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Diagnostics
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Radiomics in Oncology II
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Radiomics in Oncology II

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

Deadline for manuscript submissions: closed (30 March 2022) | Viewed by 13878

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

Related Special Issue

Published Papers (5 papers)

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11 pages, 2196 KiB  
Article
The Role of Chest CT Radiomics in Diagnosis of Lung Cancer or Tuberculosis: A Pilot Study
by Lekshmi Thattaamuriyil Padmakumari, Gisella Guido, Damiano Caruso, Ilaria Nacci, Antonella Del Gaudio, Marta Zerunian, Michela Polici, Renuka Gopalakrishnan, Aziz Kallikunnel Sayed Mohamed, Domenico De Santis, Andrea Laghi, Dania Cioni and Emanuele Neri
Diagnostics 2022, 12(3), 739; https://doi.org/10.3390/diagnostics12030739 - 18 Mar 2022
Cited by 9 | Viewed by 1964
Abstract
In many low-income countries, the poor availability of lung biopsy leads to delayed diagnosis of lung cancer (LC), which can appear radiologically similar to tuberculosis (TB). To assess the ability of CT Radiomics in differentiating between TB and LC, and to evaluate the [...] Read more.
In many low-income countries, the poor availability of lung biopsy leads to delayed diagnosis of lung cancer (LC), which can appear radiologically similar to tuberculosis (TB). To assess the ability of CT Radiomics in differentiating between TB and LC, and to evaluate the potential predictive role of clinical parameters, from March 2020 to September 2021, patients with histological diagnosis of TB or LC underwent chest CT evaluation and were retrospectively enrolled. Exclusion criteria were: availability of only enhanced CT scans, previous lung surgery and significant CT motion artefacts. After manual 3D segmentation of enhanced CT, two radiologists, in consensus, extracted and compared radiomics features (T-test or Mann–Whitney), and they tested their performance, in differentiating LC from TB, via Receiver operating characteristic (ROC) curves. Forty patients (28 LC and 12 TB) were finally enrolled, and 31 were male, with a mean age of 59 ± 13 years. Significant differences were found in normal WBC count (p < 0.019) and age (p < 0.001), in favor of the LC group (89% vs. 58%) and with an older population in LC group, respectively. Significant differences were found in 16/107 radiomic features (all p < 0.05). LargeDependenceEmphasis and LargeAreaLowGrayLevelEmphasis showed the best performance in discriminating LC from TB, (AUC: 0.92, sensitivity: 85.7%, specificity: 91.7%, p < 0.0001; AUC: 0.92, sensitivity: 75%, specificity: 100%, p < 0.0001, respectively). Radiomics may be a non-invasive imaging tool in many poor nations, for differentiating LC from TB, with a pivotal role in improving oncological patients’ management; however, future prospective studies will be necessary to validate these initial findings. Full article
(This article belongs to the Special Issue Radiomics in Oncology II)
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21 pages, 3260 KiB  
Article
A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study
by Rossana Castaldo, Nunzia Garbino, Carlo Cavaliere, Mariarosaria Incoronato, Luca Basso, Renato Cuocolo, Leonardo Pace, Marco Salvatore, Monica Franzese and Emanuele Nicolai
Diagnostics 2022, 12(2), 499; https://doi.org/10.3390/diagnostics12020499 - 15 Feb 2022
Cited by 10 | Viewed by 2622
Abstract
Radiomics is rapidly advancing in precision diagnostics and cancer treatment. However, there are several challenges that need to be addressed before translation to clinical use. This study presents an ad-hoc weighted statistical framework to explore radiomic biomarkers for a better characterization of the [...] Read more.
Radiomics is rapidly advancing in precision diagnostics and cancer treatment. However, there are several challenges that need to be addressed before translation to clinical use. This study presents an ad-hoc weighted statistical framework to explore radiomic biomarkers for a better characterization of the radiogenomic phenotypes in breast cancer. Thirty-six female patients with breast cancer were enrolled in this study. Radiomic features were extracted from MRI and PET imaging techniques for malignant and healthy lesions in each patient. To reduce within-subject bias, the ratio of radiomic features extracted from both lesions was calculated for each patient. Radiomic features were further normalized, comparing the z-score, quantile, and whitening normalization methods to reduce between-subjects bias. After feature reduction by Spearman’s correlation, a methodological approach based on a principal component analysis (PCA) was applied. The results were compared and validated on twenty-seven patients to investigate the tumor grade, Ki-67 index, and molecular cancer subtypes using classification methods (LogitBoost, random forest, and linear discriminant analysis). The classification techniques achieved high area-under-the-curve values with one PC that was calculated by normalizing the radiomic features via the quantile method. This pilot study helped us to establish a robust framework of analysis to generate a combined radiomic signature, which may lead to more precise breast cancer prognosis. Full article
(This article belongs to the Special Issue Radiomics in Oncology II)
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13 pages, 1010 KiB  
Article
Assessment of Response to Chemotherapy in Pancreatic Cancer with Liver Metastasis: CT Texture as a Predictive Biomarker
by Sihang Cheng, Zhengyu Jin and Huadan Xue
Diagnostics 2021, 11(12), 2252; https://doi.org/10.3390/diagnostics11122252 - 01 Dec 2021
Cited by 1 | Viewed by 1415
Abstract
In this paper, we assess changes in CT texture of metastatic liver lesions after treatment with chemotherapy in patients with pancreatic cancer and determine if texture parameters correlate with measured time to progression (TTP). This retrospective study included 110 patients with pancreatic cancer [...] Read more.
In this paper, we assess changes in CT texture of metastatic liver lesions after treatment with chemotherapy in patients with pancreatic cancer and determine if texture parameters correlate with measured time to progression (TTP). This retrospective study included 110 patients with pancreatic cancer with liver metastasis, and mean, entropy, kurtosis, skewness, mean of positive pixels, and standard deviation (SD) values were extracted during texture analysis. Response assessment was also obtained by using RECIST 1.1, Choi and modified Choi criteria, respectively. The correlation of texture parameters and existing assessment criteria with TTP were evaluated using Kaplan-Meier and Cox regression analyses in the training cohort. Kaplan-Meier curves of the proportion of patients without disease progression were significantly different for several texture parameters, and were better than those for RECIST 1.1-, Choi-, and modified Choi-defined response (p < 0.05 vs. p = 0.398, p = 0.142, and p = 0.536, respectively). Cox regression analysis showed that percentage change in SD was an independent predictor of TTP (p = 0.016) and confirmed in the validation cohort (p = 0.019). In conclusion, CT texture parameters have the potential to become predictive imaging biomarkers for response evaluation in pancreatic cancer with liver metastasis. Full article
(This article belongs to the Special Issue Radiomics in Oncology II)
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19 pages, 1252 KiB  
Article
A Low-Dose CT-Based Radiomic Model to Improve Characterization and Screening Recall Intervals of Indeterminate Prevalent Pulmonary Nodules
by Leonardo Rundo, Roberta Eufrasia Ledda, Christian di Noia, Evis Sala, Giancarlo Mauri, Gianluca Milanese, Nicola Sverzellati, Giovanni Apolone, Maria Carla Gilardi, Maria Cristina Messa, Isabella Castiglioni and Ugo Pastorino
Diagnostics 2021, 11(9), 1610; https://doi.org/10.3390/diagnostics11091610 - 03 Sep 2021
Cited by 11 | Viewed by 3305
Abstract
Lung cancer (LC) is currently one of the main causes of cancer-related deaths worldwide. Low-dose computed tomography (LDCT) of the chest has been proven effective in secondary prevention (i.e., early detection) of LC by several trials. In this work, we investigated the potential [...] Read more.
Lung cancer (LC) is currently one of the main causes of cancer-related deaths worldwide. Low-dose computed tomography (LDCT) of the chest has been proven effective in secondary prevention (i.e., early detection) of LC by several trials. In this work, we investigated the potential impact of radiomics on indeterminate prevalent pulmonary nodule (PN) characterization and risk stratification in subjects undergoing LDCT-based LC screening. As a proof-of-concept for radiomic analyses, the first aim of our study was to assess whether indeterminate PNs could be automatically classified by an LDCT radiomic classifier as solid or sub-solid (first-level classification), and in particular for sub-solid lesions, as non-solid versus part-solid (second-level classification). The second aim of the study was to assess whether an LCDT radiomic classifier could automatically predict PN risk of malignancy, and thus optimize LDCT recall timing in screening programs. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, positive predictive value, negative predictive value, sensitivity, and specificity. The experimental results showed that an LDCT radiomic machine learning classifier can achieve excellent performance for characterization of screen-detected PNs (mean AUC of 0.89 ± 0.02 and 0.80 ± 0.18 on the blinded test dataset for the first-level and second-level classifiers, respectively), providing quantitative information to support clinical management. Our study showed that a radiomic classifier could be used to optimize LDCT recall for indeterminate PNs. According to the performance of such a classifier on the blinded test dataset, within the first 6 months, 46% of the malignant PNs and 38% of the benign ones were identified, improving early detection of LC by doubling the current detection rate of malignant nodules from 23% to 46% at a low cost of false positives. In conclusion, we showed the high potential of LDCT-based radiomics for improving the characterization and optimizing screening recall intervals of indeterminate PNs. Full article
(This article belongs to the Special Issue Radiomics in Oncology II)
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7 pages, 2051 KiB  
Case Report
Colloid Carcinoma of the Pancreas with a Series of Radiological and Pathological Studies for Diagnosis: A Case Report
by Chuan-Han Chen, Hong-Zen Yeh and Hsin-Ni Li
Diagnostics 2022, 12(2), 282; https://doi.org/10.3390/diagnostics12020282 - 22 Jan 2022
Cited by 3 | Viewed by 3300
Abstract
Pancreatic colloid carcinoma is an uncommon and unique malignancy possessing a significantly more favorable prognosis than that of ordinary pancreatic ductal adenocarcinoma. Accurate diagnosis of this rare entity is thus important for leading the ensuing optimal treatment. Herein we report a case of [...] Read more.
Pancreatic colloid carcinoma is an uncommon and unique malignancy possessing a significantly more favorable prognosis than that of ordinary pancreatic ductal adenocarcinoma. Accurate diagnosis of this rare entity is thus important for leading the ensuing optimal treatment. Herein we report a case of colloid carcinoma of the pancreas with a series of imaging findings and pathologic assessments. Being familiar with these radio-pathological features makes early diagnosis possible prior to operation. Full article
(This article belongs to the Special Issue Radiomics in Oncology II)
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