vibrational spectroscopy of liquid biopsies for prostate cancer diagnosis

Author

Moreau, Johanna, Bouzy, Pascaline, Guillard, Julien, Garnotel, Roselyne, Marchal, Aude, Gobinet, Cyril, Terryn, Christine, Thiéfin, Gérard, Medipally, Dinesh, Cullen, Daniel, Untereiner, Valérie, Sockalingum, Ganesh, Maguire, Adrian, Nguyen, Thi Nguyet Que, Bryant, Jane, Noone, Emma, Bradshaw, Shirley, Finn, Marie, Dunne, Mary, Shannon, Aoife, Armstrong, John, Meade, Aidan, Lyng, Fiona, Biospectroscopie Translationnelle - EA 7506 (BIOSPECT), Université de Reims Champagne-Ardenne (URCA), Plateforme en Imagerie Cellulaire et Tissulaire (PICT), Université de Reims Champagne-Ardenne (URCA)-SFR CAP Santé (Champagne-Ardenne Picardie Santé), and Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)

Subjects
Pathology, medicine.medical_specialty, principal component analysis, [PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], Infrared spectroscopy, [SDV.CAN]Life Sciences [q-bio]/Cancer, [SDV.BC.BC]Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], Malignancy, lcsh:RC254-282, 03 medical and health sciences, Prostate cancer, 0302 clinical medicine, Prostate, Diagnosis, [SDV.BC.IC]Life Sciences [q-bio]/Cellular Biology/Cell Behavior [q-bio.CB], medicine, Liquid biopsy, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, Original Research, 0303 health sciences, medicine.diagnostic_test, liquid biopsy, business.industry, Rectal examination, medicine.disease, prostate cancer, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, 3. Good health, [SDV.BBM.BP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biophysics, Prostate-specific antigen, FTIR spectroscopy, medicine.anatomical_structure, Oncology, 030220 oncology & carcinogenesis, Raman spectroscopy, [SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic, partial least squares discriminant analysis and classical least squares fitting analysis, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

Background: Screening for prostate cancer with prostate specific antigen and digital rectal examination allows early diagnosis of prostate malignancy but has been associated with poor sensitivity and specificity. There is also a considerable risk of over-diagnosis and over-treatment, which highlights the need for better tools for diagnosis of prostate cancer. This study investigates the potential of high throughput Raman and Fourier Transform Infrared (FTIR) spectroscopy of liquid biopsies for rapid and accurate diagnosis of prostate cancer. Methods: Blood samples (plasma and lymphocytes) were obtained from healthy control subjects and prostate cancer patients. FTIR and Raman spectra were recorded from plasma samples, while Raman spectra were recorded from the lymphocytes. The acquired spectral data was analysed with various multivariate statistical methods, principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and classical least squares (CLS) fitting analysis. Results: Discrimination was observed between the infrared and Raman spectra of plasma and lymphocytes from healthy donors and prostate cancer patients using PCA. In addition, plasma and lymphocytes displayed differentiating signatures in patients exhibiting different Gleason scores. A PLS-DA model was able to discriminate these groups with sensitivity and specificity rates ranging from 90% to 99%. CLS fitting analysis identified key analytes that are involved in the development and progression of prostate cancer. Conclusions: This technology may have potential as an alternative first stage diagnostic triage for prostate cancer. This technology can be easily adaptable to many other bodily fluids and could be useful for translation of liquid biopsy-based diagnostics into the clinic.

Published
2020

15. Supplementary_material – Supplemental material for Vibrational spectroscopy of liquid biopsies for prostate cancer diagnosis

Author

Medipally, Dinesh K. R., Cullen, Daniel, Untereiner, Valérie, Sockalingum, Ganesh D., Maguire, Adrian, Nguyen, Thi Nguyet Que, Bryant, Jane, Noone, Emma, Bradshaw, Shirley, Finn, Marie, Dunne, Mary, Shannon, Aoife M., Armstrong, John, Meade, Aidan D., and Lyng, Fiona M

Subjects
110203 Respiratory Diseases, FOS: Clinical medicine, 111702 Aged Health Care, FOS: Health sciences, 111599 Pharmacology and Pharmaceutical Sciences not elsewhere classified, 111299 Oncology and Carcinogenesis not elsewhere classified
Abstract

Supplemental material, Supplementary_material for Vibrational spectroscopy of liquid biopsies for prostate cancer diagnosis by Dinesh K. R. Medipally, Daniel Cullen, Valérie Untereiner, Ganesh D. Sockalingum, Adrian Maguire, Thi Nguyet Que Nguyen, Jane Bryant, Emma Noone, Shirley Bradshaw, Marie Finn, Mary Dunne, Aoife M. Shannon, John Armstrong, Aidan D. Meade and Fiona M Lyng in Therapeutic Advances in Medical Oncology

Published
2020
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18. Vibrational spectroscopy of liquid biopsies for prostate cancer diagnosis

Author

Medipally, Dinesh K. R., primary, Cullen, Daniel, additional, Untereiner, Valérie, additional, Sockalingum, Ganesh D., additional, Maguire, Adrian, additional, Nguyen, Thi Nguyet Que, additional, Bryant, Jane, additional, Noone, Emma, additional, Bradshaw, Shirley, additional, Finn, Marie, additional, Dunne, Mary, additional, Shannon, Aoife M., additional, Armstrong, John, additional, Meade, Aidan D., additional, and Lyng, Fiona M, additional

Published
2020
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19. Discrimination of breast cancer from benign tumours using Raman spectroscopy

Author

Lyng, Fiona M., Traynor, Damien, Nguyen, Thi Nguyet Que, Meade, Aidan D., Rakib, Fazle, Al-Saady, Rafif, Goormaghtigh, Erik, Al-Saad, Khalid, and Ali, Mohamed H.

Subjects
Raman Spectroscopy, Support Vector Machine, Receptor, ErbB-2, Science, Psychologie appliquée, Invasive Ductal Carcinoma, Breast Neoplasms, tumours, Spectrum Analysis, Raman, Pathology and Laboratory Medicine, Research and Analysis Methods, Sensitivity and Specificity, Carcinomas, histopathological diagnosis, breast cancer, raman spectroscopy, Signs and Symptoms, Spectrum Analysis Techniques, malignant, Diagnostic Medicine, Breast Tumors, Breast Cancer, Medicine and Health Sciences, Cancer Detection and Diagnosis, Humans, Breast, skin and connective tissue diseases, Principal Component Analysis, Hyperplasia, Papillomas, Reproductive System, Discriminant Analysis, Correction, Cancers and Neoplasms, Biology and Life Sciences, Sciences bio-médicales et agricoles, Chemicals and Drugs, Receptors, Estrogen, Oncology, Lesions, Medicine, Female, benign, Anatomy, Biologie, Breast Tissue, Research Article
Abstract

Breast cancer is the most common cancer among women worldwide, with an estimated 1.7 million cases and 522,000 deaths in 2012. Breast cancer is diagnosed by histopathological examination of breast biopsy material but this is subjective and relies on morphological changes in the tissue. Raman spectroscopy uses incident radiation to induce vibrations in the molecules of a sample and the scattered radiation can be used to characterise the sample. This technique is rapid and non-destructive and is sensitive to subtle biochemical changes occurring at the molecular level. This allows spectral variations corresponding to disease onset to be detected. The aim of this work was to use Raman spectroscopy to discriminate between benign lesions (fibrocystic, fibroadenoma, intraductal papilloma) and cancer (invasive ductal carcinoma and lobular carcinoma) using formalin fixed paraffin preserved (FFPP) tissue. Haematoxylin and Eosin stained sections from the patient biopsies were marked by a pathologist. Raman maps were recorded from parallel unstained tissue sections. Immunohistochemical staining for estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2/neu) was performed on a further set of parallel sections. Both benign and cancer cases were positive for ER while only the cancer cases were positive for HER2. Significant spectral differences were observed between the benign and cancer cases and the benign cases could be differentiated from the cancer cases with good sensitivity and specificity. This study has shown the potential of Raman spectroscopy as an aid to histopathological diagnosis of breast cancer, in particular in the discrimination between benign and malignant tumours., SCOPUS: ar.j, info:eu-repo/semantics/published

Published
2019

20. Discrimination of breast cancer from benign tumours using Raman spectroscopy

Author

Lyng, F.M., Traynor, Damien, Nguyen, Thi Nguyet Que, Meade, Aidan A.D., Rakib, F., Al-Saady, R., Goormaghtigh, Erik, Al-Saad, K., Ali, M.H., Lyng, F.M., Traynor, Damien, Nguyen, Thi Nguyet Que, Meade, Aidan A.D., Rakib, F., Al-Saady, R., Goormaghtigh, Erik, Al-Saad, K., and Ali, M.H.

Abstract

Breast cancer is the most common cancer among women worldwide, with an estimated 1.7 million cases and 522,000 deaths in 2012. Breast cancer is diagnosed by histopathological examination of breast biopsy material but this is subjective and relies on morphological changes in the tissue. Raman spectroscopy uses incident radiation to induce vibrations in the molecules of a sample and the scattered radiation can be used to characterise the sample. This technique is rapid and non-destructive and is sensitive to subtle biochemical changes occurring at the molecular level. This allows spectral variations corresponding to disease onset to be detected. The aim of this work was to use Raman spectroscopy to discriminate between benign lesions (fibrocystic, fibroadenoma, intraductal papilloma) and cancer (invasive ductal carcinoma and lobular carcinoma) using formalin fixed paraffin preserved (FFPP) tissue. Haematoxylin and Eosin stained sections from the patient biopsies were marked by a pathologist. Raman maps were recorded from parallel unstained tissue sections. Immunohistochemical staining for estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2/neu) was performed on a further set of parallel sections. Both benign and cancer cases were positive for ER while only the cancer cases were positive for HER2. Significant spectral differences were observed between the benign and cancer cases and the benign cases could be differentiated from the cancer cases with good sensitivity and specificity. This study has shown the potential of Raman spectroscopy as an aid to histopathological diagnosis of breast cancer, in particular in the discrimination between benign and malignant tumours., SCOPUS: ar.j, info:eu-repo/semantics/published

Published
2019

21. Monitoring Radiotherapeutic Response in Prostate Cancer Patients Using High Throughput FTIR Spectroscopy of Liquid Biopsies

Author

Medipally, Dinesh K.R., primary, Nguyen, Thi Nguyet Que, additional, Bryant, Jane, additional, Untereiner, Valérie, additional, Sockalingum, Ganesh D., additional, Cullen, Daniel, additional, Noone, Emma, additional, Bradshaw, Shirley, additional, Finn, Marie, additional, Dunne, Mary, additional, Shannon, Aoife M., additional, Armstrong, John, additional, Lyng, Fiona M., additional, and Meade, Aidan D., additional

Published
2019
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24. Fourier-transform infrared imaging and clustering: toward an automated histology of normal colon

Author

Nguyen, Thi Nguyet Que, Jeannesson, Pierre, Groh, Audrey, Guenot, Dominique, Gobinet, Cyril, Matrice extracellulaire et dynamique cellulaire - UMR 7369 (MEDyC), Université de Reims Champagne-Ardenne (URCA)-SFR CAP Santé (Champagne-Ardenne Picardie Santé), Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), Equipe Biophotonique et Technologies pour la Santé, Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)-Université de Reims Champagne-Ardenne (URCA)-SFR CAP Santé (Champagne-Ardenne Picardie Santé), Progression tumorale et microenvironnement. Approches translationnelles et épidémiologie, Institut Régional du Cancer-Université de Strasbourg (UNISTRA)-CHU Strasbourg-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre Paul Strauss : Centre Régional de Lutte contre le Cancer (CRLCC), Cancéropôle Grand Est, Ligue contre le Cancer, URCA technological platform of cellular and tissular imaging PICT-IBiSA, Région Champagne-Ardenne, Région Alsace, MESR, SFR CAP Santé (Champagne-Ardenne Picardie Santé), Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Centre National de la Recherche Scientifique (CNRS), Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Centre National de la Recherche Scientifique (CNRS)-SFR CAP Santé (Champagne-Ardenne Picardie Santé), Université de Strasbourg (UNISTRA)-CHU Strasbourg-Les Hôpitaux Universitaires de Strasbourg (HUS)-Institut Régional du Cancer-Centre Paul Strauss : Centre Régional de Lutte contre le Cancer (CRLCC), and Frouin, Frédérique

Subjects
[SDV.IB] Life Sciences [q-bio]/Bioengineering, Image Processing, Signal Processing, Optical Imaging, [SDV.IB]Life Sciences [q-bio]/Bioengineering
Abstract

Congrès sous l’égide de la Société Française de Génie Biologique et Médical (SFGBM).; National audience; Fourier-transform infrared (FTIR) imaging is currently used as a non-destructive and label free method for analyzing biological specimens. Combined with unsupervised clustering method, this biophotonic approach allows to perform a spectral histopathology of human tissues. However, this method requires the subjective choice of the number of clusters. To overcome this problem, we developed a hierarchical double application of 9 cluster validity indices (CVIs) using K-Means clustering. Applying this approach to FTIR images of normal human colon tissue samples, PBM and SI reveal to be the most efficient indices in retreving the main structures of colon histology. These results suggest that the hierarchical double CVI application is thus a promising method for an automated spectral histology.

Published
2015

25. Optimal Spectral Histology of Human Normal Colon by Genetic Algorithm

Author

Farah, Ihsen, Nguyen, Thi Nguyet Que, Groh, Audrey, Guenot, Dominique, Jeannesson, Pierre, Gobinet, Cyril, Matrice extracellulaire et dynamique cellulaire - UMR 7369 (MEDyC), SFR CAP Santé (Champagne-Ardenne Picardie Santé), Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Centre National de la Recherche Scientifique (CNRS), Equipe Biophotonique et Technologies pour la Santé, Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Centre National de la Recherche Scientifique (CNRS)-SFR CAP Santé (Champagne-Ardenne Picardie Santé), Progression tumorale et microenvironnement. Approches translationnelles et épidémiologie, Université de Strasbourg (UNISTRA)-CHU Strasbourg-Les Hôpitaux Universitaires de Strasbourg (HUS)-Institut Régional du Cancer-Centre Paul Strauss : Centre Régional de Lutte contre le Cancer (CRLCC), Cancéropôle Grand-Est, Ligue contre le Cancer, URCA technological platform of cellular and tissular imaging PICT-IBiSA, Région Champagne-Ardenne, Région AlsaceMESR, Université de Reims Champagne-Ardenne (URCA)-SFR CAP Santé (Champagne-Ardenne Picardie Santé), Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)-Université de Reims Champagne-Ardenne (URCA)-SFR CAP Santé (Champagne-Ardenne Picardie Santé), Institut Régional du Cancer-Université de Strasbourg (UNISTRA)-CHU Strasbourg-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre Paul Strauss : Centre Régional de Lutte contre le Cancer (CRLCC), and Frouin, Frédérique

Subjects
[SDV.IB] Life Sciences [q-bio]/Bioengineering, ComputingMethodologies_PATTERNRECOGNITION, Image Processing, Signal Processing, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Optical imaging
Abstract

National audience; Fourier transform infrared (FTIR) imaging combined with unsupervised clustering method, such as k-means, achieves a real histology of human tissues. This technique has been successfully applied to diagnose different cancers. However, the clustering methods used in spectral histology are local search algorithms, i.e. these methods converge to a local optimum. Metaheuristics are effective methods to overcome this problem and to reach the optimal solution. In this work, we propose a genetic algorithm for the optimal clustering of FTIR images of normal human colon tissues. The obtained results show the efficiency of the proposed genetic algorithm to retrieve more precisely than k-means the structures of normal colon.

Published
2015

30. Understanding Social Risk Variation Across Reintegration of Post-Ischemic Stroke Patients

Author

Cisek K, Nguyen TNQ, Garcia-Rudolph A, Saurí J, Kelleher JD, and Pluta R

Abstract

The quality of life of post-ischemic stroke patients during reintegration is affected by a range of factors, including the risk of insufficient social and family support, as well as socio-economic status. A patient who is unable to access needed social support, such as home health care or a day center, is at a greater risk of poorer quality of life during reintegration. Consequently, the key goals of post-stroke reintegration are to improve patient outcomes across these factors, to inform reintegration decisions, as well as design personalized interventions for patients with social risk. This chapter presents a case-study of 240 patients of the Catalonia region of Spain that uses data visualization techniques (known as Sankey diagrams) to provide insight into changes in quality of life risk factors such as gender, and stroke severity, during reintegration. As supported by the case-study, social risk is a complex and multifactorial phenomenon that can vary significantly for an individual over the course of stroke rehabilitation and reintegration., (Copyright: The Authors.)

Published
2021
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