Editorial

4 pages, 183 KiB

Open AccessEditorial

Introduction to Special Issue of Radiology and Imaging of Cancer

by Roberta Fusco, Vincenza Granata and Antonella Petrillo

Cancers 2020, 12(9), 2665; https://doi.org/10.3390/cancers12092665 - 18 Sep 2020

Cited by 26 | Viewed by 1965

The increase in knowledge in oncology and the possibility of creating personalized medicine by selecting a more appropriate therapy related to the different tumor subtypes, as well as the management of patients with cancer within a multidisciplinary team has improved the clinical outcomes [...] Read more.

The increase in knowledge in oncology and the possibility of creating personalized medicine by selecting a more appropriate therapy related to the different tumor subtypes, as well as the management of patients with cancer within a multidisciplinary team has improved the clinical outcomes [...] Full article

(This article belongs to the Special Issue Radiology and Imaging of Cancer)

Research

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16 pages, 1272 KiB

Open AccessEditor’s ChoiceArticle

CT-Based Radiomics Analysis to Predict Histopathological Outcomes Following Liver Resection in Colorectal Liver Metastases

by Vincenza Granata, Roberta Fusco, Sergio Venanzio Setola, Federica De Muzio, Federica Dell’ Aversana, Carmen Cutolo, Lorenzo Faggioni, Vittorio Miele, Francesco Izzo and Antonella Petrillo

Cancers 2022, 14(7), 1648; https://doi.org/10.3390/cancers14071648 - 24 Mar 2022

Cited by 28 | Viewed by 4915

Abstract

Purpose: We aimed to assess the efficacy of radiomic features extracted by computed tomography (CT) in predicting histopathological outcomes following liver resection in colorectal liver metastases patients, evaluating recurrence, mutational status, histopathological characteristics (mucinous), and surgical resection margin. Methods: This retrospectively approved study [...] Read more.

Purpose: We aimed to assess the efficacy of radiomic features extracted by computed tomography (CT) in predicting histopathological outcomes following liver resection in colorectal liver metastases patients, evaluating recurrence, mutational status, histopathological characteristics (mucinous), and surgical resection margin. Methods: This retrospectively approved study included a training set and an external validation set. The internal training set included 49 patients with a median age of 60 years and 119 liver colorectal metastases. The validation cohort consisted of 28 patients with single liver colorectal metastasis and a median age of 61 years. Radiomic features were extracted using PyRadiomics on CT portal phase. Nonparametric Kruskal–Wallis tests, intraclass correlation, receiver operating characteristic (ROC) analyses, linear regression modeling, and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. Results: The median value of intraclass correlation coefficients for the features was 0.92 (range 0.87–0.96). The best performance in discriminating expansive versus infiltrative front of tumor growth was wavelet_HHL_glcm_Imc2, with an accuracy of 79%, a sensitivity of 84%, and a specificity of 67%. The best performance in discriminating expansive versus tumor budding was wavelet_LLL_firstorder_Mean, with an accuracy of 86%, a sensitivity of 91%, and a specificity of 65%. The best performance in differentiating the mucinous type of tumor was original_firstorder_RobustMeanAbsoluteDeviation, with an accuracy of 88%, a sensitivity of 42%, and a specificity of 100%. The best performance in identifying tumor recurrence was the wavelet_HLH_glcm_Idmn, with an accuracy of 85%, a sensitivity of 81%, and a specificity of 88%. The best linear regression model was obtained with the identification of recurrence considering the linear combination of the 16 significant textural metrics (accuracy of 97%, sensitivity of 94%, and specificity of 98%). The best performance for each outcome was reached using KNN as a classifier with an accuracy greater than 86% in the training and validation sets for each classification problem; the best results were obtained with the identification of tumor front growth considering the seven significant textural features (accuracy of 97%, sensitivity of 90%, and specificity of 100%). Conclusions: This study confirmed the capacity of radiomics data to identify several prognostic features that may affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach. Full article

(This article belongs to the Special Issue Radiology and Imaging of Cancer)

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18 pages, 2905 KiB

Open AccessArticle

Contrast MR-Based Radiomics and Machine Learning Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases: A Preliminary Study

by Vincenza Granata, Roberta Fusco, Federica De Muzio, Carmen Cutolo, Sergio Venanzio Setola, Federica dell’ Aversana, Alessandro Ottaiano, Antonio Avallone, Guglielmo Nasti, Francesca Grassi, Vincenzo Pilone, Vittorio Miele, Luca Brunese, Francesco Izzo and Antonella Petrillo

Cancers 2022, 14(5), 1110; https://doi.org/10.3390/cancers14051110 - 22 Feb 2022

Cited by 26 | Viewed by 2289

Abstract

Purpose: To assess radiomics features efficacy obtained by arterial and portal MRI phase in the prediction of clinical outcomes in the colorectal liver metastases patients, evaluating recurrence, mutational status, pathological characteristic (mucinous and tumor budding) and surgical resection margin. Methods: This retrospective analysis [...] Read more.

Purpose: To assess radiomics features efficacy obtained by arterial and portal MRI phase in the prediction of clinical outcomes in the colorectal liver metastases patients, evaluating recurrence, mutational status, pathological characteristic (mucinous and tumor budding) and surgical resection margin. Methods: This retrospective analysis was approved by the local Ethical Committee board, and radiological databases were used to select patients with colorectal liver metastases with pathological proof and MRI study in a pre-surgical setting after neoadjuvant chemotherapy. The cohort of patients included a training set (51 patients with 61 years of median age and 121 liver metastases) and an external validation set (30 patients with single lesion with 60 years of median age). For each segmented volume of interest on MRI by two expert radiologists, 851 radiomics features were extracted as median values using the PyRadiomics tool. Non-parametric Kruskal-Wallis test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. Results: The best predictor to discriminate expansive versus infiltrative tumor growth front was wavelet_LHH_glrlm_ShortRunLowGrayLevelEmphasis extracted on portal phase with accuracy of 82%, sensitivity of 84%, and specificity of 77%. The best predictor to discriminate tumor budding was wavelet_LLH_firstorder_10Percentile extracted on portal phase with accuracy of 92%, a sensitivity of 96%, and a specificity of 81%. The best predictor to differentiate the mucinous type of tumor was the wavelet_LLL_glcm_ClusterTendency extracted on portal phase with accuracy of 88%, a sensitivity of 38%, and a specificity of 100%. The best predictor to identify the recurrence was the wavelet_HLH_ngtdm_Complexity extracted on arterial phase with accuracy of 90%, a sensitivity of 71%, and a specificity of 95%. The best linear regression model was obtained in the identification of mucinous type considering the 13 textural significant metrics extracted by arterial phase (accuracy of 94%, sensitivity of 77% and a specificity of 99%). The best results were obtained in the identification of tumor budding with the eleven textural significant features extracted by arterial phase using a KNN (accuracy of 95%, sensitivity of 84%, and a specificity of 99%). Conclusions: Our results confirmed the capacity of radiomics to identify as biomarkers and several prognostic features that could affect the treatment choice in patients with liver metastases in order to obtain a more personalized approach. Full article

(This article belongs to the Special Issue Radiology and Imaging of Cancer)

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16 pages, 7720 KiB

Open AccessArticle

Longitudinal CT Imaging to Explore the Predictive Power of 3D Radiomic Tumour Heterogeneity in Precise Imaging of Mantle Cell Lymphoma (MCL)

by Catharina Silvia Lisson, Christoph Gerhard Lisson, Sherin Achilles, Marc Fabian Mezger, Daniel Wolf, Stefan Andreas Schmidt, Wolfgang M. Thaiss, Johannes Bloehdorn, Ambros J. Beer, Stephan Stilgenbauer, Meinrad Beer and Michael Götz

Cancers 2022, 14(2), 393; https://doi.org/10.3390/cancers14020393 - 13 Jan 2022

Cited by 4 | Viewed by 2281

Abstract

The study’s primary aim is to evaluate the predictive performance of CT-derived 3D radiomics for MCL risk stratification. The secondary objective is to search for radiomic features associated with sustained remission. Included were 70 patients: 31 MCL patients and 39 control subjects with [...] Read more.

The study’s primary aim is to evaluate the predictive performance of CT-derived 3D radiomics for MCL risk stratification. The secondary objective is to search for radiomic features associated with sustained remission. Included were 70 patients: 31 MCL patients and 39 control subjects with normal axillary lymph nodes followed over five years. Radiomic analysis of all targets (n = 745) was performed and features selected using the Mann Whitney U test; the discriminative power of identifying “high-risk MCL” was evaluated by receiver operating characteristics (ROC). The four radiomic features, “Uniformity”, “Entropy”, “Skewness” and “Difference Entropy” showed predictive significance for relapse (p < 0.05)—in contrast to the routine size measurements, which showed no relevant difference. The best prognostication for relapse achieved the feature “Uniformity” (AUC-ROC-curve 0.87; optimal cut-off ≤0.0159 to predict relapse with 87% sensitivity, 65% specificity, 69% accuracy). Several radiomic features, including the parameter “Short Axis,” were associated with sustained remission. CT-derived 3D radiomics improves the predictive estimation of MCL patients; in combination with the ability to identify potential radiomic features that are characteristic for sustained remission, it may assist physicians in the clinical management of MCL. Full article

(This article belongs to the Special Issue Radiology and Imaging of Cancer)

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13 pages, 2069 KiB

Open AccessArticle

Predicting the Performance of Concurrent Systematic Random Biopsies during Image Fusion Targeted Sampling of Multi-Parametric MRI Detected Prostate Cancer. A Prospective Study (PRESET Study)

by Saeed Alqahtani, Xinyu Zhang, Cheng Wei, Yilong Zhang, Magdalena Szewczyk-Bieda, Jennifer Wilson, Zhihong Huang and Ghulam Nabi

Cancers 2022, 14(1), 1; https://doi.org/10.3390/cancers14010001 - 21 Dec 2021

Cited by 3 | Viewed by 2804

Abstract

The study was aimed to develop a predictive model to identify patients who may benefit from performing systematic random biopsies (SB) in addition to targeted biopsies (TB) in men suspected of having prostate cancer. A total of 198 patients with positive pre-biopsy MRI [...] Read more.

The study was aimed to develop a predictive model to identify patients who may benefit from performing systematic random biopsies (SB) in addition to targeted biopsies (TB) in men suspected of having prostate cancer. A total of 198 patients with positive pre-biopsy MRI findings and who had undergone both TB and SB were prospectively recruited into this study. The primary outcome was detection rates of clinically significant prostate cancer (csPCa) in SB and TB approaches. The secondary outcome was net clinical benefits of SB in addition to TB. A logistic regression model and nomogram construction were used to perform a multivariate analysis. The detection rate of csPCa using SB was 51.0% (101/198) compared to a rate of 56.1% (111/198) for TB, using a patient-based biopsy approach. The detection rate of csPCa was higher using a combined biopsy (64.6%; 128/198) in comparison to TB (56.1%; 111/198) alone. This was statistically significant (p < 0.001). Age, PSA density and PIRADS score significantly predicted the detection of csPCa by SB in addition to TB. A nomogram based on the model showed good discriminative ability (C-index; 78%). The decision analysis curve confirmed a higher net clinical benefit at an acceptable threshold. Full article

(This article belongs to the Special Issue Radiology and Imaging of Cancer)

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18 pages, 24309 KiB

Open AccessArticle

A Multiparametric MR-Based RadioFusionOmics Model with Robust Capabilities of Differentiating Glioblastoma Multiforme from Solitary Brain Metastasis

by Jialiang Wu, Fangrong Liang, Ruili Wei, Shengsheng Lai, Xiaofei Lv, Shiwei Luo, Zhe Wu, Huixian Chen, Wanli Zhang, Xiangling Zeng, Xianghua Ye, Yong Wu, Xinhua Wei, Xinqing Jiang, Xin Zhen and Ruimeng Yang

Cancers 2021, 13(22), 5793; https://doi.org/10.3390/cancers13225793 - 18 Nov 2021

Cited by 8 | Viewed by 2224

Abstract

This study aimed to evaluate the diagnostic potential of a novel RFO model in differentiating GBM and SBM with multiparametric MR sequences collected from 244 (131 GBM and 113 SBM) patients. Three basic volume of interests (VOIs) were delineated on the conventional axial [...] Read more.

This study aimed to evaluate the diagnostic potential of a novel RFO model in differentiating GBM and SBM with multiparametric MR sequences collected from 244 (131 GBM and 113 SBM) patients. Three basic volume of interests (VOIs) were delineated on the conventional axial MR images (T₁WI, T₂WI, T₂_FLAIR, and CE_T₁WI), including volumetric non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE). Using the RFO model, radiomics features extracted from different multiparametric MRI sequence(s) and VOI(s) were fused and the best sequence and VOI, or possible combinations, were determined. A multi-disciplinary team (MDT)-like fusion was performed to integrate predictions from the high-performing models for the final discrimination of GBM vs. SBM. Image features extracted from the volumetric ET (VOI_ET) had dominant predictive performances over features from other VOI combinations. Fusion of VOI_ET features from the T₁WI and T₂_FLAIR sequences via the RFO model achieved a discrimination accuracy of AUC = 0.925, accuracy = 0.855, sensitivity = 0.856, and specificity = 0.853, on the independent testing cohort 1, and AUC = 0.859, accuracy = 0.836, sensitivity = 0.708, and specificity = 0.919 on the independent testing cohort 2, which significantly outperformed three experienced radiologists (p = 0.03, 0.01, 0.02, and 0.01, and p = 0.02, 0.01, 0.45, and 0.02, respectively) and the MDT-decision result of three experienced experts (p = 0.03, 0.02, 0.03, and 0.02, and p = 0.03, 0.02, 0.44, and 0.03, respectively). Full article

(This article belongs to the Special Issue Radiology and Imaging of Cancer)

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13 pages, 7545 KiB

Open AccessArticle

Value of Assessing Peripheral Vascularization with Micro-Flow Imaging, Resistive Index and Absent Hilum Sign as Predictor for Malignancy in Lymph Nodes in Head and Neck Squamous Cell Carcinoma

by Petra K. de Koekkoek-Doll, Sander Roberti, Michiel W. van den Brekel, Monique Maas, Laura Smit, Regina Beets-Tan and Jonas Castelijns

Cancers 2021, 13(20), 5071; https://doi.org/10.3390/cancers13205071 - 10 Oct 2021

Cited by 7 | Viewed by 15533

Abstract

Ultrasound-guided fine needle aspiration cytology (USgFNAC) is commonly used for nodal staging in head and neck squamous cell cancer (HNSCC). Peripheral vascularity is a described feature for node metastasis. Micro-flow imaging (MFI) is a new sensitive technique to evaluate micro-vascularization. Our goal is [...] Read more.

Ultrasound-guided fine needle aspiration cytology (USgFNAC) is commonly used for nodal staging in head and neck squamous cell cancer (HNSCC). Peripheral vascularity is a described feature for node metastasis. Micro-flow imaging (MFI) is a new sensitive technique to evaluate micro-vascularization. Our goal is to assess the additional value of MFI to detect malignancy in lymph nodes. A total of 102 patients with HNSCC were included prospectively. USgFNAC was performed with the Philips eL18–4 transducer. Cytological results served as a reference standard to evaluate the prediction of cytological malignancy depending on ultrasound features such as resistive index (RI), absence of fatty hilum sign, and peripheral vascularization. Results were obtained for all US examinations and for the subgroup of clinically node-negative neck (cN0). USgFNAC was performed in 211 nodes. Peripheral vascularization had a positive predictive value (PPV) of 83% (cN0: 50%) and the absence of a fatty hilum had a PPV of 82% (cN0 50%) The combination of peripheral vascularization and absent fatty hilum had a PPV of 94% (cN0: 72%). RI (threshold: 0.705) had a PPV of 61% (cN0: RI-threshold 0.615, PPV 20%), whereas the PPV of short axis diameter (threshold of 6.5mm) was 59% for all patients and 19% in cN0 necks (threshold of 4 mm). Peripheral vascularization assessed by MFI and absent hilum has a high predictive value for cytological malignancy in neck metastases. Next to size, both features should be used as additional selection criteria for USgFNAC. Full article

(This article belongs to the Special Issue Radiology and Imaging of Cancer)

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13 pages, 2253 KiB

Open AccessArticle

A Humanized Anti-GPC3 Antibody for Immuno-Positron Emission Tomography Imaging of Orthotopic Mouse Model of Patient-Derived Hepatocellular Carcinoma Xenografts

by Arutselvan Natarajan, Hui Zhang, Wei Ye, Lakshmi Huttad, Mingdian Tan, Mei-Sze Chua, Sanjiv S. Gambhir and Samuel K. So

Cancers 2021, 13(16), 3977; https://doi.org/10.3390/cancers13163977 - 06 Aug 2021

Cited by 7 | Viewed by 2922

Abstract

Glypican-3 (GPC3) is an attractive diagnostic marker for hepatocellular carcinoma (HCC). We previously reported the potential of an ⁸⁹Zr-labeled murine anti-GPC3 antibody (clone 1G12) for immunoPET imaging of HCC in orthotopic patient-derived xenograft (PDX) mouse models. We now humanized the murine antibody [...] Read more.

Glypican-3 (GPC3) is an attractive diagnostic marker for hepatocellular carcinoma (HCC). We previously reported the potential of an ⁸⁹Zr-labeled murine anti-GPC3 antibody (clone 1G12) for immunoPET imaging of HCC in orthotopic patient-derived xenograft (PDX) mouse models. We now humanized the murine antibody by complementarity determining region (CDR) grafting, to allow its clinical translation for human use. The engineered humanized anti-GPC3 antibody, clone H3K3, retained comparable binding affinity and specificity to human GPC3. H3K3 was conjugated with desferrioxamine (Df) and radiolabeled with ⁸⁹Zr to produce the PET/CT tracer ⁸⁹Zr-Df-H3K3. When injected into GPC3-expressing orthotopic HCC PDX in NOD SCID Gamma (NSG) mice, ⁸⁹Zr-Df-H3K3 showed specific high uptake into the orthotopic PDX and minimal, non-specific uptake into the non-tumor bearing liver. Specificity was demonstrated by significantly higher uptake of ⁸⁹Zr-Df-H3K3 into the non-blocked PDX mice, compared with the blocked PDX mice (which received prior injection of 100 mg of unlabeled H3K3). Region of interest (ROI) analysis showed that the PDX/non-tumor liver ratio was highest (mean ± SD: 3.4 ± 0.31) at 168 h post injection; this ratio was consistent with biodistribution studies at the same time point. Thus, our humanized anti-GPC3 antibody, H3K3, shows encouraging potential for use as an immunoPET tracer for diagnostic imaging of HCC patients. Full article

(This article belongs to the Special Issue Radiology and Imaging of Cancer)

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20 pages, 2804 KiB

Open AccessArticle

Early Prediction of Breast Cancer Recurrence for Patients Treated with Neoadjuvant Chemotherapy: A Transfer Learning Approach on DCE-MRIs

by Maria Colomba Comes, Daniele La Forgia, Vittorio Didonna, Annarita Fanizzi, Francesco Giotta, Agnese Latorre, Eugenio Martinelli, Arianna Mencattini, Angelo Virgilio Paradiso, Pasquale Tamborra, Antonella Terenzio, Alfredo Zito, Vito Lorusso and Raffaella Massafra

Cancers 2021, 13(10), 2298; https://doi.org/10.3390/cancers13102298 - 11 May 2021

Cited by 28 | Viewed by 2761

Abstract

Cancer treatment planning benefits from an accurate early prediction of the treatment efficacy. The goal of this study is to give an early prediction of three-year Breast Cancer Recurrence (BCR) for patients who underwent neoadjuvant chemotherapy. We addressed the task from a new [...] Read more.

Cancer treatment planning benefits from an accurate early prediction of the treatment efficacy. The goal of this study is to give an early prediction of three-year Breast Cancer Recurrence (BCR) for patients who underwent neoadjuvant chemotherapy. We addressed the task from a new perspective based on transfer learning applied to pre-treatment and early-treatment DCE-MRI scans. Firstly, low-level features were automatically extracted from MR images using a pre-trained Convolutional Neural Network (CNN) architecture without human intervention. Subsequently, the prediction model was built with an optimal subset of CNN features and evaluated on two sets of patients from I-SPY1 TRIAL and BREAST-MRI-NACT-Pilot public databases: a fine-tuning dataset (70 not recurrent and 26 recurrent cases), which was primarily used to find the optimal subset of CNN features, and an independent test (45 not recurrent and 17 recurrent cases), whose patients had not been involved in the feature selection process. The best results were achieved when the optimal CNN features were augmented by four clinical variables (age, ER, PgR, HER2+), reaching an accuracy of 91.7% and 85.2%, a sensitivity of 80.8% and 84.6%, a specificity of 95.7% and 85.4%, and an AUC value of 0.93 and 0.83 on the fine-tuning dataset and the independent test, respectively. Finally, the CNN features extracted from pre-treatment and early-treatment exams were revealed to be strong predictors of BCR. Full article

(This article belongs to the Special Issue Radiology and Imaging of Cancer)

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9 pages, 1286 KiB

Open AccessArticle

Radiation-Induced Pneumonitis in the Era of the COVID-19 Pandemic: Artificial Intelligence for Differential Diagnosis

by Francesco Maria Giordano, Edy Ippolito, Carlo Cosimo Quattrocchi, Carlo Greco, Carlo Augusto Mallio, Bianca Santo, Pasquale D’Alessio, Pierfilippo Crucitti, Michele Fiore, Bruno Beomonte Zobel, Rolando Maria D’Angelillo and Sara Ramella

Cancers 2021, 13(8), 1960; https://doi.org/10.3390/cancers13081960 - 19 Apr 2021

Cited by 7 | Viewed by 2333

Abstract

(1) Aim: To test the performance of a deep learning algorithm in discriminating radiation therapy-related pneumonitis (RP) from COVID-19 pneumonia. (2) Methods: In this retrospective study, we enrolled three groups of subjects: pneumonia-free (control group), COVID-19 pneumonia and RP patients. CT images were [...] Read more.

(1) Aim: To test the performance of a deep learning algorithm in discriminating radiation therapy-related pneumonitis (RP) from COVID-19 pneumonia. (2) Methods: In this retrospective study, we enrolled three groups of subjects: pneumonia-free (control group), COVID-19 pneumonia and RP patients. CT images were analyzed by mean of an artificial intelligence (AI) algorithm based on a novel deep convolutional neural network structure. The cut-off value of risk probability of COVID-19 was 30%; values higher than 30% were classified as COVID-19 High Risk, and values below 30% as COVID-19 Low Risk. The statistical analysis included the Mann–Whitney U test (significance threshold at p < 0.05) and receiver operating characteristic (ROC) curve, with fitting performed using the maximum likelihood fit of a binormal model. (3) Results: Most patients presenting RP (66.7%) were classified by the algorithm as COVID-19 Low Risk. The algorithm showed high sensitivity but low specificity in the detection of RP against COVID-19 pneumonia (sensitivity = 97.0%, specificity = 2%, area under the curve (AUC = 0.72). The specificity increased when an estimated COVID-19 risk probability cut-off of 30% was applied (sensitivity 76%, specificity 63%, AUC = 0.84). (4) Conclusions: The deep learning algorithm was able to discriminate RP from COVID-19 pneumonia, classifying most RP cases as COVID-19 Low Risk. Full article

(This article belongs to the Special Issue Radiology and Imaging of Cancer)

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12 pages, 5914 KiB

Open AccessArticle

Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis

by Carlo Augusto Mallio, Andrea Napolitano, Gennaro Castiello, Francesco Maria Giordano, Pasquale D'Alessio, Mario Iozzino, Yipeng Sun, Silvia Angeletti, Marco Russano, Daniele Santini, Giuseppe Tonini, Bruno Beomonte Zobel, Bruno Vincenzi and Carlo Cosimo Quattrocchi

Cancers 2021, 13(4), 652; https://doi.org/10.3390/cancers13040652 - 06 Feb 2021

Cited by 20 | Viewed by 6065

Abstract

Background: Coronavirus disease 2019 (COVID-19) pneumonia and immune checkpoint inhibitor (ICI) therapy-related pneumonitis share common features. The aim of this study was to determine on chest computed tomography (CT) images whether a deep convolutional neural network algorithm is able to solve the challenge [...] Read more.

Background: Coronavirus disease 2019 (COVID-19) pneumonia and immune checkpoint inhibitor (ICI) therapy-related pneumonitis share common features. The aim of this study was to determine on chest computed tomography (CT) images whether a deep convolutional neural network algorithm is able to solve the challenge of differential diagnosis between COVID-19 pneumonia and ICI therapy-related pneumonitis. Methods: We enrolled three groups: a pneumonia-free group (n = 30), a COVID-19 group (n = 34), and a group of patients with ICI therapy-related pneumonitis (n = 21). Computed tomography images were analyzed with an artificial intelligence (AI) algorithm based on a deep convolutional neural network structure. Statistical analysis included the Mann–Whitney U test (significance threshold at p < 0.05) and the receiver operating characteristic curve (ROC curve). Results: The algorithm showed low specificity in distinguishing COVID-19 from ICI therapy-related pneumonitis (sensitivity 97.1%, specificity 14.3%, area under the curve (AUC) = 0.62). ICI therapy-related pneumonitis was identified by the AI when compared to pneumonia-free controls (sensitivity = 85.7%, specificity 100%, AUC = 0.97). Conclusions: The deep learning algorithm is not able to distinguish between COVID-19 pneumonia and ICI therapy-related pneumonitis. Awareness must be increased among clinicians about imaging similarities between COVID-19 and ICI therapy-related pneumonitis. ICI therapy-related pneumonitis can be applied as a challenge population for cross-validation to test the robustness of AI models used to analyze interstitial pneumonias of variable etiology. Full article

(This article belongs to the Special Issue Radiology and Imaging of Cancer)

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12 pages, 2176 KiB

Open AccessEditor’s ChoiceArticle

Radiomics-Derived Data by Contrast Enhanced Magnetic Resonance in RAS Mutations Detection in Colorectal Liver Metastases

by Vincenza Granata, Roberta Fusco, Antonio Avallone, Alfonso De Stefano, Alessandro Ottaiano, Carolina Sbordone, Luca Brunese, Francesco Izzo and Antonella Petrillo

Cancers 2021, 13(3), 453; https://doi.org/10.3390/cancers13030453 - 25 Jan 2021

Cited by 54 | Viewed by 2821

Abstract

Purpose: To assess the association of RAS mutation status and radiomics-derived data by Contrast Enhanced-Magnetic Resonance Imaging (CE-MRI) in liver metastases. Materials and Methods: 76 patients (36 women and 40 men; 59 years of mean age and 36–80 years as range) [...] Read more.

Purpose: To assess the association of RAS mutation status and radiomics-derived data by Contrast Enhanced-Magnetic Resonance Imaging (CE-MRI) in liver metastases. Materials and Methods: 76 patients (36 women and 40 men; 59 years of mean age and 36–80 years as range) were included in this retrospective study. Texture metrics and parameters based on lesion morphology were calculated. Per-patient univariate and multivariate analysis were made. Wilcoxon-Mann-Whitney U test, receiver operating characteristic (ROC) analysis, pattern recognition approaches with features selection approaches were considered. Results: Significant results were obtained for texture features while morphological parameters had not significant results to classify RAS mutation. The results showed that using a univariate analysis was not possible to discriminate accurately the RAS mutation status. Instead, considering a multivariate analysis and classification approaches, a KNN exclusively with texture parameters as predictors reached the best results (AUC of 0.84 and an accuracy of 76.9% with 90.0% of sensitivity and 67.8% of specificity on training set and an accuracy of 87.5% with 91.7% of sensitivity and 83.3% of specificity on external validation cohort). Conclusions: Texture parameters derived by CE-MRI and combined using multivariate analysis and patter recognition approaches could allow stratifying the patients according to RAS mutation status. Full article

(This article belongs to the Special Issue Radiology and Imaging of Cancer)

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Review

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17 pages, 942 KiB

Open AccessReview

Advanced Magnetic Resonance Imaging (MRI) Techniques: Technical Principles and Applications in Nanomedicine

by Federico Bruno, Vincenza Granata, Flavia Cobianchi Bellisari, Ferruccio Sgalambro, Emanuele Tommasino, Pierpaolo Palumbo, Francesco Arrigoni, Diletta Cozzi, Francesca Grassi, Maria Chiara Brunese, Silvia Pradella, Maria Luisa Mangoni di S. Stefano, Carmen Cutolo, Ernesto Di Cesare, Alessandra Splendiani, Andrea Giovagnoni, Vittorio Miele, Roberto Grassi, Carlo Masciocchi and Antonio Barile

Cancers 2022, 14(7), 1626; https://doi.org/10.3390/cancers14071626 - 23 Mar 2022

Cited by 21 | Viewed by 3654

Abstract

In the last decades, nanotechnology has been used in a wide range of biomedical applications, both diagnostic and therapeutic. In this scenario, imaging techniques represent a fundamental tool to obtain information about the properties of nanoconstructs and their interactions with the biological environment [...] Read more.

In the last decades, nanotechnology has been used in a wide range of biomedical applications, both diagnostic and therapeutic. In this scenario, imaging techniques represent a fundamental tool to obtain information about the properties of nanoconstructs and their interactions with the biological environment in preclinical and clinical settings. This paper reviews the state of the art of the application of magnetic resonance imaging in the field of nanomedicine, as well as the use of nanoparticles as diagnostic and therapeutic tools, especially in cancer, including the characteristics that hinder the use of nanoparticles in clinical practice. Full article

(This article belongs to the Special Issue Radiology and Imaging of Cancer)

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15 pages, 9633 KiB

Open AccessReview

Imaging of Neuroendocrine Prostatic Carcinoma

by Ahmed Taher, Corey T. Jensen, Sireesha Yedururi, Devaki Shilpa Surasi, Silvana C. Faria, Tharakeshwar K. Bathala, Bilal Mujtaba, Priya Bhosale, Nicolaus Wagner-Bartak and Ajaykumar C. Morani

Cancers 2021, 13(22), 5765; https://doi.org/10.3390/cancers13225765 - 17 Nov 2021

Cited by 9 | Viewed by 2641

Abstract

Neuroendocrine prostate cancer (NEPC) is an aggressive subtype of prostate cancer that typically has a high metastatic potential and poor prognosis in comparison to the adenocarcinoma subtype. Although it can arise de novo, NEPC much more commonly occurs as a mechanism of treatment [...] Read more.

Neuroendocrine prostate cancer (NEPC) is an aggressive subtype of prostate cancer that typically has a high metastatic potential and poor prognosis in comparison to the adenocarcinoma subtype. Although it can arise de novo, NEPC much more commonly occurs as a mechanism of treatment resistance during therapy for conventional prostatic adenocarcinoma, the latter is also termed as castration-resistant prostate cancer (CRPC). The incidence of NEPC increases after hormonal therapy and they represent a challenge, both in the radiological and pathological diagnosis, as well as in the clinical management. This article provides a comprehensive imaging review of prostatic neuroendocrine tumors. Full article

(This article belongs to the Special Issue Radiology and Imaging of Cancer)

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