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Cancers
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Recent Advances in Oncology Imaging: 2nd Edition
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Recent Advances in Oncology Imaging: 2nd Edition

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 2026 | Viewed by 4244

Special Issue Editors

Dr. Li Liu

E-Mail Website
Guest Editor
Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX, USA
Interests: preclinical cellular and molecular multimodality imaging, with a particular focus on applications in small animal models of breast, kidney, lung, prostate and bladder cancers
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yicheng Ni

E-Mail Website
Guest Editor
Department of Radiology, Affiliated Zhongda Hospital, Southeast University, Nanjing, China
Interests: imaging-navigated translational theragnostic research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical imaging is playing an ever-increasing role in clinical, experimental, and translational oncology for population surveillance, patient screening, diagnosis assurance, cancer staging, therapeutic evaluation, prognosis assessment, and development of novel anticancer approaches.

Recent advances in oncologic imaging include the following: (1) multimodality (US, CT, MRI, PET, SPECT, optical imaging) and multiparametric (morphological, functional, metabolic) applications; (2) precision medicine driven by molecular imaging and nano-technologies; (3) theragnostics that utilize molecular imaging to identify cancer patient-specific biomarkers to guide individualized treatment decisions; and (4) radiomics assisted by artificial intelligence, big data analytics, and deep learning for multi-feature characterization. This Special Issue will update and highlight the state of the art with respect to cancer imaging.

Dr. Li Liu
Prof. Dr. Yicheng Ni
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 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

  • medical imaging
  • cancer
  • oncology
  • molecular imaging
  • theragnostics

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

Published Papers (4 papers)

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14 pages, 2536 KiB  
Article
Association of Increased CT-Attenuation of Visceral Adipose Tissue After Surgery with Poor Survival Outcomes in Patients with Stage II–III Gastric Cancer: A Retrospective Cohort Study
by Sang Mi Lee, Geum Jong Song, Myoung Won Son, Jong Hyuk Yun, Moon-Soo Lee and Jeong Won Lee
Cancers 2025, 17(2), 235; https://doi.org/10.3390/cancers17020235 - 13 Jan 2025
Viewed by 732
Abstract
Background/Objectives: This study aimed to investigate whether post-operative changes in the computed tomography (CT)-attenuation of subcutaneous (SAT) and visceral (VAT) adipose tissues were significantly associated with recurrence-free survival (RFS), peritoneal RFS, and overall survival (OS) in patients with stage II–III gastric cancer. Methods: [...] Read more.
Background/Objectives: This study aimed to investigate whether post-operative changes in the computed tomography (CT)-attenuation of subcutaneous (SAT) and visceral (VAT) adipose tissues were significantly associated with recurrence-free survival (RFS), peritoneal RFS, and overall survival (OS) in patients with stage II–III gastric cancer. Methods: This retrospective study analyzed 243 patients with stage II–III gastric cancer who underwent curative surgery. CT-attenuation values of SAT (SAT HU) and VAT (VAT HU) were measured from non-contrast-enhanced abdominopelvic CT images taken pre-operatively and 6 months post-operatively. Changes in SAT HU (ΔSAT HU) and VAT HU (ΔVAT HU) between the two CT scans were calculated. The prognostic value of these variables for predicting survival outcomes was assessed. Results: Correlation analyses showed that both ΔSAT HU and ΔVAT HU were significantly positively correlated with T stage, TNM stage, and tumor size (p < 0.05). In the multivariate survival analysis, ΔVAT HU emerged as an independent significant predictor for RFS (p = 0.002, hazard ratio, 2.437), peritoneal RFS (p = 0.023, hazard ratio, 2.457), and OS (p = 0.043, hazard ratio, 2.204) after adjusting for age, sex, histopathological classification, T stage, and N stage. Patients with high ΔVAT HU had worse RFS, peritoneal RFS, and OS compared to those with low ΔVAT HU. Conclusions: Change in CT-attenuation of VAT following surgery was significantly correlated with tumor characteristics and was a significant predictor of RFS, peritoneal RFS, and OS in patients with stage II–III gastric cancer. Full article
(This article belongs to the Special Issue Recent Advances in Oncology Imaging: 2nd Edition)
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16 pages, 2225 KiB  
Article
Multimodal MRI Deep Learning for Predicting Central Lymph Node Metastasis in Papillary Thyroid Cancer
by Xiuyu Wang, Heng Zhang, Hang Fan, Xifeng Yang, Jiansong Fan, Puyeh Wu, Yicheng Ni and Shudong Hu
Cancers 2024, 16(23), 4042; https://doi.org/10.3390/cancers16234042 - 2 Dec 2024
Viewed by 969
Abstract
Background: Central lymph node metastasis (CLNM) in papillary thyroid cancer (PTC) significantly influences surgical decision-making strategies. Objectives: This study aims to develop a predictive model for CLNM in PTC patients using magnetic resonance imaging (MRI) and clinicopathological data. Methods: By incorporating deep learning [...] Read more.
Background: Central lymph node metastasis (CLNM) in papillary thyroid cancer (PTC) significantly influences surgical decision-making strategies. Objectives: This study aims to develop a predictive model for CLNM in PTC patients using magnetic resonance imaging (MRI) and clinicopathological data. Methods: By incorporating deep learning (DL) algorithms, the model seeks to address the challenges in diagnosing CLNM and reduce overtreatment. The results were compared with traditional machine learning (ML) models. In this retrospective study, preoperative MRI data from 105 PTC patients were divided into training and testing sets. A radiologist manually outlined the region of interest (ROI) on MRI images. Three classic ML algorithms (support vector machine [SVM], logistic regression [LR], and random forest [RF]) were employed across different data modalities. Additionally, an AMMCNet utilizing convolutional neural networks (CNNs) was proposed to develop DL models for CLNM. Predictive performance was evaluated using receiver operator characteristic (ROC) curve analysis, and clinical utility was assessed through decision curve analysis (DCA). Results: Lesion diameter was identified as an independent risk factor for CLNM. Among ML models, the RF-(T1WI + T2WI, T1WI + T2WI + Clinical) models achieved the highest area under the curve (AUC) at 0.863. The DL fusion model surpassed all ML fusion models with an AUC of 0.891. Conclusions: A fusion model based on the AMMCNet architecture using MRI images and clinicopathological data was developed, effectively predicting CLNM in PTC patients. Full article
(This article belongs to the Special Issue Recent Advances in Oncology Imaging: 2nd Edition)
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13 pages, 3026 KiB  
Article
Value of Spinal Cord Diffusion Imaging and Tractography in Providing Predictive Factors for Tumor Resection in Patients with Intramedullary Tumors: A Pilot Study
by Corentin Dauleac, Timothée Jacquesson, Carole Frindel, Nathalie André-Obadia, François Ducray, Patrick Mertens and François Cotton
Cancers 2024, 16(16), 2834; https://doi.org/10.3390/cancers16162834 - 13 Aug 2024
Cited by 1 | Viewed by 1393
Abstract
This pilot study aimed to investigate the interest of high angular resolution diffusion imaging (HARDI) and tractography of the spinal cord (SC) in the management of patients with intramedullary tumors by providing predictive elements for tumor resection. Eight patients were included in a [...] Read more.
This pilot study aimed to investigate the interest of high angular resolution diffusion imaging (HARDI) and tractography of the spinal cord (SC) in the management of patients with intramedullary tumors by providing predictive elements for tumor resection. Eight patients were included in a prospective study. HARDI images of the SC were acquired using a 3T MRI scanner with a reduced field of view. Opposed phase-encoding directions allowed distortion corrections. SC fiber tracking was performed using a deterministic approach, with extraction of tensor metrics. Then, regions of interest were drawn to track the spinal pathways of interest. HARDI and tractography added value by providing characteristics about the microstructural organization of the spinal white fibers. In patients with SC tumors, tensor metrics demonstrated significant changes in microstructural architecture, axonal density, and myelinated fibers (all, p < 0.0001) of the spinal white matter. Tractography aided in the differentiation of tumor histological types (SC-invaded vs. pushed back by the tumor), and differentiation of the spinal tracts enabled the determination of precise anatomical relationships between the tumor and the SC, defining the tumor resectability. This study underlines the value of using HARDI and tractography in patients with intramedullary tumors, to show alterations in SC microarchitecture and to differentiate spinal tracts to establish predictive factors for tumor resectability. Full article
(This article belongs to the Special Issue Recent Advances in Oncology Imaging: 2nd Edition)
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15 pages, 1109 KiB  
Systematic Review
Effect of Indocyanine Green-Guided Lymphadenectomy During Gastrectomy on Survival: Individual Patient Data Meta-Analysis
by Matteo Calì, Alberto Aiolfi, Sho Sato, Jawon Hwang, Gianluca Bonitta, Francesca Albanesi, Giulia Bonavina, Marta Cavalli, Giampiero Campanelli, Antonio Biondi, Luigi Bonavina and Davide Bona
Cancers 2025, 17(6), 980; https://doi.org/10.3390/cancers17060980 - 14 Mar 2025
Viewed by 530
Abstract
Background: Indocyanine green-guided (ICG-guided) lymphadenectomy during gastrectomy for cancer has been proposed to enhance the accuracy of lymphadenectomy. The impact of ICG-guided lymphadenectomy on patient survival remains debated. Methods: The findings of the systematic review were reconstructed into an individual patient data (IDP) [...] Read more.
Background: Indocyanine green-guided (ICG-guided) lymphadenectomy during gastrectomy for cancer has been proposed to enhance the accuracy of lymphadenectomy. The impact of ICG-guided lymphadenectomy on patient survival remains debated. Methods: The findings of the systematic review were reconstructed into an individual patient data (IDP) meta-analysis with restricted mean survival time difference (RMSTD). Overall survival (OS) and disease-free (DFS) survival were primary outcomes. RMSTD, standardized mead difference (SMD), and 95% confidence intervals (CI) were used as pooled effect size measures. Results: Three studies (6325 patients) were included; 42% of patients underwent ICG-guided lymphadenectomy. The patients’ age ranged from 47 to 72 years and 58% were males. Proximal, distal, and total gastrectomy were completed in 6.8%, 80.4%, and 12.8% of patients, respectively. The surgical approach was laparoscopic (62.3%) and robotic (37.7%). ICG-guided lymphadenectomy was associated with a higher number of harvested lymph nodes compared to non-ICG-guided lymphadenectomy (SMD 0.50; 95% CI 0.45–0.55). At the 42-month follow-up, OS and DFS estimates for ICG-guided vs. non-ICG-guided lymphadenectomy were 0.5 months (95% CI −0.01, 1.1) and 1.3 months (95% CI 0.39, 2.15), respectively. Conclusions: Our analysis suggests that ICG-guided lymphadenectomy offers equivalent long-term OS and DFS compared to non-ICG-guided lymphadenectomy. Full article
(This article belongs to the Special Issue Recent Advances in Oncology Imaging: 2nd Edition)
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