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Imaging Biomarkers for Stratified Medicine and Personalised Healthcare
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Imaging Biomarkers for Stratified Medicine and Personalised Healthcare

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: closed (1 February 2022) | Viewed by 9067

Special Issue Editor

Prof. John C. Waterton

E-Mail Website
Guest Editor
Centre for Imaging Sciences, Division of Informatics Imaging & Data Sciences, School of Health Sciences, Faculty of Biology Medicine & Health, University of Manchester, Manchester Academic Health Sciences Centre, Manchester M13 9PL, UK
Interests: evaluation and use of imaging biomarkers in drug development; biomarker ontologies; biomedical imaging; imaging biomarkers; personalized medicine; translation

Special Issue Information

Dear Colleagues,

Imaging biomarkers (IBs) are widely used in medicine. They have widely used for many years – indeed some were introduced as long ago as the 1950s, when molecular biology was still in its infancy.  IBs are routinely employed by regulatory agencies as surrogate endpoints or monitoring methods, but still only rarely as companion diagnostics. 

Prognostic IBs forecast whether patients will do well or badly, irrespective of any treatment they may receive.  Thus, in oncology, TNM staging biomarkers tend to be prognostic: the outcome forecast for an M1 patient is always worse than for an otherwise-similar M0 patient.

Personalised medicine, however, relies not on Prognostic but on Predictive (or Prescriptive) biomarkers. A Predictive biomarker forecasts which patients will benefit from a given treatment, and which patients will fail to benefit, or even by harmed, by that treatment.  Thus in thalassaemia, FDA has determined that patients whose liver R2 biomarker in MRI is elevated may benefit from treatment with deferasirox, while patients with normal liver R2 are unlikely to benefit, and may be harmed by the drug’s side-effects.

The development and validation of Predictive IBs is hard. Naturally a Predictive IB needs a technically validated assay, plus a biological rationale supported by a robust platform of evidence. But also, it needs clinical trial evidence showing, not only that the IB forecasts which patients will respond well to an investigational treatment, but also that the IB performs better at forecasting the response to the investigational treatment than at forecasting the response to alternative or standard-of-care treatments.

Different IBs are at different stages in their validation journey.  This Special Issue includes reports on a range of IBs with potential in personalised medicine, and describes current work to extend the platform of evidence that will eventually support their clinical use in improving patient outcomes.

Prof. John C. Waterton
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • imaging biomarkers
  • translational imaging
  • drug development
  • clinical decision-making
  • personalized medicine
  • stratified medicine

Published Papers (4 papers)

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Research

16 pages, 2827 KiB  
Article
Personalized Prediction of Postconcussive Working Memory Decline: A Feasibility Study
by Yung-Chieh Chen, Yung-Li Chen, Duen-Pang Kuo, Yi-Tien Li, Yung-Hsiao Chiang, Jyh-Jong Chang, Sung-Hui Tseng and Cheng-Yu Chen
J. Pers. Med. 2022, 12(2), 196; https://doi.org/10.3390/jpm12020196 - 31 Jan 2022
Cited by 3 | Viewed by 2433
Abstract
Concussion, also known as mild traumatic brain injury (mTBI), commonly causes transient neurocognitive symptoms, but in some cases, it causes cognitive impairment, including working memory (WM) deficit, which can be long-lasting and impede a patient’s return to work. The predictors of long-term cognitive [...] Read more.
Concussion, also known as mild traumatic brain injury (mTBI), commonly causes transient neurocognitive symptoms, but in some cases, it causes cognitive impairment, including working memory (WM) deficit, which can be long-lasting and impede a patient’s return to work. The predictors of long-term cognitive outcomes following mTBI remain unclear, because abnormality is often absent in structural imaging findings. Previous studies have demonstrated that WM functional activity estimated from functional magnetic resonance imaging (fMRI) has a high sensitivity to postconcussion WM deficits and may be used to not only evaluate but guide treatment strategies, especially targeting brain areas involved in postconcussion cognitive decline. The purpose of the study was to determine whether machine learning-based models using fMRI biomarkers and demographic or neuropsychological measures at the baseline could effectively predict the 1-year cognitive outcomes of concussion. We conducted a prospective, observational study of patients with mTBI who were compared with demographically matched healthy controls enrolled between September 2015 and August 2020. Baseline assessments were collected within the first week of injury, and follow-ups were conducted at 6 weeks, 3 months, 6 months, and 1 year. Potential demographic, neuropsychological, and fMRI features were selected according to their significance of correlation with the estimated changes in WM ability. The support vector machine classifier was trained using these potential features and estimated changes in WM between the predefined time periods. Patients demonstrated significant cognitive recovery at the third month, followed by worsened performance after 6 months, which persisted until 1 year after a concussion. Approximately half of the patients experienced prolonged cognitive impairment at the 1-year follow up. Satisfactory predictions were achieved for patients whose WM function did not recover at 3 months (accuracy = 87.5%), 6 months (accuracy = 83.3%), and 1 year (accuracy = 83.3%) and performed worse at the 1-year follow-up compared to the baseline assessment (accuracy = 83.3%). This study demonstrated the feasibility of personalized prediction for long-term postconcussive WM outcomes based on baseline fMRI and demographic features, opening a new avenue for early rehabilitation intervention in selected individuals with possible poor long-term cognitive outcomes. Full article
(This article belongs to the Special Issue Imaging Biomarkers for Stratified Medicine and Personalised Healthcare)
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14 pages, 962 KiB  
Article
Optimized Prediction Models from Fundus Imaging and Genetics for Late Age-Related Macular Degeneration
by Arun Govindaiah, Abdul Baten, R. Theodore Smith, Siva Balasubramanian and Alauddin Bhuiyan
J. Pers. Med. 2021, 11(11), 1127; https://doi.org/10.3390/jpm11111127 - 1 Nov 2021
Cited by 2 | Viewed by 1504
Abstract
Age-related macular degeneration (AMD) is a leading cause of blindness in the developed world. In this study, we compare the performance of retinal fundus images and genetic-information-based machine learning models for the prediction of late AMD. Using data from the Age-related Eye Disease [...] Read more.
Age-related macular degeneration (AMD) is a leading cause of blindness in the developed world. In this study, we compare the performance of retinal fundus images and genetic-information-based machine learning models for the prediction of late AMD. Using data from the Age-related Eye Disease Study, we built machine learning models with various combinations of genetic, socio-demographic/clinical, and retinal image data to predict late AMD using its severity and category in a single visit, in 2, 5, and 10 years. We compared their performance in sensitivity, specificity, accuracy, and unweighted kappa. The 2-year model based on retinal image and socio-demographic (S-D) parameters achieved a sensitivity of 91.34%, specificity of 84.49% while the same for genetic and S-D-parameters-based model was 79.79% and 66.84%. For the 5-year model, the retinal image and S-D-parameters-based model also outperformed the genetic and S-D parameters-based model. The two 10-year models achieved similar sensitivities of 74.24% and 75.79%, respectively, but the retinal image and S-D-parameters-based model was otherwise superior. The retinal-image-based models were not further improved by adding genetic data. Retinal imaging and S-D data can build an excellent machine learning predictor of developing late AMD over 2–5 years; the retinal imaging model appears to be the preferred prognostic tool for efficient patient management. Full article
(This article belongs to the Special Issue Imaging Biomarkers for Stratified Medicine and Personalised Healthcare)
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20 pages, 1881 KiB  
Article
Effective Connectivity between Major Nodes of the Limbic System, Salience and Frontoparietal Networks Differentiates Schizophrenia and Mood Disorders from Healthy Controls
by Sevdalina Kandilarova, Drozdstoy St. Stoyanov, Rositsa Paunova, Anna Todeva-Radneva, Katrin Aryutova and Michael Maes
J. Pers. Med. 2021, 11(11), 1110; https://doi.org/10.3390/jpm11111110 - 28 Oct 2021
Cited by 12 | Viewed by 2698
Abstract
This study was conducted to examine whether there are quantitative or qualitative differences in the connectome between psychiatric patients and healthy controls and to delineate the connectome features of major depressive disorder (MDD), schizophrenia (SCZ) and bipolar disorder (BD), as well as the [...] Read more.
This study was conducted to examine whether there are quantitative or qualitative differences in the connectome between psychiatric patients and healthy controls and to delineate the connectome features of major depressive disorder (MDD), schizophrenia (SCZ) and bipolar disorder (BD), as well as the severity of these disorders. Toward this end, we performed an effective connectivity analysis of resting state functional MRI data in these three patient groups and healthy controls. We used spectral Dynamic Causal Modeling (spDCM), and the derived connectome features were further subjected to machine learning. The results outlined a model of five connections, which discriminated patients from controls, comprising major nodes of the limbic system (amygdala (AMY), hippocampus (HPC) and anterior cingulate cortex (ACC)), the salience network (anterior insula (AI), and the frontoparietal and dorsal attention network (middle frontal gyrus (MFG), corresponding to the dorsolateral prefrontal cortex, and frontal eye field (FEF)). Notably, the alterations in the self-inhibitory connection of the anterior insula emerged as a feature of both mood disorders and SCZ. Moreover, four out of the five connectome features that discriminate mental illness from controls are features of mood disorders (both MDD and BD), namely the MFG→FEF, HPC→FEF, AI→AMY, and MFG→AMY connections, whereas one connection is a feature of SCZ, namely the AMY→SPL connectivity. A large part of the variance in the severity of depression (31.6%) and SCZ (40.6%) was explained by connectivity features. In conclusion, dysfunctions in the self-regulation of the salience network may underpin major mental disorders, while other key connectome features shape differences between mood disorders and SCZ, and can be used as potential imaging biomarkers. Full article
(This article belongs to the Special Issue Imaging Biomarkers for Stratified Medicine and Personalised Healthcare)
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13 pages, 1189 KiB  
Article
Concurrent Chemoradiation in Anal Cancer Patients Delivered with Bone Marrow-Sparing IMRT: Final Results of a Prospective Phase II Trial
by Francesca Arcadipane, Patrick Silvetti, Francesco Olivero, Alessio Gastino, Roberta Carlevato, Ilaria Chiovatero, Lavinia Spinelli, Massimiliano Mistrangelo, Paola Cassoni, Giuliana Ritorto, Elena Gallio, Adriana Lesca, Riccardo Faletti, Francesca Romana Giglioli, Christian Fiandra, Umberto Ricardi and Pierfrancesco Franco
J. Pers. Med. 2021, 11(5), 427; https://doi.org/10.3390/jpm11050427 - 18 May 2021
Cited by 7 | Viewed by 1773
Abstract
We investigated the role of the selective avoidance of haematopoietically active pelvic bone marrow (BM), with a targeted intensity-modulated radiotherapy (IMRT) approach, to reduce acute hematologic toxicity (HT) in anal cancer patients undergoing concurrent chemo-radiation. We designed a one-armed two-stage Simon’s design study [...] Read more.
We investigated the role of the selective avoidance of haematopoietically active pelvic bone marrow (BM), with a targeted intensity-modulated radiotherapy (IMRT) approach, to reduce acute hematologic toxicity (HT) in anal cancer patients undergoing concurrent chemo-radiation. We designed a one-armed two-stage Simon’s design study to test the hypothesis that BM-sparing IMRT would improve by 20% the rate of G0–G2 (vs. G3–G4) HT, from 42% of RTOG 0529 historical data to 62% (α = 0.05; β = 0.20). A minimum of 21/39 (54%) with G0–G2 toxicity represented the threshold for the fulfilment of the criteria to define this approach as ‘promising’. We employed 18FDG-PET to identify active BM within the pelvis. Acute HT was assessed via weekly blood counts and scored as per the Common Toxicity Criteria for Adverse Effects version 4.0. From December 2017 to October 2020, we enrolled 39 patients. Maximum observed acute HT comprised 20% rate of ≥G3 leukopenia and 11% rate of ≥G3 thrombocytopenia. Overall, 11 out of 39 treated patients (28%) experienced ≥G3 acute HT. Conversely, in 28 patients (72%) G0–G2 HT events were observed, above the threshold set. Hence, 18FDG-PET-guided BM-sparing IMRT was able to reduce acute HT in this clinical setting. Full article
(This article belongs to the Special Issue Imaging Biomarkers for Stratified Medicine and Personalised Healthcare)
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