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10 pages, 1650 KiB

Open AccessArticle

Influence of Adaptive Statistical Iterative Reconstructions on CT Radiomic Features in Oncologic Patients

by Damiano Caruso, Marta Zerunian, Francesco Pucciarelli, Benedetta Bracci, Michela Polici, Benedetta D’Arrigo, Tiziano Polidori, Gisella Guido, Luca Barbato, Daniele Polverari, Antonella Benvenga, Elsa Iannicelli and Andrea Laghi

Diagnostics 2021, 11(6), 1000; https://doi.org/10.3390/diagnostics11061000 - 31 May 2021

Cited by 8 | Viewed by 2115

Abstract

Iterative reconstructions (IR) might alter radiomic features extraction. We aim to evaluate the influence of Adaptive Statistical Iterative Reconstruction-V (ASIR-V) on CT radiomic features. Patients who underwent unenhanced abdominal CT (Revolution Evo, GE Healthcare, USA) were retrospectively enrolled. Raw data of filtered-back projection [...] Read more.

Iterative reconstructions (IR) might alter radiomic features extraction. We aim to evaluate the influence of Adaptive Statistical Iterative Reconstruction-V (ASIR-V) on CT radiomic features. Patients who underwent unenhanced abdominal CT (Revolution Evo, GE Healthcare, USA) were retrospectively enrolled. Raw data of filtered-back projection (FBP) were reconstructed with 10 levels of ASIR-V (10–100%). CT texture analysis (CTTA) of liver, kidney, spleen and paravertebral muscle for all datasets was performed. Six radiomic features (mean intensity, standard deviation (SD), entropy, mean of positive pixel (MPP), skewness, kurtosis) were extracted and compared between FBP and all ASIR-V levels, with and without altering the spatial scale filter (SSF). CTTA of all organs revealed significant differences between FBP and all ASIR-V reconstructions for mean intensity, SD, entropy and MPP (all p < 0.0001), while no significant differences were observed for skewness and kurtosis between FBP and all ASIR-V reconstructions (all p > 0.05). A per-filter analysis was also performed comparing FBP with all ASIR-V reconstructions for all six SSF separately (SSF0-SSF6). Results showed significant differences between FBP and all ASIR-V reconstruction levels for mean intensity, SD, and MPP (all filters p < 0.0315). Skewness and kurtosis showed no differences for all comparisons performed (all p > 0.05). The application of incremental ASIR-V levels affects CTTA across various filters. Skewness and kurtosis are not affected by IR and may be reliable quantitative parameters for radiomic analysis. Full article

(This article belongs to the Special Issue Radiomics in Oncology)

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12 pages, 1482 KiB

Open AccessArticle

A CT-Based Radiomic Signature Can Be Prognostic for 10-Months Overall Survival in Metastatic Tumors Treated with Nivolumab: An Exploratory Study

by Valentina D. A. Corino, Marco Bologna, Giuseppina Calareso, Lisa Licitra, Mariagrazia Ghi, Gaetana Rinaldi, Francesco Caponigro, Franco Morelli, Mario Airoldi, Giacomo Allegrini, Alessandra Cassano, Daris Ferrari, Aurora Mirabile, Alicia Tosoni, Danilo Galizia, Marco Merlano, Andrea Sponghini, Gabriella Moretti, Luca Mainardi and Paolo Bossi

Diagnostics 2021, 11(6), 979; https://doi.org/10.3390/diagnostics11060979 - 28 May 2021

Cited by 6 | Viewed by 2248

Abstract

Baseline clinical prognostic factors for recurrent and/or metastatic (RM) head and neck squamous cell carcinoma (HNSCC) treated with immunotherapy are lacking. CT-based radiomics may provide additional prognostic information. A total of 85 patients with RM-HNSCC were enrolled for this study. For each tumor, [...] Read more.

Baseline clinical prognostic factors for recurrent and/or metastatic (RM) head and neck squamous cell carcinoma (HNSCC) treated with immunotherapy are lacking. CT-based radiomics may provide additional prognostic information. A total of 85 patients with RM-HNSCC were enrolled for this study. For each tumor, radiomic features were extracted from the segmentation of the largest tumor mass. A pipeline including different feature selection steps was used to train a radiomic signature prognostic for 10-month overall survival (OS). Features were selected based on their stability to geometrical transformation of the segmentation (intraclass correlation coefficient, ICC > 0.75) and their predictive power (area under the curve, AUC > 0.7). The predictive model was developed using the least absolute shrinkage and selection operator (LASSO) in combination with the support vector machine. The model was developed based on the first 68 enrolled patients and tested on the last 17 patients. Classification performance of the radiomic risk was evaluated accuracy and the AUC. The same metrics were computed for some baseline predictors used in clinical practice (volume of largest lesion, total tumor volume, number of tumor lesions, number of affected organs, performance status). The AUC in the test set was 0.67, while accuracy was 0.82. The performance of the radiomic score was higher than the one obtainable with the clinical variables (largest lesion volume: accuracy 0.59, AUC = 0.55; number of tumoral lesions: accuracy 0.71, AUC 0.36; number of affected organs: accuracy 0.47; AUC 0.42; total tumor volume: accuracy 0.59, AUC 0.53; performance status: accuracy 0.41, AUC = 0.47). Radiomics may provide additional baseline prognostic value compared to the variables used in clinical practice. Full article

(This article belongs to the Special Issue Radiomics in Oncology)

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16 pages, 2032 KiB

Open AccessArticle

A [68Ga]Ga-DOTANOC PET/CT Radiomic Model for Non-Invasive Prediction of Tumour Grade in Pancreatic Neuroendocrine Tumours

by Alessandro Bevilacqua, Diletta Calabrò, Silvia Malavasi, Claudio Ricci, Riccardo Casadei, Davide Campana, Serena Baiocco, Stefano Fanti and Valentina Ambrosini

Diagnostics 2021, 11(5), 870; https://doi.org/10.3390/diagnostics11050870 - 12 May 2021

Cited by 14 | Viewed by 1975

Abstract

Predicting grade 1 (G1) and 2 (G2) primary pancreatic neuroendocrine tumour (panNET) is crucial to foresee panNET clinical behaviour. Fifty-one patients with G1-G2 primary panNET demonstrated by pre-surgical [68Ga]Ga-DOTANOC PET/CT and diagnostic conventional imaging were grouped according to the tumour grade assessment method: [...] Read more.

Predicting grade 1 (G1) and 2 (G2) primary pancreatic neuroendocrine tumour (panNET) is crucial to foresee panNET clinical behaviour. Fifty-one patients with G1-G2 primary panNET demonstrated by pre-surgical [68Ga]Ga-DOTANOC PET/CT and diagnostic conventional imaging were grouped according to the tumour grade assessment method: histology on the whole excised primary lesion (HS) or biopsy (BS). First-order and second-order radiomic features (RFs) were computed from SUV maps for the whole tumour volume on HS. The RFs showing the lowest p-values and the highest area under the curve (AUC) were selected. Three radiomic models were assessed: A (trained on HS, validated on BS), B (trained on BS, validated on HS), and C (using the cross-validation on the whole dataset). The second-order normalized homogeneity and entropy was the most effective RFs couple predicting G2 and G1. The best performance was achieved by model A (test AUC = 0.90, sensitivity = 0.88, specificity = 0.89), followed by model C (median test AUC = 0.87, sensitivity = 0.83, specificity = 0.82). Model B performed worse. Using HS to train a radiomic model leads to the best prediction, although a “hybrid” (HS+BS) population performs better than biopsy-only. The non-invasive prediction of panNET grading may be especially useful in lesions not amenable to biopsy while [68Ga]Ga-DOTANOC heterogeneity might recommend FDG PET/CT. Full article

(This article belongs to the Special Issue Radiomics in Oncology)

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24 pages, 4868 KiB

Open AccessArticle

Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification

by Roberta Fusco, Adele Piccirillo, Mario Sansone, Vincenza Granata, Maria Rosaria Rubulotta, Teresa Petrosino, Maria Luisa Barretta, Paolo Vallone, Raimondo Di Giacomo, Emanuela Esposito, Maurizio Di Bonito and Antonella Petrillo

Diagnostics 2021, 11(5), 815; https://doi.org/10.3390/diagnostics11050815 - 30 Apr 2021

Cited by 27 | Viewed by 2777

Abstract

The aim of the study was to estimate the diagnostic accuracy of textural features extracted by dual-energy contrast-enhanced mammography (CEM) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. In total, 80 patients with known breast lesion were enrolled [...] Read more.

The aim of the study was to estimate the diagnostic accuracy of textural features extracted by dual-energy contrast-enhanced mammography (CEM) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. In total, 80 patients with known breast lesion were enrolled in this prospective study according to regulations issued by the local Institutional Review Board. All patients underwent dual-energy CEM examination in both craniocaudally (CC) and double acquisition of mediolateral oblique (MLO) projections (early and late). The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy, and vacuum assisted breast biopsy for benign lesions. In total, 104 samples of 80 patients were analyzed. Furthermore, 48 textural parameters were extracted by manually segmenting regions of interest. Univariate and multivariate approaches were performed: non-parametric Wilcoxon–Mann–Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), artificial neural network (NNET), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance considering the CC view (accuracy (ACC) = 0.75; AUC = 0.82) was reached with a DT trained with leave-one-out cross-variation (LOOCV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of three robust textural features (MAD, VARIANCE, and LRLGE). The best performance (ACC = 0.77; AUC = 0.83) considering the early-MLO view was reached with a NNET trained with LOOCV and balanced data (with ADASYN function) and a subset of ten robust features (MEAN, MAD, RANGE, IQR, VARIANCE, CORRELATION, RLV, COARSNESS, BUSYNESS, and STRENGTH). The best performance (ACC = 0.73; AUC = 0.82) considering the late-MLO view was reached with a NNET trained with LOOCV and balanced data (with ADASYN function) and a subset of eleven robust features (MODE, MEDIAN, RANGE, RLN, LRLGE, RLV, LZLGE, GLV_GLSZM, ZSV, COARSNESS, and BUSYNESS). Multivariate analyses using pattern recognition approaches, considering 144 textural features extracted from all three mammographic projections (CC, early MLO, and late MLO), optimized by adaptive synthetic sampling and feature selection operations obtained the best results (ACC = 0.87; AUC = 0.90) and showed the best performance in the discrimination of benign and malignant lesions. Full article

(This article belongs to the Special Issue Radiomics in Oncology)

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13 pages, 5060 KiB

Open AccessArticle

The Heterogeneity of Skewness in T2W-Based Radiomics Predicts the Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

by Francesca Coppola, Margherita Mottola, Silvia Lo Monaco, Arrigo Cattabriga, Maria Adriana Cocozza, Jia Cheng Yuan, Caterina De Benedittis, Dajana Cuicchi, Alessandra Guido, Fabiola Lorena Rojas Llimpe, Antonietta D’Errico, Andrea Ardizzoni, Gilberto Poggioli, Lidia Strigari, Alessio Giuseppe Morganti, Franco Bazzoli, Luigi Ricciardiello, Rita Golfieri and Alessandro Bevilacqua

Diagnostics 2021, 11(5), 795; https://doi.org/10.3390/diagnostics11050795 - 28 Apr 2021

Cited by 18 | Viewed by 2294

Abstract

Our study aimed to investigate whether radiomics on MRI sequences can differentiate responder (R) and non-responder (NR) patients based on the tumour regression grade (TRG) assigned after surgical resection in locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT). Eighty-five patients undergoing [...] Read more.

Our study aimed to investigate whether radiomics on MRI sequences can differentiate responder (R) and non-responder (NR) patients based on the tumour regression grade (TRG) assigned after surgical resection in locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT). Eighty-five patients undergoing primary staging with MRI were retrospectively evaluated, and 40 patients were finally selected. The ROIs were manually outlined in the tumour site on T2w sequences in the oblique-axial plane. Based on the TRG, patients were grouped as having either a complete or a partial response (TRG = (0,1), n = 15). NR patients had a minimal or poor nCRT response (TRG = (2,3), n = 25). Eighty-four local first-order radiomic features (RFs) were extracted from tumour ROIs. Only single RFs were investigated. Each feature was selected using univariate analysis guided by a one-tailed Wilcoxon rank-sum. ROC curve analysis was performed, using AUC computation and the Youden index (YI) for sensitivity and specificity. The RF measuring the heterogeneity of local skewness of T2w values from tumour ROIs differentiated Rs and NRs with a p-value ≈ 10⁻⁵; AUC = 0.90 (95%CI, 0.73–0.96); and YI = 0.68, corresponding to 80% sensitivity and 88% specificity. In conclusion, higher heterogeneity in skewness maps of the baseline tumour correlated with a greater benefit from nCRT. Full article

(This article belongs to the Special Issue Radiomics in Oncology)

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15 pages, 1948 KiB

Open AccessArticle

The Primacy of High B-Value 3T-DWI Radiomics in the Prediction of Clinically Significant Prostate Cancer

by Alessandro Bevilacqua, Margherita Mottola, Fabio Ferroni, Alice Rossi, Giampaolo Gavelli and Domenico Barone

Diagnostics 2021, 11(5), 739; https://doi.org/10.3390/diagnostics11050739 - 21 Apr 2021

Cited by 13 | Viewed by 2265

Abstract

Predicting clinically significant prostate cancer (csPCa) is crucial in PCa management. 3T-magnetic resonance (MR) systems may have a novel role in quantitative imaging and early csPCa prediction, accordingly. In this study, we develop a radiomic model for predicting csPCa based solely on native [...] Read more.

Predicting clinically significant prostate cancer (csPCa) is crucial in PCa management. 3T-magnetic resonance (MR) systems may have a novel role in quantitative imaging and early csPCa prediction, accordingly. In this study, we develop a radiomic model for predicting csPCa based solely on native b2000 diffusion weighted imaging (DWI_b2000) and debate the effectiveness of apparent diffusion coefficient (ADC) in the same task. In total, 105 patients were retrospectively enrolled between January–November 2020, with confirmed csPCa or ncsPCa based on biopsy. DWI_b2000 and ADC images acquired with a 3T-MRI were analyzed by computing 84 local first-order radiomic features (RFs). Two predictive models were built based on DWI_b2000 and ADC, separately. Relevant RFs were selected through LASSO, a support vector machine (SVM) classifier was trained using repeated 3-fold cross validation (CV) and validated on a holdout set. The SVM models rely on a single couple of uncorrelated RFs (ρ < 0.15) selected through Wilcoxon rank-sum test (p ≤ 0.05) with Holm–Bonferroni correction. On the holdout set, while the ADC model yielded AUC = 0.76 (95% CI, 0.63–0.96), the DWI_b2000 model reached AUC = 0.84 (95% CI, 0.63–0.90), with specificity = 75%, sensitivity = 90%, and informedness = 0.65. This study establishes the primary role of 3T-DWI_b2000 in PCa quantitative analyses, whilst ADC can remain the leading sequence for detection. Full article

(This article belongs to the Special Issue Radiomics in Oncology)

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12 pages, 1285 KiB

Open AccessArticle

Prognostic Value of Baseline Radiomic Features of ¹⁸F-FDG PET in Patients with Diffuse Large B-Cell Lymphoma

by Kun-Han Lue, Yi-Feng Wu, Hsin-Hon Lin, Tsung-Cheng Hsieh, Shu-Hsin Liu, Sheng-Chieh Chan and Yu-Hung Chen

Diagnostics 2021, 11(1), 36; https://doi.org/10.3390/diagnostics11010036 - 28 Dec 2020

Cited by 24 | Viewed by 2580

Abstract

This study investigates whether baseline ¹⁸F-FDG PET radiomic features can predict survival outcomes in patients with diffuse large B-cell lymphoma (DLBCL). We retrospectively enrolled 83 patients diagnosed with DLBCL who underwent ¹⁸F-FDG PET scans before treatment. The patients were divided into [...] Read more.

This study investigates whether baseline ¹⁸F-FDG PET radiomic features can predict survival outcomes in patients with diffuse large B-cell lymphoma (DLBCL). We retrospectively enrolled 83 patients diagnosed with DLBCL who underwent ¹⁸F-FDG PET scans before treatment. The patients were divided into the training cohort (n = 58) and the validation cohort (n = 25). Eighty radiomic features were extracted from the PET images for each patient. Least absolute shrinkage and selection operator regression were used to reduce the dimensionality within radiomic features. Cox proportional hazards model was used to determine the prognostic factors for progression-free survival (PFS) and overall survival (OS). A prognostic stratification model was built in the training cohort and validated in the validation cohort using Kaplan–Meier survival analysis. In the training cohort, run length non-uniformity (RLN), extracted from a gray level run length matrix (GLRLM), was independently associated with PFS (hazard ratio (HR) = 15.7, p = 0.007) and OS (HR = 8.64, p = 0.040). The International Prognostic Index was an independent prognostic factor for OS (HR = 2.63, p = 0.049). A prognostic stratification model was devised based on both risk factors, which allowed identification of three risk groups for PFS and OS in the training (p < 0.001 and p < 0.001) and validation (p < 0.001 and p = 0.020) cohorts. Our results indicate that the baseline ¹⁸F-FDG PET radiomic feature, RLN_GLRLM, is an independent prognostic factor for survival outcomes. Furthermore, we propose a prognostic stratification model that may enable tailored therapeutic strategies for patients with DLBCL. Full article

(This article belongs to the Special Issue Radiomics in Oncology)

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Review

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19 pages, 427 KiB

Open AccessReview

Imaging Assessment of Tumor Response in the Era of Immunotherapy

by Jun Nakata, Kayako Isohashi, Yoshihiro Oka, Hiroko Nakajima, Soyoko Morimoto, Fumihiro Fujiki, Yusuke Oji, Akihiro Tsuboi, Atsushi Kumanogoh, Naoya Hashimoto, Jun Hatazawa and Haruo Sugiyama

Diagnostics 2021, 11(6), 1041; https://doi.org/10.3390/diagnostics11061041 - 05 Jun 2021

Cited by 4 | Viewed by 2693

Abstract

Assessment of tumor response during treatment is one of the most important purposes of imaging. Before the appearance of immunotherapy, response evaluation criteria in solid tumors (RECIST) and positron emission tomography response criteria in solid tumors (PERCIST) were, respectively, the established morphologic and [...] Read more.

Assessment of tumor response during treatment is one of the most important purposes of imaging. Before the appearance of immunotherapy, response evaluation criteria in solid tumors (RECIST) and positron emission tomography response criteria in solid tumors (PERCIST) were, respectively, the established morphologic and metabolic response criteria, and cessation of treatment was recommended when progressive disease was detected according to these criteria. However, various types of immunotherapy have been developed over the past 20 years, which show novel false positive findings on images, as well as distinct response patterns from conventional therapies. Antitumor immune response itself causes 18F-fluorodeoxyglucose (FDG) uptake in tumor sites, known as “flare phenomenon”, so that positron emission tomography using FDG can no longer accurately identify remaining tumors. Furthermore, tumors often initially increase, followed by stability or decrease resulting from immunotherapy, which is called “pseudoprogression”, so that progressive disease cannot be confirmed by computed tomography or magnetic resonance imaging at a single time point. As a result, neither RECIST nor PERCIST can accurately predict the response to immunotherapy, and therefore several new response criteria fixed for immunotherapy have been proposed. However, these criteria are still controversial, and also require months for response confirmation. The establishment of optimal response criteria and the development of new imaging technologies other than FDG are therefore urgently needed. In this review, we summarize the false positive images and the revision of response criteria for each immunotherapy, in order to avoid discontinuation of a truly effective immunotherapy. Full article

(This article belongs to the Special Issue Radiomics in Oncology)

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15 pages, 2882 KiB

Open AccessReview

Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice

by Francesca Coppola, Valentina Giannini, Michela Gabelloni, Jovana Panic, Arianna Defeudis, Silvia Lo Monaco, Arrigo Cattabriga, Maria Adriana Cocozza, Luigi Vincenzo Pastore, Michela Polici, Damiano Caruso, Andrea Laghi, Daniele Regge, Emanuele Neri, Rita Golfieri and Lorenzo Faggioni

Diagnostics 2021, 11(5), 756; https://doi.org/10.3390/diagnostics11050756 - 23 Apr 2021

Cited by 41 | Viewed by 3660

Abstract

While cross-sectional imaging has seen continuous progress and plays an undiscussed pivotal role in the diagnostic management and treatment planning of patients with rectal cancer, a largely unmet need remains for improved staging accuracy, assessment of treatment response and prediction of individual patient [...] Read more.

While cross-sectional imaging has seen continuous progress and plays an undiscussed pivotal role in the diagnostic management and treatment planning of patients with rectal cancer, a largely unmet need remains for improved staging accuracy, assessment of treatment response and prediction of individual patient outcome. Moreover, the increasing availability of target therapies has called for developing reliable diagnostic tools for identifying potential responders and optimizing overall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fully evolving research topic, which could harness the power of modern computer technology to generate quantitative information from imaging datasets based on advanced data-driven biomathematical models, potentially providing an added value to conventional imaging for improved patient management. The present study aimed to illustrate the contribution that current radiomics methods applied to magnetic resonance imaging can offer to managing patients with rectal cancer. Full article

(This article belongs to the Special Issue Radiomics in Oncology)

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29 pages, 2469 KiB

Open AccessReview

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

by Manuel Piñeiro-Fiel, Alexis Moscoso, Virginia Pubul, Álvaro Ruibal, Jesús Silva-Rodríguez and Pablo Aguiar

Diagnostics 2021, 11(2), 380; https://doi.org/10.3390/diagnostics11020380 - 23 Feb 2021

Cited by 33 | Viewed by 3791

Abstract

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: [...] Read more.

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years. Full article

(This article belongs to the Special Issue Radiomics in Oncology)

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