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CT/MRI/PET in Cancer
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CT/MRI/PET in Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 4811

Special Issue Editors

Dr. Dávid Sipos

E-Mail Website
Guest Editor
Department of Medical Imaging, Medical School, University of Pécs, 7621 Pécs, Hungary
Interests: nuclear medicine; PET imaging; diagnostics; therapy
Dr. Ritter Zsombor

E-Mail Website
Guest Editor
Department of Medical Imaging, Medical School, University of Pécs, 7621 Pécs, Hungary
Interests: nuclear medicine; PET imaging; diagnostics; therapy
Prof. Dr. Elif Hindié

E-Mail Website
Guest Editor
INCIA, University of Bordeaux, CNRS, EPHE, UMR 5287, 33000 Bordeaux, France
Interests: molecular imaging; theranostics; PET/CT; targeted radionuclide therapy; breast cancer; melanoma; neuroendocrine tumors

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to a Special Issue focused on CT/MRI/PET in cancer, a crucial area in advancing cancer diagnosis, staging, and treatment evaluation. The integration of CT/MRI and PET has revolutionized oncology by providing precise metabolic and anatomical imaging, significantly improving patient outcomes.

This Special Issue aims to explore the latest advancements, applications, and innovations in CT/MRI/PET imaging within oncology. By aligning with the journal’s scope, we seek to provide a platform for sharing high-impact research and comprehensive reviews to deepen the understanding of this transformative technology.

We welcome original research articles and reviews on themes such as

  • Novel CT/MRI/PET imaging techniques and protocols.
  • Applications in tumor characterization, treatment planning, and response monitoring.
  • Quantitative imaging biomarkers and artificial intelligence integration.
  • Insights into clinical and preclinical studies.

Your contributions will play a vital role in advancing knowledge and fostering collaboration in this dynamic field. If the Special Issue achieves at least 10 accepted articles, it will be published as a standalone book for wider dissemination.

We look forward to receiving your valuable work and shaping this exciting collection together.

Sincerely,

Dr. Dávid Sipos
Dr. Ritter Zsombor
Prof. Dr. Elif Hindié
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 communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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 imaging
  • CT
  • MRI
  • cancer
  • characterization
  • treatment monitoring
  • biomarkers
  • predictive value
  • AI integration

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

12 pages, 854 KB  
Article
Longitudinal Bone Density During TSH Suppression in Differentiated Thyroid Cancer: A Paired PET/CT Analysis
by Holger Einspieler, Hannah Klimpfinger, Song Xue, Aleksandar Debeljkovic, Bettina Reiterits, Bengt Hennig, Marcus Hacker and Georgios Karanikas
Cancers 2025, 17(21), 3462; https://doi.org/10.3390/cancers17213462 - 28 Oct 2025
Viewed by 1143
Abstract
Background: While TSH suppression is essential in patients with differentiated thyroid cancer (DTC) to reduce the risk of recurrence, it has also been linked to side effects, particularly a reduction in bone mineral density that may contribute to osteoporosis. However, previous studies [...] Read more.
Background: While TSH suppression is essential in patients with differentiated thyroid cancer (DTC) to reduce the risk of recurrence, it has also been linked to side effects, particularly a reduction in bone mineral density that may contribute to osteoporosis. However, previous studies investigating this association have yielded inconsistent results. This study aimed to evaluate bone density using Hounsfield units from PET/CT scans in a longitudinal analysis including both sexes. Methods: Patients with DTC under continuous TSH suppression who underwent two PET/CT scans were included. Hounsfield units were measured for each lumbar vertebra (L1–L5) in the CT by placing an elliptical region of interest (ROI) in the center of the vertebra, avoiding hyperdense edges. Laboratory parameters were also collected. Results: A total of 50 patients were included in the study (25 male, 25 female), with a mean age of 57.2 (±15.3) years at the time of the first scan. The mean duration of TSH suppression before the first scan was 3.7 ± 3.9 years, and the mean interval between both scans was 4.4 ± 4.0 years. At the follow-up scan, bone density was significantly lower compared with baseline for all lumbar vertebrae (L1–L5 combined and individually) (all p < 0.05). Subgroup analysis revealed a significant decline in women at L1, L2, L4, and L5 and for overall lumbar bone density, while men showed nonsignificant trends. Conclusions: Our study suggests a sustained reduction in vertebral bone density during TSH suppression. The results support routine monitoring in both sexes, risk stratification by age and duration of suppression, and, when oncologically appropriate, consideration of lower suppression intensity or initiation of bone-protective therapy in high-risk patients. Full article
(This article belongs to the Special Issue CT/MRI/PET in Cancer)
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18 pages, 2025 KB  
Article
A Priori Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Using Deep Features from Pre-Treatment MRI and CT
by Deok Hyun Jang, Laurentius O. Osapoetra, Lakshmanan Sannachi, Belinda Curpen, Ana Pejović-Milić and Gregory J. Czarnota
Cancers 2025, 17(20), 3394; https://doi.org/10.3390/cancers17203394 - 21 Oct 2025
Viewed by 1588
Abstract
Background: Response to neoadjuvant chemotherapy (NAC) is a key prognostic indicator in breast cancer, yet current assessment relies on postoperative pathology. This study investigated the use of deep features derived from pre-treatment MRI and CT scans, in conjunction with clinical variables, to [...] Read more.
Background: Response to neoadjuvant chemotherapy (NAC) is a key prognostic indicator in breast cancer, yet current assessment relies on postoperative pathology. This study investigated the use of deep features derived from pre-treatment MRI and CT scans, in conjunction with clinical variables, to predict treatment response a priori. Methods: Two response endpoints were analyzed: pathologic complete response (pCR) versus non-pCR, and responders versus non-responders, with response defined as a reduction in tumor size of at least 30%. Intratumoral and peritumoral segmentations were generated on contrast-enhanced T1-weighted (CE-T1) and T2-weighted MRI, as well as contrast-enhanced CT images of tumors. Deep features were extracted from these regions using ResNet10, ResNet18, ResNet34, and ResNet50 architectures pre-trained with MedicalNet. Handcrafted radiomic features were also extracted for comparison. Feature selection was conducted with minimum redundancy maximum relevance (mRMR) followed by recursive feature elimination (RFE), and classification was performed using XGBoost across ten independent data partitions. Results: A total of 177 patients were analyzed in this study. ResNet34-derived features achieved the highest overall classification performance under both criteria, outperforming handcrafted features and deep features from other ResNet architectures. For distinguishing pCR from non-pCR, ResNet34 achieved a balanced accuracy of 81.6%, whereas handcrafted radiomics achieved 77.9%. For distinguishing responders from non-responders, ResNet34 achieved a balanced accuracy of 73.5%, compared with 70.2% for handcrafted radiomics. Conclusions: Deep features extracted from routinely acquired MRI and CT, when combined with clinical information, improve the prediction of NAC response in breast cancer. This multimodal framework demonstrates the value of deep learning-based approaches as a complement to handcrafted radiomics and provides a basis for more individualized treatment strategies. Full article
(This article belongs to the Special Issue CT/MRI/PET in Cancer)
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20 pages, 1386 KB  
Article
AI-Assistance Body Composition CT at T12 and T4 in Lung Cancer: Diagnosing Sarcopenia, and Its Correlation with Morphofunctional Assessment Techniques
by Maria Zhao Montero-Benitez, Alba Carmona-Llanos, Rocio Fernández-Jiménez, Alicia Román-Jobacho, Jaime Gómez-Millán, Javier Modamio-Molina, Eva Cabrera-Cesar, Isabel Vegas-Aguilar, Maria del Mar Amaya-Campos, Francisco J. Tinahones, Esther Molina-Montes, Manuel Cayón-Blanco and Jose Manuel García-Almeida
Cancers 2025, 17(19), 3255; https://doi.org/10.3390/cancers17193255 - 8 Oct 2025
Cited by 1 | Viewed by 1463
Abstract
Background: Sarcopenia and low muscle mass are prevalent and prognostically relevant in patients with lung cancer, yet their diagnosis remains challenging in routine clinical practice. Opportunistic assessment using computed tomography (CT) has emerged as a valuable tool for body composition evaluation. We aimed [...] Read more.
Background: Sarcopenia and low muscle mass are prevalent and prognostically relevant in patients with lung cancer, yet their diagnosis remains challenging in routine clinical practice. Opportunistic assessment using computed tomography (CT) has emerged as a valuable tool for body composition evaluation. We aimed to assess the utility of thoracic CT at T12 and T4 levels in identifying sarcopenia and low muscle mass and explore their correlation with morphofunctional tools such as bioelectrical impedance vector analysis (BIVA), nutritional ultrasound (NU), and functional performance tests. Methods: In this prospective observational study, 80 patients with lung cancer were evaluated at diagnosis. Body composition was assessed using BIVA-, NU-, and CT-derived parameters at T12 and T4 levels. Functional status was measured using the Timed Up and Go (TUG) and 30-Second Chair Stand Test. Sarcopenia was defined according to EWGSOP2 criteria. Results: Sarcopenia was identified in 20% of patients. CT-derived indices at T12CT demonstrated better diagnostic performance than T4CT. For detecting low muscle mass, the optimal SMI cut-off values were SMI_T12CT < 31.98 cm2/m2 and SMI_T4CT < 59.05 cm2/m2 in men and SMI_T12CT < 28.23 cm2/m2 and SMI_T4CT < 41.69 cm2/m2 in women. For sarcopenia diagnosis, the values were SMI_T12CT < 24.78 cm2/m2 and SMI_T4CT < 57.23 cm2/m2 in men and SMI_T12CT < 21.24 cm2/m2 and SMI_T4CT < 49.35 cm2/m2 in women. A combined model including SMI_T12CT, RF_CSA, and the 30 s squat test showed high diagnostic accuracy (AUC = 0.826). In multivariable analysis, lower SMA_T12CT was independently associated with risk of sarcopenia (OR = 0.96, 95% CI: 0.92–0.99, p = 0.022), as were older age (OR = 1.23, 95% CI: 1.07–1.47, p = 0.010) and fewer repetitions in the 30 s squat test (OR = 0.78, 95% CI: 0.63–0.91, p = 0.007). Conclusions: CT-derived body composition assessment, particularly at the T12 level, shows good correlation with morphofunctional tools and may offer a reliable and timely alternative for identifying sarcopenia and low muscle mass in patients with lung cancer. Full article
(This article belongs to the Special Issue CT/MRI/PET in Cancer)
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