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Xin Z, Yan W, Feng Y, Yunzhi L, Zhang Y, Wang D, Chen W, Peng J, Guo C, Chen Z, Wang X, Zhu J, Lei J. An MRI-based machine learning radiomics can predict short-term response to neoadjuvant chemotherapy in patients with cervical squamous cell carcinoma: A multicenter study. Cancer Med 2023; 12:19383-19393. [PMID: 37772478 PMCID: PMC10587964 DOI: 10.1002/cam4.6525] [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: 03/30/2023] [Revised: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 09/30/2023] Open
Abstract
BACKGROUND AND PURPOSE Neoadjuvant chemotherapy (NACT) has become an essential component of the comprehensive treatment of cervical squamous cell carcinoma (CSCC). However, not all patients respond to chemotherapy due to individual differences in sensitivity and tolerance to chemotherapy drugs. Therefore, accurately predicting the sensitivity of CSCC patients to NACT was vital for individual chemotherapy. This study aims to construct a machine learning radiomics model based on magnetic resonance imaging (MRI) to assess its efficacy in predicting NACT susceptibility among CSCC patients. METHODS This study included 234 patients with CSCC from two hospitals, who were divided into a training set (n = 180), a testing set (n = 20), and an external validation set (n = 34). Manual radiomic features were extracted from transverse section MRI images, and feature selection was performed using the recursive feature elimination (RFE) method. A prediction model was then generated using three machine learning algorithms, namely logistic regression, random forest, and support vector machines (SVM), for predicting NACT susceptibility. The model's performance was assessed based on the area under the receiver operating characteristic curve (AUC), accuracy, and sensitivity. RESULTS The SVM approach achieves the highest scores on both the testing set and the external validation set. In the testing set and external validation set, the AUC of the model was 0.88 and 0.764, and the accuracy was 0.90 and 0.853, the sensitivity was 0.93 and 0.962, respectively. CONCLUSIONS Machine learning radiomics models based on MRI images have achieved satisfactory performance in predicting the sensitivity of NACT in CSCC patients with high accuracy and robustness, which has great significance for the treatment and personalized medicine of CSCC patients.
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Affiliation(s)
- Zhonghong Xin
- Department of RadiologyThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Wanying Yan
- Infervision Medical Technology Co., LtdBeijingChina
| | - Yibo Feng
- Infervision Medical Technology Co., LtdBeijingChina
| | - Li Yunzhi
- Department of RadiologyGansu Provincial Maternity and Child‐care HospitalLanzhouChina
| | - Yaping Zhang
- Department of RadiologyThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Dawei Wang
- Infervision Medical Technology Co., LtdBeijingChina
| | - Weidao Chen
- Infervision Medical Technology Co., LtdBeijingChina
| | - Jianhong Peng
- Department of RadiologyThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Cheng Guo
- Department of RadiologyThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Zixian Chen
- Department of RadiologyThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Xiaohui Wang
- Department of Gynecology and ObstetricsThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Jun Zhu
- Department of PathologyThe First Hospital of Lanzhou UniversityLanzhouChina
| | - Junqiang Lei
- Department of RadiologyThe First Hospital of Lanzhou UniversityLanzhouChina
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Abdul-Latif M, Tharmalingam H, Tsang Y, Hoskin PJ. Functional Magnetic Resonance Imaging in Cervical Cancer Diagnosis and Treatment. Clin Oncol (R Coll Radiol) 2023; 35:598-610. [PMID: 37246040 DOI: 10.1016/j.clon.2023.05.006] [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: 04/12/2023] [Accepted: 05/12/2023] [Indexed: 05/30/2023]
Abstract
Cervical Cancer is the fourth most common cancer in women worldwide. Treatment with chemoradiotherapy followed by brachytherapy achieves high local control, but recurrence with metastatic disease impacts survival. This highlights the need for predictive and prognostic biomarkers identifying populations at risk of poorer treatment response and survival. Magnetic resonance imaging (MRI) is routinely used in cervical cancer and is a potential source for biomarkers. Functional MRI (fMRI) can characterise tumour beyond anatomical MRI, which is limited to the assessment of morphology. This review summarises fMRI techniques used in cervical cancer and examines the role of fMRI parameters as predictive or prognostic biomarkers. Different techniques characterise different tumour factors, which helps to explain the variation in patient outcomes. These can impact simultaneously on outcomes, making biomarker identification challenging. Most studies are small, focussing on single MRI techniques, which raises the need to investigate combined fMRI approaches for a more holistic characterisation of tumour.
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Affiliation(s)
| | | | - Y Tsang
- Mount Vernon Cancer Centre, Northwood, UK; Radiation Medicine Programme, Princess Margaret Cancer Centre, Toronto, Canada
| | - P J Hoskin
- Mount Vernon Cancer Centre, Northwood, UK; Division of Cancer Sciences, University of Manchester, Manchester, UK
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Granata V, Fusco R, Setola SV, Cozzi D, Rega D, Petrillo A. Diffusion and Perfusion Imaging in Rectal Cancer Restaging. Semin Ultrasound CT MR 2023; 44:117-125. [PMID: 37245878 DOI: 10.1053/j.sult.2023.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
The assessment of tumor response, after neoadjuvant radiochemotherapy (n-CRT), permits the stratification of patients for the proper therapeutical management. Although histopathology analysis of the surgical speciemen is considered the gold standard for assessing tumor response, magnetic resonance imaging (MRI), with its significant developments in technical imaging, have allowed an increase in accuracy for the evaluation of response. MRI provides a radiological tumor regression grade (mrTRG) that is correlated with the pathologic tumor regression grade (pTRG). Functional MRI parameters have additional impending in early prediction of the efficacy of therapy. Some of functional methodologies are already part of clinical practice: diffusion-weighted MRI (DW-MRI) and perfusion imaging (dynamic contrast enhanced MRI [DCE-MRI]).
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | | | - Sergio Venazio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Diletta Cozzi
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy; Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
| | - Daniela Rega
- Division of Gastrointestinal Surgical Oncology, "Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale", Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
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Ota T, Tsuboyama T, Onishi H, Nakamoto A, Fukui H, Yano K, Honda T, Kiso K, Tatsumi M, Tomiyama N. Diagnostic accuracy of MRI for evaluating myometrial invasion in endometrial cancer: a comparison of MUSE-DWI, rFOV-DWI, and DCE-MRI. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01635-4. [PMID: 37120661 DOI: 10.1007/s11547-023-01635-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/20/2023] [Indexed: 05/01/2023]
Abstract
OBJECTIVES To compare the image quality of high-resolution diffusion-weighted imaging (DWI) using multiplexed sensitivity encoding (MUSE) versus reduced field-of-view (rFOV) techniques in endometrial cancer (EC) and to compare the diagnostic performance of these techniques with that of dynamic contrast-enhanced (DCE) MRI for assessing myometrial invasion of EC. METHODS MUSE-DWI and rFOV-DWI were obtained preoperatively in 58 women with EC. Three radiologists assessed the image quality of MUSE-DWI and rFOV-DWI. For 55 women who underwent DCE-MRI, the same radiologists assessed the superficial and deep myometrial invasion using MUSE-DWI, rFOV-DWI, and DCE-MRI. Qualitative scores were compared using the Wilcoxon signed-rank test. Receiver operating characteristic analysis was performed to compare the diagnostic performance. RESULTS Artifacts, sharpness, lesion conspicuity, and overall quality were significantly better with MUSE-DWI than with rFOV-DWI (p < 0.05). The area under the curve (AUC) of MUSE-DWI, rFOV-DWI, and DCE-MRI for the assessment of myometrial invasion were not significantly different except for significantly higher AUC of MUSE-DWI than that of DCE-MRI for superficial myometrial invasion (0.76 for MUSE-DWI and 0.64 for DCE-MRI, p = 0.049) and for deep myometrial invasion (0.92 for MUSE-DWI and 0.80 for DCE-MRI, p = 0.022) in one observer, and that of rFOV-DWI for deep myometrial invasion in another observer (0.96 for MUSE-DWI and 0.89 for rFOV-MRI, p = 0.048). CONCLUSION MUSE-DWI exhibits better image quality than rFOV-DWI. MUSE-DWI and rFOV-DWI shows almost equivalent diagnostic performance compared to DCE-MRI for assessing superficial and deep myometrial invasion in EC although MUSE-DWI may be helpful for some radiologists.
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Affiliation(s)
- Takashi Ota
- Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, D1, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan.
| | - Takahiro Tsuboyama
- Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, D1, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Hiromitsu Onishi
- Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, D1, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Atsushi Nakamoto
- Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, D1, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Hideyuki Fukui
- Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, D1, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Keigo Yano
- Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, D1, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Toru Honda
- Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, D1, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Kengo Kiso
- Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, D1, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Mitsuaki Tatsumi
- Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, D1, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Noriyuki Tomiyama
- Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, D1, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
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Nardini M, Capotosti A, Mazzoni LN, Cusumano D, Boldrini L, Chiloiro G, Romano A, Valentini V, Indovina L, Placidi L. Tuning the optimal diffusion-weighted MRI parameters on a 0.35-T MR-Linac for clinical implementation: A phantom study. Front Oncol 2022; 12:867792. [PMID: 36523999 PMCID: PMC9745186 DOI: 10.3389/fonc.2022.867792] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 11/07/2022] [Indexed: 12/06/2023] Open
Abstract
PURPOSE This study aims to assess the quality of a new diffusion-weighted imaging (DWI) sequence implemented on an MR-Linac MRIdian system, evaluating and optimizing the acquisition parameters to explore the possibility of clinically implementing a DWI acquisition protocol in a 0.35-T MR-Linac. MATERIALS AND METHODS All the performed analyses have been carried out on two types of phantoms: a homogeneous 24-cm diameter polymethylmethacrylate (PMMA) sphere (SP) and a homemade phantom (HMP) constating in a PMMA cylinder filled with distilled water with empty sockets into which five cylindrical vials filled with five different concentrations of methylcellulose water solutions have been inserted. SP was used to evaluate the dependence of diffusion gradient inhomogeneity artifacts on gantry position. Four diffusion sequences with b-values of 500 s/mm2 and 3 averages have been acquired: three with diffusion gradients in the three main directions (phase direction, read direction, slice direction) and one with the diffusion gradients switched off. The dependence of diffusion image uniformity and SNR on the number of averages in the MR sequences was also investigated to determine the optimal number of averages. Finally, the ADC values of HMP have been computed and then compared between images acquired in the scanners at 0.35 and 1.5 T. RESULTS In order to acquire high-quality artifact-free DWI images, the "slice" gradient direction has been identified to be the optimal one and 0° to be the best gradient angle. Both the SNR ratio and the uniformity increase with the number of averages. A threshold value of 80 for SNR and 85% for uniformity was adopted to choose the best number of averages. By making a compromise between time and quality and limiting the number of b-values, it is possible to reduce the acquisition time to 78 s. The Passing-Bablok test showed that the two methods, with 0.35 and 1.5 T scanners, led to similar results. CONCLUSION The quality of the DWI has been accurately evaluated in relation to different sequence parameters, and optimal parameters have been identified to select a clinical protocol for the acquisition of ADC maps sustainable in the workflow of a hybrid radiotherapy system with a 0.35-T MRI scanner.
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Affiliation(s)
- Matteo Nardini
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Amedeo Capotosti
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Lorenzo Nicola Mazzoni
- Azienda Unità Sanitaria Locale (AUSL) Toscana Centro, Medical Physics Unit, Prato-Pistoia, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
- Mater Olbia Hospital, UOS Fisica Medica, Olbia, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Giuditta Chiloiro
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Angela Romano
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Luca Indovina
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
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Francolini G, Morelli I, Carnevale MG, Grassi R, Nardone V, Loi M, Valzano M, Salvestrini V, Livi L, Desideri I. Integration between Novel Imaging Technologies and Modern Radiotherapy Techniques: How the Eye Drove the Chisel. Cancers (Basel) 2022; 14:5277. [PMID: 36358695 PMCID: PMC9656145 DOI: 10.3390/cancers14215277] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 10/17/2022] [Accepted: 10/24/2022] [Indexed: 03/12/2024] Open
Abstract
INTRODUCTION Targeted dose-escalation and reduction of dose to adjacent organs at risk have been the main goal of radiotherapy in the last decade. Prostate cancer benefited the most from this process. In recent years, the development of Intensity Modulated Radiation Therapy (IMRT) and Stereotactic Body Radiotherapy (SBRT) radically changed clinical practice, also thanks to the availability of modern imaging techniques. The aim of this paper is to explore the relationship between diagnostic imaging and prostate cancer radiotherapy techniques. MATERIALS AND METHODS Aiming to provide an overview of the integration between modern imaging and radiotherapy techniques, we performed a non-systematic search of papers exploring the predictive value of imaging before treatment, the role of radiomics in predicting treatment outcomes, implementation of novel imaging in RT planning and influence of imaging integration on use of RT in current clinical practice. Three independent authors (GF, IM and ID) performed an independent review focusing on these issues. Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used, and grey literature was searched for further papers of interest. The final choice of papers included was discussed between all co-authors. RESULTS This paper contains a narrative report and a critical discussion of the role of new modern techniques in predicting outcomes before treatment, in radiotherapy planning and in the integration with systemic therapy in the management of prostate cancer. Also, the role of radiomics in a tailored treatment approach is explored. CONCLUSIONS Integration between diagnostic imaging and radiotherapy is of great importance for the modern treatment of prostate cancer. Future clinical trials should be aimed at exploring the real clinical benefit of complex workflows in clinical practice.
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Affiliation(s)
- Giulio Francolini
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
| | - Ilaria Morelli
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy
| | - Maria Grazia Carnevale
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Valerio Nardone
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Mauro Loi
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
| | - Marianna Valzano
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy
| | - Viola Salvestrini
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy
| | - Lorenzo Livi
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy
| | - Isacco Desideri
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy
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MRI Using Artificial Intelligence Algorithm to Evaluate Concurrent Chemoradiotherapy for Local Recurrence and Distant Metastasis of Cervical Squamous Cell Carcinoma. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4449696. [PMID: 35936360 PMCID: PMC9352503 DOI: 10.1155/2022/4449696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 06/21/2022] [Accepted: 06/27/2022] [Indexed: 11/17/2022]
Abstract
The aim of this study was to investigate the magnetic resonance imaging (MRI) features of patients with local recurrence and distant metastasis of cervical squamous cell carcinoma before and after concurrent chemoradiotherapy based on artificial intelligence algorithm. In this study, 100 patients with cervical squamous cell carcinoma with local recurrence and distant metastasis who underwent concurrent chemoradiotherapy were collected as the research subjects, and all underwent MRI multisequence imaging scans. At the same time, according to the evaluation criteria of solid tumor efficacy, patients with complete remission were classified into the effective group, and patients with partial remission, progressive disease, and stable disease were classified into the ineffective group. In addition, an image segmentation algorithm based on Balloon Snake model was proposed for MRI image processing, and simulation experiments were carried out. The results showed that the Dice coefficient of the proposed model segmentation of the reconstructed image was significantly higher than that of the level set model and the greedy algorithm, while the running time was the opposite (
). The lesion volume (
cm3) in the effective group after treatment was significantly smaller than that in the noneffective group (
cm3), and the rate of lesion volume shrinkage (28.71%) was significantly larger than that in the noneffective group (12.49%) (
). The relative apparent diffusion coefficient (rADC) value and rADC value change rate of the lesion after treatment in the effective group were significantly greater than those in the noneffective group (
). In summary, the image segmentation and reconstruction algorithm based on Balloon Snake model can not only improve the quality of MRI images but also shorten the processing time and improve the diagnostic efficiency. The volume regression rate and rADC value change rate of cervical squamous cell carcinoma lesion can reflect the early efficacy of concurrent chemoradiotherapy for cervical squamous cell carcinoma and have predictive value.
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Multimodality Imaging Assessment of Desmoid Tumors: The Great Mime in the Era of Multidisciplinary Teams. J Pers Med 2022; 12:jpm12071153. [PMID: 35887650 PMCID: PMC9319486 DOI: 10.3390/jpm12071153] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/14/2022] [Accepted: 07/15/2022] [Indexed: 11/17/2022] Open
Abstract
Desmoid tumors (DTs), also known as desmoid fibromatosis or aggressive fibromatosis, are rare, locally invasive, non-metastatic soft tissue tumors. Although histological results represent the gold standard diagnosis, imaging represents the fundamental tool for the diagnosis of these tumors. Although histological analysis represents the gold standard for diagnosis, imaging represents the fundamental tool for the diagnosis of these tumors. DTs represent a challenge for the radiologist, being able to mimic different pathological conditions. A proper diagnosis is required to establish an adequate therapeutic approach. Multimodality imaging, including ultrasound (US), computed tomography (CT) and Magnetic Resonance Imaging (MRI), should be preferred. Different imaging techniques can also guide minimally invasive treatments and monitor their effectiveness. The purpose of this review is to describe the state-of-the-art multidisciplinary imaging of DTs; and its role in patient management.
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Granata V, Fusco R, Belli A, Danti G, Bicci E, Cutolo C, Petrillo A, Izzo F. Diffusion weighted imaging and diffusion kurtosis imaging in abdominal oncological setting: why and when. Infect Agent Cancer 2022; 17:25. [PMID: 35681237 PMCID: PMC9185934 DOI: 10.1186/s13027-022-00441-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 05/30/2022] [Indexed: 12/13/2022] Open
Abstract
This article provides an overview of diffusion kurtosis (DKI) imaging in abdominal oncology. DKI allows for more data on tissue structures than the conventional diffusion model (DWI). However, DKI requires high quality images at b-values greater than 1000 s/mm2 and high signal-to-noise ratio (SNR) that traditionally MRI systems are not able to acquire and therefore there are generally amplified anatomical distortions on the images due to less homogeneity of the field. Advances in both hardware and software on modern MRI scanners have currently enabled ultra-high b-value imaging and offered the ability to apply DKI to multiple extracranial sites. Previous studies have evaluated the ability of DKI to characterize and discriminate tumor grade compared to conventional DWI. Additionally, in several studies the DKI sequences used were based on planar echo (EPI) acquisition, which is susceptible to motion, metal and air artefacts and prone to low SNRs and distortions, leading to low quality images for some small lesions, which may affect the accuracy of the results. Another problem is the optimal b-value of DKI, which remains to be explored and not yet standardized, as well as the manual selection of the ROI, which could affect the accuracy of some parameters.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy.
| | | | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy
| | - Ginevra Danti
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology, SIRM Foundation, Milan, Italy
| | - Eleonora Bicci
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Salerno, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", I-80131, Naples, Italy
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10
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Granata V, Fusco R, Belli A, Borzillo V, Palumbo P, Bruno F, Grassi R, Ottaiano A, Nasti G, Pilone V, Petrillo A, Izzo F. Conventional, functional and radiomics assessment for intrahepatic cholangiocarcinoma. Infect Agent Cancer 2022; 17:13. [PMID: 35346300 PMCID: PMC8961950 DOI: 10.1186/s13027-022-00429-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 03/18/2022] [Indexed: 02/08/2023] Open
Abstract
Background This paper offers an assessment of diagnostic tools in the evaluation of Intrahepatic Cholangiocarcinoma (ICC). Methods Several electronic datasets were analysed to search papers on morphological and functional evaluation in ICC patients. Papers published in English language has been scheduled from January 2010 to December 2021.
Results We found that 88 clinical studies satisfied our research criteria. Several functional parameters and morphological elements allow a truthful ICC diagnosis. The contrast medium evaluation, during the different phases of contrast studies, support the recognition of several distinctive features of ICC. The imaging tool to employed and the type of contrast medium in magnetic resonance imaging, extracellular or hepatobiliary, should change considering patient, departement, and regional features. Also, Radiomics is an emerging area in the evaluation of ICCs. Post treatment studies are required to evaluate the efficacy and the safety of therapies so as the patient surveillance. Conclusions Several morphological and functional data obtained during Imaging studies allow a truthful ICC diagnosis.
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Autorino R, Gui B, Panza G, Boldrini L, Cusumano D, Russo L, Nardangeli A, Persiani S, Campitelli M, Ferrandina G, Macchia G, Valentini V, Gambacorta MA, Manfredi R. Radiomics-based prediction of two-year clinical outcome in locally advanced cervical cancer patients undergoing neoadjuvant chemoradiotherapy. Radiol Med 2022; 127:498-506. [PMID: 35325372 PMCID: PMC9098600 DOI: 10.1007/s11547-022-01482-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/08/2022] [Indexed: 12/31/2022]
Abstract
PURPOSE The aim of this study is to determine if radiomics features extracted from staging magnetic resonance (MR) images could predict 2-year long-term clinical outcome in patients with locally advanced cervical cancer (LACC) after neoadjuvant chemoradiotherapy (NACRT). MATERIALS AND METHODS We retrospectively enrolled patients with LACC diagnosis who underwent NACRT followed by radical surgery in two different institutions. Radiomics features were extracted from pre-treatment 1.5 T T2w MR images. The predictive performance of each feature was quantified in terms of Wilcoxon-Mann-Whitney test. Among the significant features, Pearson correlation coefficient (PCC) was calculated to quantify the correlation among the different predictors. A logistic regression model was calculated considering the two most significant features at the univariate analysis showing the lowest PCC value. The predictive performance of the model created was quantified out using the area under the receiver operating characteristic curve (AUC). RESULTS A total of 175 patients were retrospectively enrolled (142 for the training cohort and 33 for the validation one). 1896 radiomic feature were extracted, 91 of which showed significance (p < 0.05) at the univariate analysis. The radiomic model showing the highest predictive value combined the features calculated starting from the gray level co-occurrence-based features. This model achieved an AUC of 0.73 in the training set and 0.91 in the validation set. CONCLUSIONS The proposed radiomic model showed promising performances in predicting 2-year overall survival before NACRT. Nevertheless, the observed results should be tested in larger studies with consistent external validation cohorts, to confirm their potential clinical use.
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Affiliation(s)
- Rosa Autorino
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy
| | - Benedetta Gui
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy
| | - Giulia Panza
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy.
| | - Luca Boldrini
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy.,Mater Olbia Hospital, 07026, Olbia, SS, Italy
| | - Luca Russo
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy
| | - Alessia Nardangeli
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy
| | - Salvatore Persiani
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Maura Campitelli
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy
| | - Gabriella Ferrandina
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy
| | - Gabriella Macchia
- Gemelli Molise Hospital, Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy.,Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Maria Antonietta Gambacorta
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy.,Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Riccardo Manfredi
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168, Roma, Italy.,Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
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