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Radiomics in Precision Medicine
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Radiomics in Precision Medicine

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Omics/Informatics".

Deadline for manuscript submissions: closed (1 July 2023) | Viewed by 6177

Special Issue Editor

Dr. Kenneth S. Hettie

E-Mail Website
Guest Editor
Molecular Imaging Program at Stanford (MIPS), Departments of Radiology (Interventional) and Otolaryngology - Head/Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
Interests: molecular imaging; nanoparticles; drug delivery; immunotherapy; glioblastoma; affi-/nanobody; bio-/molecular probes and sensors; fluorescence-guided surgery; near-infrared I/II fluorescence; image-guided surgery; surgical navigational technology; directed enzyme prodrug therapy (DEPT)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The reliance on using multimodal medical imaging techniques (e.g., PET, CT, MRI) for the visual identification and observation-based interpretation/diagnosis of clinical features has resulted in the production of a profound volume of qualitative data. Radiomics, which is the field of research that can be defined as the high-throughput extraction of high-dimensional quantitative data from features found in concatenated medical images followed by their subsequent conversion into appropriate data for mining and deep learning, emerged in 2012 and has progressed at an accelerated rate such that it has recently afforded the development of predictive models for personalized medical management. As such, there is a pressing need to develop computational and biostatistical methods that are capable of decoding or correlating the vast amounts of biological features with underlying molecular characteristics so as to allow for accurate diagnoses and proper therapeutic treatment.  

This Special Issue of the Journal of Personalized Medicine aims to highlight the progress and current state of radiomic approaches that have been recently developed for or afforded personalized medical management. In the broadest sense, all studies oriented towards basic, preclinical, or clinical applications that fall within this wide purview are welcomed.

All research articles, reviews, and case reports are invited.

Dr. Kenneth S. Hettie
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

  • artificial intelligence (AI)
  • deep learning
  • medical image analysis
  • precision medicine
  • oncology
  • radiomics

Published Papers (3 papers)

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Research

14 pages, 1231 KiB  
Article
Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches
by Khuram Faraz, Grégoire Dauce, Amine Bouhamama, Benjamin Leporq, Hajime Sasaki, Yoshitaka Bito, Olivier Beuf and Frank Pilleul
J. Pers. Med. 2023, 13(7), 1062; https://doi.org/10.3390/jpm13071062 - 28 Jun 2023
Cited by 1 | Viewed by 1130
Abstract
Determining histological subtypes, such as invasive ductal and invasive lobular carcinomas (IDCs and ILCs) and immunohistochemical markers, such as estrogen response (ER), progesterone response (PR), and the HER2 protein status is important in planning breast cancer treatment. MRI-based radiomic analysis is emerging as [...] Read more.
Determining histological subtypes, such as invasive ductal and invasive lobular carcinomas (IDCs and ILCs) and immunohistochemical markers, such as estrogen response (ER), progesterone response (PR), and the HER2 protein status is important in planning breast cancer treatment. MRI-based radiomic analysis is emerging as a non-invasive substitute for biopsy to determine these signatures. We explore the effectiveness of radiomics-based and CNN (convolutional neural network)-based classification models to this end. T1-weighted dynamic contrast-enhanced, contrast-subtracted T1, and T2-weighted MR images of 429 breast cancer tumors from 323 patients are used. Various combinations of input data and classification schemes are applied for ER+ vs. ER−, PR+ vs. PR−, HER2+ vs. HER2−, and IDC vs. ILC classification tasks. The best results were obtained for the ER+ vs. ER− and IDC vs. ILC classification tasks, with their respective AUCs reaching 0.78 and 0.73 on test data. The results with multi-contrast input data were generally better than the mono-contrast alone. The radiomics and CNN-based approaches generally exhibited comparable results. ER and IDC/ILC classification results were promising. PR and HER2 classifications need further investigation through a larger dataset. Better results by using multi-contrast data might indicate that multi-parametric quantitative MRI could be used to achieve more reliable classifiers. Full article
(This article belongs to the Special Issue Radiomics in Precision Medicine)
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16 pages, 7622 KiB  
Article
Machine Learning for Prediction of Recurrence in Parasagittal and Parafalcine Meningiomas: Combined Clinical and MRI Texture Features
by Hsun-Ping Hsieh, Ding-You Wu, Kuo-Chuan Hung, Sher-Wei Lim, Tai-Yuan Chen, Yang Fan-Chiang and Ching-Chung Ko
J. Pers. Med. 2022, 12(4), 522; https://doi.org/10.3390/jpm12040522 - 24 Mar 2022
Cited by 4 | Viewed by 2717
Abstract
A subset of parasagittal and parafalcine (PSPF) meningiomas may show early progression/recurrence (P/R) after surgery. This study applied machine learning using combined clinical and texture features to predict P/R in PSPF meningiomas. A total of 57 consecutive patients with pathologically confirmed (WHO grade [...] Read more.
A subset of parasagittal and parafalcine (PSPF) meningiomas may show early progression/recurrence (P/R) after surgery. This study applied machine learning using combined clinical and texture features to predict P/R in PSPF meningiomas. A total of 57 consecutive patients with pathologically confirmed (WHO grade I) PSPF meningiomas treated in our institution between January 2007 to January 2019 were included. All included patients had complete preoperative magnetic resonance imaging (MRI) and more than one year MRI follow-up after surgery. Preoperative contrast-enhanced T1WI, T2WI, T1WI, and T2 fluid-attenuated inversion recovery (FLAIR) were analyzed retrospectively. The most significant 12 clinical features (extracted by LightGBM) and 73 texture features (extracted by SVM) were combined in random forest to predict P/R, and personalized radiomic scores were calculated. Thirteen patients (13/57, 22.8%) had P/R after surgery. The radiomic score was a high-risk factor for P/R with hazard ratio of 15.73 (p < 0.05) in multivariate hazards analysis. In receiver operating characteristic (ROC) analysis, an AUC of 0.91 with cut-off value of 0.269 was observed in radiomic scores for predicting P/R. Subtotal resection, low apparent diffusion coefficient (ADC) values, and high radiomic scores were associated with shorter progression-free survival (p < 0.05). Among different data input, machine learning using combined clinical and texture features showed the best predictive performance, with an accuracy of 91%, precision of 85%, and AUC of 0.88. Machine learning using combined clinical and texture features may have the potential to predict recurrence in PSPF meningiomas. Full article
(This article belongs to the Special Issue Radiomics in Precision Medicine)
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18 pages, 7536 KiB  
Article
Multivariate Analysis of Associations between Patellofemoral Instability and Gluteal Muscle Contracture: A Radiological Analysis
by Qihang Su, Yi Zhang, Yuanzhen Zhang, Jie Li, Chao Xue, Hengan Ge and Biao Cheng
J. Pers. Med. 2022, 12(2), 242; https://doi.org/10.3390/jpm12020242 - 08 Feb 2022
Cited by 2 | Viewed by 1582
Abstract
The purpose of this study was to investigate the associations between gluteal muscle contracture (GMC) severity and patellofemoral instability and evaluate the reliability of novel indicators by multivariate analysis. Clinical and imaging data from 115 patients with GMC were collected for retrospective analysis. [...] Read more.
The purpose of this study was to investigate the associations between gluteal muscle contracture (GMC) severity and patellofemoral instability and evaluate the reliability of novel indicators by multivariate analysis. Clinical and imaging data from 115 patients with GMC were collected for retrospective analysis. Two novel indicators were used to evaluate GMC severity (knee flexion angle and hip flexion angle, feet distance), and two additional novel parameters were used to reflect patellofemoral instability [patellar displacement vector (L, α), patella-femoral trochlear (P-FT) area, and femoral-trochlear-patella (FT-P) area]. In this study, patients with moderate contracture were dominant, and 35.65% also experienced anterior knee pain after physical activity. Ordered logistic regression analysis indicated that a more serious GMC represented a higher risk of lateral tilt and lateral displacement of the patella. Multivariate analysis showed that feet distance was a reliable indicator for evaluating the severity of GMC. The results showed that the more serious the GMC, the more significant the difference between the P-FT area and the FT-P area of the patellofemoral joint space. L, patellar tilt angle, patellar congruency angle, and lateral patellofemoral angle were independent risk factors for this difference. A more serious GMC represents a higher risk of patellar subluxation. Full article
(This article belongs to the Special Issue Radiomics in Precision Medicine)
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