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Radiomics in Oncology 3rd Edition
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Radiomics in Oncology 3rd Edition

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 10185

Special Issue Editor

Dr. Damiano Caruso

E-Mail Website
Guest Editor
Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sant’Andrea University Hospital, Sapienza-University of Rome, 00100 Rome, Italy
Interests: imaging; oncology; CT; MRI; artificial intelligence; radiomics; response to therapy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of oncologic personalized medicine, radiomics represents an emerging diagnostic tool to support clinicians in decision making, cancer detection, and treatment response assessment. Radiomics by the extraction of several quantitative features, including tumor shape and textural parameters, could provide additional information on cancer phenotype and the tumor microenvironment. Digitally coded medical images that include information related to tumor heterogeneity are transformed in quantitative and dimensional data. Radiomics-derived data, if combined with other clinical data and correlated with outcome, could support physicians in making an accurate and structured evidence-based clinical decision.

In that scenario, radiologists have the means to stratify patients at diagnosis according to tumor aggressiveness and to predict or assess the treatment response in neuro-oncology, lung cancer, gastrointestinal and hepatobiliary tumors, as well as gynecological and genitourinary cancers. Radiomics has the main advantage for physicians that it could be an additional and integrated tool in patient management workflow.

Welcome to the era of bright data!

Dr. Damiano Caruso
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. Diagnostics 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 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

  • radiomics
  • oncology
  • artificial intelligence
  • precision medicine
  • texture analysis
  • imaging

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Related Special Issues

Published Papers (5 papers)

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Editorial

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2 pages, 160 KiB  
Editorial
Radiomics in Oncology III
by Marta Zerunian, Andrea Laghi and Damiano Caruso
Diagnostics 2023, 13(1), 149; https://doi.org/10.3390/diagnostics13010149 - 1 Jan 2023
Viewed by 1452
Abstract
In recent years, radiomics has been among the most impactful topics in the research field of quantitative imaging [...] Full article
(This article belongs to the Special Issue Radiomics in Oncology 3rd Edition)

Research

Jump to: Editorial, Review, Other

19 pages, 2557 KiB  
Article
Applicability of the CT Radiomics of Skeletal Muscle and Machine Learning for the Detection of Sarcopenia and Prognostic Assessment of Disease Progression in Patients with Gastric and Esophageal Tumors
by Daniel Vogele, Teresa Mueller, Daniel Wolf, Stephanie Otto, Sabitha Manoj, Michael Goetz, Thomas J. Ettrich and Meinrad Beer
Diagnostics 2024, 14(2), 198; https://doi.org/10.3390/diagnostics14020198 - 16 Jan 2024
Cited by 1 | Viewed by 2019
Abstract
Purpose: Sarcopenia is considered a negative prognostic factor in patients with malignant tumors. Among other diagnostic options, computed tomography (CT), which is repeatedly performed on tumor patients, can be of further benefit. The present study aims to establish a framework for classifying the [...] Read more.
Purpose: Sarcopenia is considered a negative prognostic factor in patients with malignant tumors. Among other diagnostic options, computed tomography (CT), which is repeatedly performed on tumor patients, can be of further benefit. The present study aims to establish a framework for classifying the impact of sarcopenia on the prognosis of patients diagnosed with esophageal or gastric cancer. Additionally, it explores the significance of CT radiomics in both diagnostic and prognostic methodologies. Materials and Methods: CT scans of 83 patients with esophageal or gastric cancer taken at the time of diagnosis and during a follow-up period of one year were evaluated retrospectively. A total of 330 CT scans were analyzed. Seventy three of these patients received operative tumor resection after neoadjuvant chemotherapy, and 74% of the patients were male. The mean age was 64 years (31–83 years). Three time points (t) were defined as a basis for the statistical analysis in order to structure the course of the disease: t1 = initial diagnosis, t2 = following (neoadjuvant) chemotherapy and t3 = end of the first year after surgery in the “surgery” group or end of the first year after chemotherapy. Sarcopenia was determined using the psoas muscle index (PMI). The additional analysis included the analysis of selected radiomic features of the psoas major, quadratus lumborum, and erector spinae muscles at the L3 level. Disease progression was monitored according to the response evaluation criteria in solid tumors (RECIST 1.1). CT scans and radiomics were used to assess the likelihood of tumor progression and their correlation to sarcopenia. For machine learning, the established algorithms decision tree (DT), K-nearest neighbor (KNN), and random forest (RF) were applied. To evaluate the performance of each model, a 10-fold cross-validation as well as a calculation of Accuracy and Area Under the Curve (AUC) was used. Results: During the observation period of the study, there was a significant decrease in PMI. This was most evident in patients with surgical therapy in the comparison between diagnosis and after both neoadjuvant therapy and surgery (each p < 0.001). Tumor progression (PD) was not observed significantly more often in the patients with sarcopenia compared to those without sarcopenia at any time point (p = 0.277 to p = 0.465). On average, PD occurred after 271.69 ± 104.20 days. The time from initial diagnosis to PD in patients “with sarcopenia” was not significantly shorter than in patients “without sarcopenia” at any of the time points (p = 0.521 to p = 0.817). The CT radiomics of skeletal muscle could predict both sarcopenia and tumor progression, with the best results for the psoas major muscle using the RF algorithm. For the detection of sarcopenia, the Accuracy was 0.90 ± 0.03 and AUC was 0.96 ± 0.02. For the prediction of PD, the Accuracy was 0.88 ± 0.04 and the AUC was 0.93 ± 0.04. Conclusions: In the present study, the CT radiomics of skeletal muscle together with machine learning correlated with the presence of sarcopenia, and this can additionally assist in predicting disease progression. These features can be classified as promising alternatives to conventional methods, with great potential for further research and future clinical application. However, when sarcopenia was diagnosed with PMI, no significant correlation between sarcopenia and PD could be observed. Full article
(This article belongs to the Special Issue Radiomics in Oncology 3rd Edition)
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14 pages, 3152 KiB  
Article
Computed Tomography-Based Radiomics Using Tumor and Vessel Features to Assess Resectability in Cancer of the Pancreatic Head
by Geke Litjens, Joris P. E. A. Broekmans, Tim Boers, Marco Caballo, Maud H. F. van den Hurk, Dilek Ozdemir, Caroline J. van Schaik, Markus H. A. Janse, Erwin J. M. van Geenen, Cees J. H. M. van Laarhoven, Mathias Prokop, Peter H. N. de With, Fons van der Sommen and John J. Hermans
Diagnostics 2023, 13(20), 3198; https://doi.org/10.3390/diagnostics13203198 - 13 Oct 2023
Cited by 3 | Viewed by 1523
Abstract
The preoperative prediction of resectability pancreatic ductal adenocarcinoma (PDAC) is challenging. This retrospective single-center study examined tumor and vessel radiomics to predict the resectability of PDAC in chemo-naïve patients. The tumor and adjacent arteries and veins were segmented in the portal-venous phase of [...] Read more.
The preoperative prediction of resectability pancreatic ductal adenocarcinoma (PDAC) is challenging. This retrospective single-center study examined tumor and vessel radiomics to predict the resectability of PDAC in chemo-naïve patients. The tumor and adjacent arteries and veins were segmented in the portal-venous phase of contrast-enhanced CT scans, and radiomic features were extracted. Features were selected via stability and collinearity testing, and least absolute shrinkage and selection operator application (LASSO). Three models, using tumor features, vessel features, and a combination of both, were trained with the training set (N = 86) to predict resectability. The results were validated with the test set (N = 15) and compared to the multidisciplinary team’s (MDT) performance. The vessel-features-only model performed best, with an AUC of 0.92 and sensitivity and specificity of 97% and 73%, respectively. Test set validation showed a sensitivity and specificity of 100% and 88%, respectively. The combined model was as good as the vessel model (AUC = 0.91), whereas the tumor model showed poor performance (AUC = 0.76). The MDT’s prediction reached a sensitivity and specificity of 97% and 84% for the training set and 88% and 100% for the test set, respectively. Our clinician-independent vessel-based radiomics model can aid in predicting resectability and shows performance comparable to that of the MDT. With these encouraging results, improved, automated, and generalizable models can be developed that reduce workload and can be applied in non-expert hospitals. Full article
(This article belongs to the Special Issue Radiomics in Oncology 3rd Edition)
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Review

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11 pages, 262 KiB  
Review
Image Guided Radiotherapy (IGRT) and Delta (Δ) Radiomics—An Urgent Alliance for the Front Line of the War against Head and Neck Cancers
by Camil Ciprian Mireștean, Roxana Irina Iancu and Dragoș Petru Teodor Iancu
Diagnostics 2023, 13(12), 2045; https://doi.org/10.3390/diagnostics13122045 - 13 Jun 2023
Cited by 3 | Viewed by 2106
Abstract
The identification of a biomarker that is response predictive could offer a solution for the stratification of the treatment of head and neck cancers (HNC) in the context of high recurrence rates, especially those associated with loco-regional failure. Delta (Δ) radiomics, a concept [...] Read more.
The identification of a biomarker that is response predictive could offer a solution for the stratification of the treatment of head and neck cancers (HNC) in the context of high recurrence rates, especially those associated with loco-regional failure. Delta (Δ) radiomics, a concept based on the variation of parameters extracted from medical imaging using artificial intelligence (AI) algorithms, demonstrates its potential as a predictive biomarker of treatment response in HNC. The concept of image-guided radiotherapy (IGRT), including computer tomography simulation (CT) and position control imaging with cone-beam-computed tomography (CBCT), now offers new perspectives for radiomics applied in radiotherapy. The use of Δ features of texture, shape, and size, both from the primary tumor and from the tumor-involved lymph nodes, demonstrates the best predictive accuracy. If, in the case of treatment response, promising Δ radiomics results could be obtained, even after 24 h from the start of treatment, for radiation-induced xerostomia, the evaluation of Δ radiomics in the middle of treatment could be recommended. The fused models (clinical and Δ radiomics) seem to offer benefits, both in comparison to the clinical model and to the radiomic model. The selection of patients who benefit from induction chemotherapy is underestimated in Δ radiomic studies and may be an unexplored territory with major potential. The advantage offered by “in house” simulation CT and CBCT favors the rapid implementation of Δ radiomics studies in radiotherapy departments. Positron emission tomography (PET)-CT Δ radiomics could guide the new concepts of dose escalation on radio-resistant sub-volumes based on radiobiological criteria, but also guide the “next level” of HNC adaptive radiotherapy (ART). Full article
(This article belongs to the Special Issue Radiomics in Oncology 3rd Edition)

Other

4 pages, 1290 KiB  
Interesting Images
Encapsulated Papillary Carcinoma: A Rare Case Report and Its Imaging Features
by Noorzuliana Ahmad, Arasaratnam A. Shantini, Iqbal Hussain Rizuana and Muhammad Rohaizak
Diagnostics 2022, 12(9), 2098; https://doi.org/10.3390/diagnostics12092098 - 30 Aug 2022
Cited by 1 | Viewed by 2283
Abstract
Papillary lesions in the breasts are uncommon and have a wide range of pathologies. Due to diverse non-specific findings radiologically and histologically, papillary neoplasms are always a challenge to radiologists. Encapsulated papillary carcinomas (EPCs) of the breast, also known as intracystic papillary carcinomas, [...] Read more.
Papillary lesions in the breasts are uncommon and have a wide range of pathologies. Due to diverse non-specific findings radiologically and histologically, papillary neoplasms are always a challenge to radiologists. Encapsulated papillary carcinomas (EPCs) of the breast, also known as intracystic papillary carcinomas, are a subgroup of intraductal papillary lesions of the breast. We present a case of a painless right breast lump with the aim to describe a rare encapsulated papillary carcinoma and its imaging features. Full article
(This article belongs to the Special Issue Radiomics in Oncology 3rd Edition)
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