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Arias-Lorza AM, Costello JR, Hingorani SR, Von Hoff DD, Korn RL, Raghunand N. Magnetic resonance imaging of tumor response to stroma-modifying pegvorhyaluronidase alpha (PEGPH20) therapy in early-phase clinical trials. Sci Rep 2024; 14:11570. [PMID: 38773189 PMCID: PMC11109088 DOI: 10.1038/s41598-024-62470-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 05/17/2024] [Indexed: 05/23/2024] Open
Abstract
Pre-clinical and clinical studies have shown that PEGPH20 depletes intratumoral hyaluronic acid (HA), which is linked to high interstitial fluid pressures and poor distribution of chemotherapies. 29 patients with metastatic advanced solid tumors received quantitative magnetic resonance imaging (qMRI) in 3 prospective clinical trials of PEGPH20: HALO-109-101 (NCT00834704), HALO-109-102 (NCT01170897), and HALO-109-201 (NCT01453153). Apparent Diffusion Coefficient of water (ADC), T1, ktrans, vp, ve, and iAUC maps were computed from qMRI acquired at baseline and ≥ 1 time point post-PEGPH20. Tumor ADC and T1 decreased, while iAUC, ktrans, vp, and ve increased, on day 1 post-PEGPH20 relative to baseline values. This is consistent with HA depletion leading to a decrease in tumor extracellular water content and an increase in perfusion, permeability, extracellular matrix space, and vascularity. Baseline parameter values predictive of pharmacodynamic responses were: ADC > 1.46 × 10-3 mm2/s (Balanced Accuracy (BA) = 72%, p < 0.01), T1 > 0.54 s (BA = 82%, p < 0.01), iAUC < 9.2 mM-s (BA = 76%, p < 0.05), ktrans < 0.07 min-1 (BA = 72%, p = 0.2), ve < 0.17 (BA = 68%, p < 0.01), and vp < 0.02 (BA = 60%, p < 0.01). A low ve at baseline was moderately predictive of response in any parameter (BA = 65.6%, p < 0.01 averaged across patients). These qMRI biomarkers are potentially useful for guiding patient pre-selection and post-treatment follow-up in future clinical studies of PEGPH20 and other tumor stroma-modifying anti-cancer therapies.
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Affiliation(s)
| | | | - Sunil R Hingorani
- Division of Hematology and Oncology, Department of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
- Fred & Pamela Buffett Cancer Center, Pancreatic Cancer Center of Excellence, University of Nebraska Medical Center, Omaha, NE, USA
| | - Daniel D Von Hoff
- Translational Genomics Research Institute (TGen), Scottsdale, AZ, USA
- HonorHealth Clinical Research Institute, Phoenix, AZ, USA
| | | | - Natarajan Raghunand
- Department of Cancer Physiology, Moffitt Cancer Center, Tampa, FL, USA.
- Department of Oncologic Sciences, University of South Florida, Tampa, FL, USA.
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2
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van der Reijd DJ, Chupetlovska K, van Dijk E, Westerink B, Monraats MA, Van Griethuysen JJM, Lambregts DMJ, Tissier R, Beets-Tan RGH, Benson S, Maas M. Multi-sequence MRI radiomics of colorectal liver metastases: Which features are reproducible across readers? Eur J Radiol 2024; 172:111346. [PMID: 38309217 DOI: 10.1016/j.ejrad.2024.111346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/15/2024] [Accepted: 01/25/2024] [Indexed: 02/05/2024]
Abstract
PURPOSE To assess the inter-reader reproducibility of radiomics features on multiple MRI sequences after segmentations of colorectal liver metastases (CRLM). METHOD 30 CRLM (in 23 patients) were manually delineated by three readers on MRI before the start of chemotherapy on the contrast enhanced T1-weighted images (CE-T1W) in the portal venous phase, T2-weighted images (T2W) and b800 diffusion weighted images (DWI). DWI delineations were copied to the ADC-maps. 107 radiomics features were extracted per sequence. The intraclass correlation coefficient (ICC) was calculated per feature. Features were considered reproducible if ICC > 0.9. RESULTS 90% of CE-T1W features were reproducible with a median ICC of 0.98 (range 0.76-1.00). 81% of DWI features were robust with median ICC = 0.97 (range 0.38-1.00). The T2W features had a median ICC of 0.96 (range 0.55-0.99) and were reproducible in 80%. ADC showed the lowest number of reproducible features with 58% and median ICC = 0.91 (range 0.38-0.99) When considering the lower bound of the ICC 95% confidence intervals, 58%, 66%, 54% and 29% reached 0.9 for the CE-T1W, DWI, T2W and ADC features, respectively. The feature class with the best reproducibility differed per sequence. CONCLUSIONS The majority of MRI radiomics features from CE-T1W, T2W, DWI and ADC in colorectal liver metastases were robust for segmentation variability between readers. The CE-T1W yielded slightly better reproducibility results compared to DWI and T2W. The ADC features seem more susceptible to reader differences compared to the other three sequences.
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Affiliation(s)
- Denise J van der Reijd
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands; GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands
| | - Kalina Chupetlovska
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands
| | - Eleanor van Dijk
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands
| | - Bram Westerink
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands
| | - Melanie A Monraats
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands
| | - Joost J M Van Griethuysen
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands
| | - Doenja M J Lambregts
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands; GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands
| | - Renaud Tissier
- Biostatistics Center, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands; GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands; Faculty of Health Sciences, University of Southern Denmark, Campusvej 55, DK 5203 Odense, Denmark
| | - Sean Benson
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands; Department of Cardiology, Amsterdam University Medical Centres, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, the Netherlands; GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands.
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3
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Granata V, Fusco R, De Muzio F, Brunese MC, Setola SV, Ottaiano A, Cardone C, Avallone A, Patrone R, Pradella S, Miele V, Tatangelo F, Cutolo C, Maggialetti N, Caruso D, Izzo F, Petrillo A. Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment. LA RADIOLOGIA MEDICA 2023; 128:1310-1332. [PMID: 37697033 DOI: 10.1007/s11547-023-01710-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 08/22/2023] [Indexed: 09/13/2023]
Abstract
OBJECTIVE The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated. METHODS The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed. RESULTS The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence. CONCLUSIONS The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy.
| | | | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Alessandro Ottaiano
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Claudia Cardone
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Antonio Avallone
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Renato Patrone
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Silvia Pradella
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology (SIRM), 20122, Milan, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology (SIRM), 20122, Milan, Italy
| | - Fabiana Tatangelo
- Division of Pathological Anatomy and Cytopathology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084, Salerno, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari "Aldo Moro", 70124, Bari, Italy
| | - Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, Radiology Unit-Sant'Andrea University Hospital, Sapienza-University of Rome, 00189, Rome, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
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Arias-Lorza AM, Costello JR, Hingorani SR, Von Hoff DD, Korn RL, Raghunand N. Tumor Response to Stroma-Modifying Therapy: Magnetic Resonance Imaging Findings in Early-Phase Clinical Trials of Pegvorhyaluronidase alpha (PEGPH20). RESEARCH SQUARE 2023:rs.3.rs-3314770. [PMID: 37720027 PMCID: PMC10503830 DOI: 10.21203/rs.3.rs-3314770/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Pre-clinical and clinical studies have shown that PEGPH20 depletes intratumoral hyaluronic acid (HA), which is linked to high interstitial fluid pressures and poor distribution of chemotherapies. 29 patients with metastatic advanced solid tumors received quantitative magnetic resonance imaging (qMRI) in 3 prospective clinical trials of PEGPH20, HALO-109-101 (NCT00834704), HALO-109-102 (NCT01170897), and HALO-109-201 (NCT01453153). Apparent Diffusion Coefficient of water (ADC), T1, ktrans, vp, ve, and iAUC maps were computed from qMRI acquired at baseline and ≥ 1 time point post-PEGPH20. Tumor ADC and T1 decreased, while iAUC, ktrans, vp, and ve increased, on day 1 post-PEGPH20 relative to baseline values. This is consistent with HA depletion leading to a decrease in tumor water content and an increase in perfusion, permeability, extracellular matrix space, and vascularity. Baseline parameter values that were predictive of pharmacodynamic responses were: ADC > 1.46×10-3 mm2/s (Balanced Accuracy (BA) = 72%, p < 0.01), T1 > 0.54s (BA = 82%, p < 0.01), iAUC < 9.2 mM-s (BA = 76%, p < 0.05), ktrans<0.07min-1 (BA = 72%, p = 0.2), ve<0.17 (BA = 68%, p < 0.01), and vp<0.02 (BA = 60%, p < 0.01). Further, ve<0.39 at baseline was moderately predictive of response in any parameter (BA = 65.6%, p < 0.01 averaged across patients). These qMRI biomarkers are potentially useful for guiding patient pre-selection and post-treatment follow-up in future clinical studies of PEGPH20 and other tumor stroma-modifying anti-cancer therapies.
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Sherminie LPG, Jayatilake ML, Hewavithana B, Weerakoon BS, Vijithananda SM. Morphometry-based radiomics for predicting therapeutic response in patients with gliomas following radiotherapy. Front Oncol 2023; 13:1139902. [PMID: 37664038 PMCID: PMC10470056 DOI: 10.3389/fonc.2023.1139902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 07/31/2023] [Indexed: 09/05/2023] Open
Abstract
Introduction Gliomas are still considered as challenging in oncologic management despite the developments in treatment approaches. The complete elimination of a glioma might not be possible even after a treatment and assessment of therapeutic response is important to determine the future course of actions for patients with such cancers. In the recent years radiomics has emerged as a promising solution with potential applications including prediction of therapeutic response. Hence, this study was focused on investigating whether morphometry-based radiomics signature could be used to predict therapeutic response in patients with gliomas following radiotherapy. Methods 105 magnetic resonance (MR) images including segmented and non-segmented images were used to extract morphometric features and develop a morphometry-based radiomics signature. After determining the appropriate machine learning algorithm, a prediction model was developed to predict the therapeutic response eliminating the highly correlated features as well as without eliminating the highly correlated features. Then the model performance was evaluated. Results Tumor grade had the highest contribution to develop the morphometry-based signature. Random forest provided the highest accuracy to train the prediction model derived from the morphometry-based radiomics signature. An accuracy of 86% and area under the curve (AUC) value of 0.91 were achieved for the prediction model evaluated without eliminating the highly correlated features whereas accuracy and AUC value were 84% and 0.92 respectively for the prediction model evaluated after eliminating the highly correlated features. Discussion Nonetheless, the developed morphometry-based radiomics signature could be utilized as a noninvasive biomarker for therapeutic response in patients with gliomas following radiotherapy.
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Affiliation(s)
- Lahanda Purage G. Sherminie
- Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka
| | - Mohan L. Jayatilake
- Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka
| | - Badra Hewavithana
- Department of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka
| | - Bimali S. Weerakoon
- Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka
| | - Sahan M. Vijithananda
- Department of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka
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Granata V, Fusco R, Setola SV, Galdiero R, Maggialetti N, Patrone R, Ottaiano A, Nasti G, Silvestro L, Cassata A, Grassi F, Avallone A, Izzo F, Petrillo A. Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics. Infect Agent Cancer 2023; 18:18. [PMID: 36927442 PMCID: PMC10018963 DOI: 10.1186/s13027-023-00495-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 03/07/2023] [Indexed: 03/18/2023] Open
Abstract
In this narrative review, we reported un up-to-date on the role of radiomics to assess prognostic features, which can impact on the liver metastases patient treatment choice. In the liver metastases patients, the possibility to assess mutational status (RAS or MSI), the tumor growth pattern and the histological subtype (NOS or mucinous) allows a better treatment selection to avoid unnecessary therapies. However, today, the detection of these features require an invasive approach. Recently, radiomics analysis application has improved rapidly, with a consequent growing interest in the oncological field. Radiomics analysis allows the textural characteristics assessment, which are correlated to biological data. This approach is captivating since it should allow to extract biological data from the radiological images, without invasive approach, so that to reduce costs and time, avoiding any risk for the patients. Several studies showed the ability of Radiomics to identify mutational status, tumor growth pattern and histological type in colorectal liver metastases. Although, radiomics analysis in a non-invasive and repeatable way, however features as the poor standardization and generalization of clinical studies results limit the translation of this analysis into clinical practice. Clear limits are data-quality control, reproducibility, repeatability, generalizability of results, and issues related to model overfitting.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy.
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, Napoli, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, Milan, 20122, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Roberta Galdiero
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari "Aldo Moro", Bari, 70124, Italy
| | - Renato Patrone
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, 80131, Italy
| | - Alessandro Ottaiano
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Guglielmo Nasti
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Lucrezia Silvestro
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Antonio Cassata
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Francesca Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, 80138, Italy
| | - Antonio Avallone
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, 80131, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
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Abstract
Computer-extracted tumour characteristics have been incorporated into medical imaging computer-aided diagnosis (CAD) algorithms for decades. With the advent of radiomics, an extension of CAD involving high-throughput computer-extracted quantitative characterization of healthy or pathological structures and processes as captured by medical imaging, interest in such computer-extracted measurements has increased substantially. However, despite the thousands of radiomic studies, the number of settings in which radiomics has been successfully translated into a clinically useful tool or has obtained FDA clearance is comparatively small. This relative dearth might be attributable to factors such as the varying imaging and radiomic feature extraction protocols used from study to study, the numerous potential pitfalls in the analysis of radiomic data, and the lack of studies showing that acting upon a radiomic-based tool leads to a favourable benefit-risk balance for the patient. Several guidelines on specific aspects of radiomic data acquisition and analysis are already available, although a similar roadmap for the overall process of translating radiomics into tools that can be used in clinical care is needed. Herein, we provide 16 criteria for the effective execution of this process in the hopes that they will guide the development of more clinically useful radiomic tests in the future.
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8
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Radiomics for Discriminating Benign and Malignant Salivary Gland Tumors; Which Radiomic Feature Categories and MRI Sequences Should Be Used? Cancers (Basel) 2022; 14:cancers14235804. [PMID: 36497285 PMCID: PMC9740105 DOI: 10.3390/cancers14235804] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 11/12/2022] [Accepted: 11/22/2022] [Indexed: 11/26/2022] Open
Abstract
The lack of a consistent MRI radiomic signature, partly due to the multitude of initial feature analyses, limits the widespread clinical application of radiomics for the discrimination of salivary gland tumors (SGTs). This study aimed to identify the optimal radiomics feature category and MRI sequence for characterizing SGTs, which could serve as a step towards obtaining a consensus on a radiomics signature. Preliminary radiomics models were built to discriminate malignant SGTs (n = 34) from benign SGTs (n = 57) on T1-weighted (T1WI), fat-suppressed (FS)-T2WI and contrast-enhanced (CE)-T1WI images using six feature categories. The discrimination performances of these preliminary models were evaluated using 5-fold-cross-validation with 100 repetitions and the area under the receiver operating characteristic curve (AUC). The differences between models’ performances were identified using one-way ANOVA. Results show that the best feature categories were logarithm for T1WI and CE-T1WI and exponential for FS-T2WI, with AUCs of 0.828, 0.754 and 0.819, respectively. These AUCs were higher than the AUCs obtained using all feature categories combined, which were 0.750, 0.707 and 0.774, respectively (p < 0.001). The highest AUC (0.846) was obtained using a combination of T1WI + logarithm and FS-T2WI + exponential features, which reduced the initial features by 94.0% (from 1015 × 3 to 91 × 2). CE-T1WI did not improve performance. Using one feature category rather than all feature categories combined reduced the number of initial features without compromising radiomic performance.
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9
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Scalco E, Rizzo G, Mastropietro A. The stability of oncologic MRI radiomic features and the potential role of deep learning: a review. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac60b9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 03/24/2022] [Indexed: 11/11/2022]
Abstract
Abstract
The use of MRI radiomic models for the diagnosis, prognosis and treatment response prediction of tumors has been increasingly reported in literature. However, its widespread adoption in clinics is hampered by issues related to features stability. In the MRI radiomic workflow, the main factors that affect radiomic features computation can be found in the image acquisition and reconstruction phase, in the image pre-processing steps, and in the segmentation of the region of interest on which radiomic indices are extracted. Deep Neural Networks (DNNs), having shown their potentiality in the medical image processing and analysis field, can be seen as an attractive strategy to partially overcome the issues related to radiomic stability and mitigate their impact. In fact, DNN approaches can be prospectively integrated in the MRI radiomic workflow to improve image quality, obtain accurate and reproducible segmentations and generate standardized images. In this review, DNN methods that can be included in the image processing steps of the radiomic workflow are described and discussed, in the light of a detailed analysis of the literature in the context of MRI radiomic reliability.
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10
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Tharmalingam H, Tsang Y, Alonzi R, Beasley W, Taylor N, McWilliam A, Padhani A, Choudhury A, Hoskin P. Changes in Magnetic Resonance Imaging Radiomic Features in Response to Androgen Deprivation Therapy in Patients with Intermediate- and High-risk Prostate Cancer. Clin Oncol (R Coll Radiol) 2022; 34:e246-e253. [DOI: 10.1016/j.clon.2021.12.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 11/26/2021] [Accepted: 12/22/2021] [Indexed: 11/03/2022]
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11
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Shur JD, Doran SJ, Kumar S, Ap Dafydd D, Downey K, O'Connor JPB, Papanikolaou N, Messiou C, Koh DM, Orton MR. Radiomics in Oncology: A Practical Guide. Radiographics 2021; 41:1717-1732. [PMID: 34597235 PMCID: PMC8501897 DOI: 10.1148/rg.2021210037] [Citation(s) in RCA: 126] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Radiomics refers to the extraction of mineable data from medical imaging
and has been applied within oncology to improve diagnosis,
prognostication, and clinical decision support, with the goal of
delivering precision medicine. The authors provide a practical approach
for successfully implementing a radiomic workflow from planning and
conceptualization through manuscript writing. Applications in oncology
typically are either classification tasks that involve computing the
probability of a sample belonging to a category, such as benign versus
malignant, or prediction of clinical events with a time-to-event
analysis, such as overall survival. The radiomic workflow is
multidisciplinary, involving radiologists and data and imaging
scientists, and follows a stepwise process involving tumor segmentation,
image preprocessing, feature extraction, model development, and
validation. Images are curated and processed before segmentation, which
can be performed on tumors, tumor subregions, or peritumoral zones.
Extracted features typically describe the distribution of signal
intensities and spatial relationship of pixels within a region of
interest. To improve model performance and reduce overfitting, redundant
and nonreproducible features are removed. Validation is essential to
estimate model performance in new data and can be performed iteratively
on samples of the dataset (cross-validation) or on a separate hold-out
dataset by using internal or external data. A variety of noncommercial
and commercial radiomic software applications can be used. Guidelines
and artificial intelligence checklists are useful when planning and
writing up radiomic studies. Although interest in the field continues to
grow, radiologists should be familiar with potential pitfalls to ensure
that meaningful conclusions can be drawn. Online supplemental material is available for this
article. Published under a CC BY 4.0 license.
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Affiliation(s)
- Joshua D Shur
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
| | - Simon J Doran
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
| | - Santosh Kumar
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
| | - Derfel Ap Dafydd
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
| | - Kate Downey
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
| | - James P B O'Connor
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
| | - Nikolaos Papanikolaou
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
| | - Christina Messiou
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
| | - Dow-Mu Koh
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
| | - Matthew R Orton
- From the Department of Radiology, Royal Marsden Hospital NHS Foundation Trust, Sutton, England (J.D.S., D.a.D., K.D., N. P., C.M., D.M.K.); Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, England (S.J.D., S.K., J.P.B.O., N. P., C.M., D.M.K., M.R.O.); and Computational Clinical Imaging Group, Champalimaud Foundation, Centre for the Unknown, Lisbon, Portugal (N.P.)
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