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PET and MRI Radiomics in Cancer Predictive Modeling

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A special issue of Cancers (ISSN 2072-6694).

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 22021

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Special Issue Editors

Dr. Mathieu Hatt

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Guest Editor

Université de Brest (UBO), Brest, France
Interests: radiomics; machine/deep learning; image processing and analysis

Prof. Catherine Cheze Le Rest

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Guest Editor

Centre Hospitalier Universitaire de Poitiers, Poitiers, France
Interests: PET radiomics; nuclear medicine; cancer applications

Dr. Ulrike Schick

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Guest Editor

Centre Hospitalier Universitaire de Brest, Brest, France
Interests: PET and MRI radiomics; radiotherapy; cancer applications

Dr. Thomas C Booth

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Guest Editor

King's College Hospital NHS Foundation Trust, London, UK
Interests: PET and MRI radiomics, radiology, neuroradiology applications

Special Issue Information

Dear Colleagues,

Today, radiomics has become an important part of cancer research, and imaging is obviously a crucial tool for the management of cancer patients. For the present Special Issue, we wish to focus on the contributions of radiomics and machine (deep) learning developments dedicated to the use of PET images and MRI multimodal-sequence-derived modeling (obtained either in sequential/simultaneous PET/MRI integrated scanners or separated machines) for cancer applications, including diagnosis (e.g., virtual biopsy, staging), prognosis (e.g., risk stratification, survival analysis), therapy response assessment and predictive modeling (e.g., response to therapy prediction relying on before/during/after imaging), and relationships or combinations with other -omics (e.g., radiogenomics). We call for papers dedicated to the development of methods for image analysis and processing, integration of machine/deep learning developments within the radiomics framework, and clinically-relevant studies in patient cohorts regarding various endpoints for all types of cancer for which PET and/or MRI images play a significant role (e.g., brain, cervical, rectum, head and neck).

Dr. Mathieu Hatt
Prof. Catherine Cheze Le Rest
Dr. Ulrike Schick
Dr. Thomas C Booth
Guest Editors

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. Cancers is an international peer-reviewed open access semimonthly 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 2900 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

PET and MRI
nuclear medicine
radiology
radiomics
machine/deep learning
diagnosis
prognosis
radiogenomics
predictive modelling
cancer

Published Papers (9 papers)

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Research

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18 pages, 4099 KiB

Open AccessArticle

Combining Multi-Shell Diffusion with Conventional MRI Improves Molecular Diagnosis of Diffuse Gliomas with Deep Learning

by Golestan Karami, Riccardo Pascuzzo, Matteo Figini, Cosimo Del Gratta, Hui Zhang and Alberto Bizzi

Cancers 2023, 15(2), 482; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers15020482 - 12 Jan 2023

Cited by 3 | Viewed by 1745

Abstract

The WHO classification since 2016 confirms the importance of integrating molecular diagnosis for prognosis and treatment decisions of adult-type diffuse gliomas. This motivates the development of non-invasive diagnostic methods, in particular MRI, to predict molecular subtypes of gliomas before surgery. At present, this development has been focused on deep-learning (DL)-based predictive models, mainly with conventional MRI (cMRI), despite recent studies suggesting multi-shell diffusion MRI (dMRI) offers complementary information to cMRI for molecular subtyping. The aim of this work is to evaluate the potential benefit of combining cMRI and multi-shell dMRI in DL-based models. A model implemented with deep residual neural networks was chosen as an illustrative example. Using a dataset of 146 patients with gliomas (from grade 2 to 4), the model was trained and evaluated, with nested cross-validation, on pre-operative cMRI, multi-shell dMRI, and a combination of the two for the following classification tasks: (i) IDH-mutation; (ii) 1p/19q-codeletion; and (iii) three molecular subtypes according to WHO 2021. The results from a subset of 100 patients with lower grades gliomas (2 and 3 according to WHO 2016) demonstrated that combining cMRI and multi-shell dMRI enabled the best performance in predicting IDH mutation and 1p/19q codeletion, achieving an accuracy of 75 ± 9% in predicting the IDH-mutation status, higher than using cMRI and multi-shell dMRI separately (both 70 ± 7%). Similar findings were observed for predicting the 1p/19q-codeletion status, with the accuracy from combining cMRI and multi-shell dMRI (72 ± 4%) higher than from each modality used alone (cMRI: 65 ± 6%; multi-shell dMRI: 66 ± 9%). These findings remain when we considered all 146 patients for predicting the IDH status (combined: 81 ± 5% accuracy; cMRI: 74 ± 5%; multi-shell dMRI: 73 ± 6%) and for the diagnosis of the three molecular subtypes according to WHO 2021 (combined: 60 ± 5%; cMRI: 57 ± 8%; multi-shell dMRI: 56 ± 7%). Together, these findings suggest that combining cMRI and multi-shell dMRI can offer higher accuracy than using each modality alone for predicting the IDH and 1p/19q status and in diagnosing the three molecular subtypes with DL-based models. Full article

(This article belongs to the Special Issue PET and MRI Radiomics in Cancer Predictive Modeling)

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16 pages, 874 KiB

Open AccessArticle

Development of Radiomic-Based Model to Predict Clinical Outcomes in Non-Small Cell Lung Cancer Patients Treated with Immunotherapy

by Olena Tankyevych, Flora Trousset, Claire Latappy, Moran Berraho, Julien Dutilh, Jean Pierre Tasu, Corinne Lamour and Catherine Cheze Le Rest

Cancers 2022, 14(23), 5931; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers14235931 - 30 Nov 2022

Cited by 7 | Viewed by 1503

Abstract

Purpose: We aimed to assess the ability of radiomics features extracted from baseline (PET/CT0) and follow-up PET/CT scans, as well as their evolution (delta-radiomics), to predict clinical outcome (durable clinical benefit (DCB), progression, response to therapy, OS and PFS) in non-small cell lung cancer (NSCLC) patients treated with immunotherapy. Methods: 83 NSCLC patients treated with immunotherapy who underwent a baseline PET/CT were retrospectively included. Response was assessed at 6–8 weeks (PET/CT1) using PERCIST criteria and at 3 months with iPERCIST (PET/CT2) or RECIST 1.1 criteria using CT. The predictive performance of clinical parameters (CP), standard PET metrics (SUV, Metabolic Tumor volume, Total Lesion Glycolysis), delta-radiomics and PET and CT radiomics features extracted at baseline and during follow-up were studied. Seven multivariate models with different combinations of CP and radiomics were trained on a subset of patients (75%) using least absolute shrinkage, selection operator (LASSO) and random forest classification with 10-fold cross-validation to predict outcome. Model validation was performed on the remaining patients (25%). Overall and progression-free survival was also performed by Kaplan–Meier survival analysis. Results: Numerous radiomics and delta-radiomics parameters had a high individual predictive value of patient outcome with areas under receiver operating characteristics curves (AUCs) >0.80. Their performance was superior to that of CP and standard PET metrics. Several multivariate models were also promising, especially for the prediction of progression (AUCs of 1 and 0.96 for the training and testing subsets with the PET-CT model (PET/CT0)) or DCB (AUCs of 0.85 and 0.83 with the PET-CT-CP model (PET/CT0)). Conclusions: Delta-radiomics and radiomics features extracted from baseline and follow-up PET/CT images could predict outcome in NSCLC patients treated with immunotherapy and identify patients who would benefit from this new standard. These data reinforce the rationale for the use of advanced image analysis of PET/CT scans to further improve personalized treatment management in advanced NSCLC. Full article

(This article belongs to the Special Issue PET and MRI Radiomics in Cancer Predictive Modeling)

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16 pages, 1455 KiB

Open AccessArticle

Relationship of FDG PET/CT Textural Features with the Tumor Microenvironment and Recurrence Risks in Patients with Advanced Gastric Cancers

by Hyein Ahn, Geum Jong Song, Si-Hyong Jang, Hyun Ju Lee, Moon-Soo Lee, Ji-Hye Lee, Mee-Hye Oh, Geum Cheol Jeong, Sang Mi Lee and Jeong Won Lee

Cancers 2022, 14(16), 3936; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers14163936 - 15 Aug 2022

Cited by 4 | Viewed by 1650

Abstract

The relationship between 2-deoxy-2-[¹⁸F]fluoro-D-glucose (FDG) positron emission tomography/computed tomography (PET/CT) textural features and histopathological findings in gastric cancer has not been fully evaluated. We investigated the relationship between the textural features of primary tumors on FDG PET/CT with histopathological findings and recurrence-free survival (RFS) in patients with advanced gastric cancer (AGC). Fifty-six patients with AGC who underwent FDG PET/CT for staging work-ups were retrospectively enrolled. Conventional parameters and the first- and second-order textural features of AGC were extracted using PET textural analysis. Upon histopathological analysis, along with histopathological classification and staging, the degree of CD4, CD8, and CD163 cell infiltrations and expressions of interleukin-6 and matrix-metalloproteinase-11 (MMP-11) in the primary tumor were assessed. The histopathological classification, Lauren classification, lymph node metastasis, CD8 T lymphocyte and CD163 macrophage infiltrations, and MMP-11 expression were significantly associated with the textural features of AGC. The multivariate survival analysis showed that increased FDG uptake and intra-tumoral metabolic heterogeneity were significantly associated with an increased risk of recurrence after curative surgery. Textural features of AGC on FDG PET/CT showed significant correlations with the inflammatory response in the tumor microenvironment and histopathological features of AGC, and they showed significant prognostic values for predicting RFS. Full article

(This article belongs to the Special Issue PET and MRI Radiomics in Cancer Predictive Modeling)

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14 pages, 1365 KiB

Open AccessArticle

A Radiomics-Based Machine Learning Model for Prediction of Tumor Mutational Burden in Lower-Grade Gliomas

by Luu Ho Thanh Lam, Ngan Thy Chu, Thi-Oanh Tran, Duyen Thi Do and Nguyen Quoc Khanh Le

Cancers 2022, 14(14), 3492; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers14143492 - 18 Jul 2022

Cited by 10 | Viewed by 2130

Abstract

Glioma is a Center Nervous System (CNS) neoplasm that arises from the glial cells. In a new scheme category of the World Health Organization 2016, lower-grade gliomas (LGGs) are grade II and III gliomas. Following the discovery of suppression of negative immune regulation, immunotherapy is a promising effective treatment method for lower-grade glioma patients. However, the therapy is not effective for all types of LGGs, and tumor mutational burden (TMB) has been shown to be a potential biomarker for the susceptibility and prognosis of immunotherapy in lower-grade glioma patients. Hence, predicting TMB benefits brain cancer patients. In this study, we investigated the correlation between MRI (magnetic resonance imaging)-based radiomic features and TMB in LGG by applying machine learning methods. Six machine learning classifiers were examined on the features extracted from the genetic algorithm. Subsequently, a light gradient boosting machine (LightGBM) succeeded in selecting 11 radiomics signatures for TMB classification. Our LightGBM model resulted in high accuracy of 0.7936, and reached a balance between sensitivity and specificity, achieving 0.76 and 0.8107, respectively. To our knowledge, our study represents the best model for classification of TMB in LGG patients at present. Full article

(This article belongs to the Special Issue PET and MRI Radiomics in Cancer Predictive Modeling)

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16 pages, 2189 KiB

Open AccessArticle

Texture Analysis of Fractional Water Content Images Acquired during PET/MRI: Initial Evidence for an Association with Total Lesion Glycolysis, Survival and Gene Mutation Profile in Primary Colorectal Cancer

by Balaji Ganeshan, Kenneth Miles, Asim Afaq, Shonit Punwani, Manuel Rodriguez, Simon Wan, Darren Walls, Luke Hoy, Saif Khan, Raymond Endozo, Robert Shortman, John Hoath, Aman Bhargava, Matthew Hanson, Daren Francis, Tan Arulampalam, Sanjay Dindyal, Shih-Hsin Chen, Tony Ng and Ashley Groves

Cancers 2021, 13(11), 2715; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13112715 - 31 May 2021

Cited by 5 | Viewed by 2859

Abstract

To assess the capability of fractional water content (FWC) texture analysis (TA) to generate biologically relevant information from routine PET/MRI acquisitions for colorectal cancer (CRC) patients. Thirty consecutive primary CRC patients (mean age 63.9, range 42–83 years) prospectively underwent FDG-PET/MRI. FWC tumor parametric images generated from Dixon MR sequences underwent TA using commercially available research software (TexRAD). Data analysis comprised (1) identification of functional imaging correlates for texture features (TF) with low inter-observer variability (intraclass correlation coefficient: ICC > 0.75), (2) evaluation of prognostic performance for FWC-TF, and (3) correlation of prognostic imaging signatures with gene mutation (GM) profile. Of 32 FWC-TF with ICC > 0.75, 18 correlated with total lesion glycolysis (TLG, highest: r_s = −0.547, p = 0.002). Using optimized cut-off values, five MR FWC-TF identified a good prognostic group with zero mortality (lowest: p = 0.017). For the most statistically significant prognostic marker, favorable prognosis was significantly associated with a higher number of GM per patient (medians: 7 vs. 1.5, p = 0.009). FWC-TA derived from routine PET/MRI Dixon acquisitions shows good inter-operator agreement, generates biological relevant information related to TLG, GM count, and provides prognostic information that can unlock new clinical applications for CRC patients. Full article

(This article belongs to the Special Issue PET and MRI Radiomics in Cancer Predictive Modeling)

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17 pages, 1322 KiB

Open AccessArticle

MRI-Based Radiomics Analysis for the Pretreatment Prediction of Pathologic Complete Tumor Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Multicenter Study

by Renée W. Y. Granzier, Abdalla Ibrahim, Sergey P. Primakov, Sanaz Samiei, Thiemo J. A. van Nijnatten, Maaike de Boer, Esther M. Heuts, Frans-Jan Hulsmans, Avishek Chatterjee, Philippe Lambin, Marc B. I. Lobbes, Henry C. Woodruff and Marjolein L. Smidt

Cancers 2021, 13(10), 2447; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13102447 - 18 May 2021

Cited by 19 | Viewed by 2649

Abstract

This retrospective study investigated the value of pretreatment contrast-enhanced Magnetic Resonance Imaging (MRI)-based radiomics for the prediction of pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients. A total of 292 breast cancer patients, with 320 tumors, who were treated with neo-adjuvant systemic therapy and underwent a pretreatment MRI exam were enrolled. As the data were collected in two different hospitals with five different MRI scanners and varying acquisition protocols, three different strategies to split training and validation datasets were used. Radiomics, clinical, and combined models were developed using random forest classifiers in each strategy. The analysis of radiomics features had no added value in predicting pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients compared with the clinical models, nor did the combined models perform significantly better than the clinical models. Further, the radiomics features selected for the models and their performance differed with and within the different strategies. Due to previous and current work, we tentatively attribute the lack of improvement in clinical models following the addition of radiomics to the effects of variations in acquisition and reconstruction parameters. The lack of reproducibility data (i.e., test-retest or similar) meant that this effect could not be analyzed. These results indicate the need for reproducibility studies to preselect reproducible features in order to properly assess the potential of radiomics. Full article

(This article belongs to the Special Issue PET and MRI Radiomics in Cancer Predictive Modeling)

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15 pages, 3536 KiB

Open AccessArticle

Dedicated Axillary MRI-Based Radiomics Analysis for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer

by Sanaz Samiei, Renée W. Y. Granzier, Abdalla Ibrahim, Sergey Primakov, Marc B. I. Lobbes, Regina G. H. Beets-Tan, Thiemo J. A. van Nijnatten, Sanne M. E. Engelen, Henry C. Woodruff and Marjolein L. Smidt

Cancers 2021, 13(4), 757; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13040757 - 12 Feb 2021

Cited by 19 | Viewed by 2654

Abstract

Radiomics features may contribute to increased diagnostic performance of MRI in the prediction of axillary lymph node metastasis. The objective of the study was to predict preoperative axillary lymph node metastasis in breast cancer using clinical models and radiomics models based on T2-weighted (T2W) dedicated axillary MRI features with node-by-node analysis. From August 2012 until October 2014, all women who had undergone dedicated axillary 3.0T T2W MRI, followed by axillary surgery, were retrospectively identified, and available clinical data were collected. All axillary lymph nodes were manually delineated on the T2W MR images, and quantitative radiomics features were extracted from the delineated regions. Data were partitioned patient-wise to train 100 models using different splits for the training and validation cohorts to account for multiple lymph nodes per patient and class imbalance. Features were selected in the training cohorts using recursive feature elimination with repeated 5-fold cross-validation, followed by the development of random forest models. The performance of the models was assessed using the area under the curve (AUC). A total of 75 women (median age, 61 years; interquartile range, 51–68 years) with 511 axillary lymph nodes were included. On final pathology, 36 (7%) of the lymph nodes had metastasis. A total of 105 original radiomics features were extracted from the T2W MR images. Each cohort split resulted in a different number of lymph nodes in the training cohorts and a different set of selected features. Performance of the 100 clinical and radiomics models showed a wide range of AUC values between 0.41–0.74 and 0.48–0.89 in the training cohorts, respectively, and between 0.30–0.98 and 0.37–0.99 in the validation cohorts, respectively. With these results, it was not possible to obtain a final prediction model. Clinical characteristics and dedicated axillary MRI-based radiomics with node-by-node analysis did not contribute to the prediction of axillary lymph node metastasis in breast cancer based on data where variations in acquisition and reconstruction parameters were not addressed. Full article

(This article belongs to the Special Issue PET and MRI Radiomics in Cancer Predictive Modeling)

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11 pages, 1705 KiB

Open AccessArticle

MRI-Based Radiomics Input for Prediction of 2-Year Disease Recurrence in Anal Squamous Cell Carcinoma

by Nicolas Giraud, Olivier Saut, Thomas Aparicio, Philippe Ronchin, Louis-Arnaud Bazire, Emilie Barbier, Claire Lemanski, Xavier Mirabel, Pierre-Luc Etienne, Astrid Lièvre, Wulfran Cacheux, Ariane Darut-Jouve, Christelle De la Fouchardière, Arnaud Hocquelet, Hervé Trillaud, Thomas Charleux, Gilles Breysacher, Delphine Argo-Leignel, Alexandre Tessier, Nicolas Magné, Meher Ben Abdelghani, Côme Lepage and Véronique Vendrely Show full author list Hide full author list

Cancers 2021, 13(2), 193; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13020193 - 07 Jan 2021

Cited by 7 | Viewed by 2140

Abstract

Purpose: Chemo-radiotherapy (CRT) is the standard treatment for non-metastatic anal squamous cell carcinomas (ASCC). Despite excellent results for T1-2 stages, relapses still occur in around 35% of locally advanced tumors. Recent strategies focus on treatment intensification, but could benefit from a better patient selection. Our goal was to assess the prognostic value of pre-therapeutic MRI radiomics on 2-year disease control (DC). Methods: We retrospectively selected patients with non-metastatic ASCC treated at the CHU Bordeaux and in the French FFCD0904 multicentric trial. Radiomic features were extracted from T2-weighted pre-therapeutic MRI delineated sequences. After random division between training and testing sets on a 2:1 ratio, univariate and multivariate analysis were performed on the training cohort to select optimal features. The correlation with 2-year DC was assessed using logistic regression models, with AUC and accuracy as performance gauges, and the prediction of disease-free survival using Cox regression and Kaplan-Meier analysis. Results: A total of 82 patients were randomized in the training (n = 54) and testing sets (n = 28). At 2 years, 24 patients (29%) presented relapse. In the training set, two clinical (tumor size and CRT length) and two radiomic features (FirstOrder_Entropy and GLCM_JointEnergy) were associated with disease control in univariate analysis and included in the model. The clinical model was outperformed by the mixed (clinical and radiomic) model in both the training (AUC 0.758 versus 0.825, accuracy of 75.9% versus 87%) and testing (AUC 0.714 versus 0.898, accuracy of 78.6% versus 85.7%) sets, which led to distinctive high and low risk of disease relapse groups (HR 8.60, p = 0.005). Conclusion: A mixed model with two clinical and two radiomic features was predictive of 2-year disease control after CRT and could contribute to identify high risk patients amenable to treatment intensification with view of personalized medicine. Full article

(This article belongs to the Special Issue PET and MRI Radiomics in Cancer Predictive Modeling)

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Review

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12 pages, 1061 KiB

Open AccessReview

Quantitative FDG PET Assessment for Oncology Therapy

by Kenji Hirata and Nagara Tamaki

Cancers 2021, 13(4), 869; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13040869 - 19 Feb 2021

Cited by 18 | Viewed by 3368

Abstract

Positron emission tomography (PET) has unique characteristics for quantitative assessment of tumour biology in vivo. Accumulation of F-18 fluorodeoxyglucose (FDG) may reflect tumour characteristics based on its metabolic activity. Quantitative assessment of FDG uptake can often be applied for treatment monitoring after chemotherapy or chemoradiotherapy. Numerous studies indicated biochemical change assessed by FDG PET as a more sensitive marker than morphological change estimated by CT or MRI. In addition, those with complete metabolic response after therapy may show better disease-free survival and overall survival than those with other responses. Assessment of metabolic change may be performed using absolute FDG uptake in the tumour (standardized uptake value: SUV). In addition, volumetric parameters such as metabolic tumour volume (MTV) have been introduced for quantitative assessment of FDG uptake in tumour. More recently, radiomics approaches that focus on image-based precision medicine have been applied to FDG PET, as well as other radiological imaging. Among these, texture analysis extracts intratumoral heterogeneity on a voxel-by-voxel basis. Combined with various machine learning techniques, these new quantitative parameters hold a promise for assessing tissue characterization and predicting treatment effect, and could also be used for future prognosis of various tumours, although multicentre clinical trials are needed before application in clinical settings. Full article

(This article belongs to the Special Issue PET and MRI Radiomics in Cancer Predictive Modeling)

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