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Holthuis EI, Heins MJ, van Houdt WJ, Haas RL, Overbeek JA, Olde Hartman TC, Uijen AA, Wee L, van der Graaf WTA, Husson O. Improving Diagnosis and Care for Patients With Sarcoma: Do Real-World General Practitioners Data and Prospective Data Collections Have a Place Next to Clinical Trials? JCO Clin Cancer Inform 2024; 8:e2400054. [PMID: 38950319 DOI: 10.1200/cci.24.00054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 04/16/2024] [Accepted: 05/10/2024] [Indexed: 07/03/2024] Open
Abstract
There has been growing interest in the use of real-world data (RWD) to address clinically and policy-relevant (research) questions that cannot be answered with data from randomized controlled trials (RCTs) alone. This is, for example, the case in rare malignancies such as sarcomas as limited patient numbers pose challenges in conducting RCTs within feasible timeliness, a manageable number of collaborators, and statistical power. This narrative review explores the potential of RWD to generate real-world evidence (RWE) in sarcoma research, elucidating its application across different phases of the patient journey, from prediagnosis to the follow-up/survivorship phase. For instance, examining electronic health records (EHRs) from general practitioners (GPs) enables the exploration of consultation frequency and presenting symptoms in primary care before a sarcoma diagnosis. In addition, alternative study designs that integrate RWD with well-designed observational RCTs may offer relevant information on the effectiveness of clinical treatments. As, especially in cases of ultrarare sarcomas, it can be an extreme challenge to perform well-powered randomized prospective studies. Therefore, it is crucial to support the adaptation of novel study designs. Regarding the follow-up/survivorship phase, examining EHR from primary and secondary care can provide valuable insights into identifying the short- and long-term effects of treatment over an extended follow-up period. The utilization of RWD also comes with several challenges, including issues related to data quality and privacy, as described in this study. Notwithstanding these challenges, this study underscores the potential of RWD to bridge, at least partially, gaps between evidence and practice and holds promise in contributing to the improvement of sarcoma care.
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Affiliation(s)
- Emily I Holthuis
- Medical Oncology Department, NKI-AVL-Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Marianne J Heins
- Netherlands Institute for Health Services Research (Nivel), Utrecht, the Netherlands
| | - Winan J van Houdt
- Surgical Oncology Department, NKI-AVL-Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Rick L Haas
- Department of Radiotherapy, NKI-AVL-Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Jetty A Overbeek
- PHARMO Institute for Drug Outcomes Research, Utrecht, the Netherlands
| | - Tim C Olde Hartman
- Department of Primary and Community Care, Radboud University Medical Center, Radboud Institute of Medical Innovation, Nijmegen, the Netherlands
| | - Annemarie A Uijen
- Department of Primary and Community Care, Radboud University Medical Center, Radboud Institute of Medical Innovation, Nijmegen, the Netherlands
| | - Leonard Wee
- School for Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, the Netherlands
| | - Winette T A van der Graaf
- Medical Oncology Department, NKI-AVL-Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
- Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Olga Husson
- Medical Oncology Department, NKI-AVL-Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands
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Jansma CYMN, Wan X, Acem I, Spaanderman DJ, Visser JJ, Hanff D, Taal W, Verhoef C, Klein S, Martin E, Starmans MPA. Preoperative Classification of Peripheral Nerve Sheath Tumors on MRI Using Radiomics. Cancers (Basel) 2024; 16:2039. [PMID: 38893158 PMCID: PMC11170987 DOI: 10.3390/cancers16112039] [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: 04/25/2024] [Revised: 05/21/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive soft-tissue tumors prevalent in neurofibromatosis type 1 (NF1) patients, posing a significant risk of metastasis and recurrence. Current magnetic resonance imaging (MRI) imaging lacks decisiveness in distinguishing benign peripheral nerve sheath tumors (BPNSTs) and MPNSTs, necessitating invasive biopsies. This study aims to develop a radiomics model using quantitative imaging features and machine learning to distinguish MPNSTs from BPNSTs. Clinical data and MRIs from MPNST and BPNST patients (2000-2019) were collected at a tertiary sarcoma referral center. Lesions were manually and semi-automatically segmented on MRI scans, and radiomics features were extracted using the Workflow for Optimal Radiomics Classification (WORC) algorithm, employing automated machine learning. The evaluation was conducted using a 100× random-split cross-validation. A total of 35 MPNSTs and 74 BPNSTs were included. The T1-weighted (T1w) MRI radiomics model outperformed others with an area under the curve (AUC) of 0.71. The incorporation of additional MRI scans did not enhance performance. Combining T1w MRI with clinical features achieved an AUC of 0.74. Experienced radiologists achieved AUCs of 0.75 and 0.66, respectively. Radiomics based on T1w MRI scans and clinical features show some ability to distinguish MPNSTs from BPNSTs, potentially aiding in the management of these tumors.
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Affiliation(s)
- Christianne Y. M. N. Jansma
- Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Institute University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands; (I.A.); (C.V.)
- Department of Plastic and Reconstructive Surgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands;
| | - Xinyi Wan
- Department of Radiology & Nuclear Medicine, Erasmus MC Cancer Institute University Hospital Rotterdam, 3015 GD Rotterdam, The Netherlands; (X.W.); (D.J.S.); (J.J.V.); (D.H.); (S.K.); (M.P.A.S.)
| | - Ibtissam Acem
- Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Institute University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands; (I.A.); (C.V.)
| | - Douwe J. Spaanderman
- Department of Radiology & Nuclear Medicine, Erasmus MC Cancer Institute University Hospital Rotterdam, 3015 GD Rotterdam, The Netherlands; (X.W.); (D.J.S.); (J.J.V.); (D.H.); (S.K.); (M.P.A.S.)
| | - Jacob J. Visser
- Department of Radiology & Nuclear Medicine, Erasmus MC Cancer Institute University Hospital Rotterdam, 3015 GD Rotterdam, The Netherlands; (X.W.); (D.J.S.); (J.J.V.); (D.H.); (S.K.); (M.P.A.S.)
| | - David Hanff
- Department of Radiology & Nuclear Medicine, Erasmus MC Cancer Institute University Hospital Rotterdam, 3015 GD Rotterdam, The Netherlands; (X.W.); (D.J.S.); (J.J.V.); (D.H.); (S.K.); (M.P.A.S.)
| | - Walter Taal
- Department of Neurology, Erasmus MC Cancer Institute University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands;
| | - Cornelis Verhoef
- Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Institute University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands; (I.A.); (C.V.)
| | - Stefan Klein
- Department of Radiology & Nuclear Medicine, Erasmus MC Cancer Institute University Hospital Rotterdam, 3015 GD Rotterdam, The Netherlands; (X.W.); (D.J.S.); (J.J.V.); (D.H.); (S.K.); (M.P.A.S.)
| | - Enrico Martin
- Department of Plastic and Reconstructive Surgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands;
| | - Martijn P. A. Starmans
- Department of Radiology & Nuclear Medicine, Erasmus MC Cancer Institute University Hospital Rotterdam, 3015 GD Rotterdam, The Netherlands; (X.W.); (D.J.S.); (J.J.V.); (D.H.); (S.K.); (M.P.A.S.)
- Department of Pathology, Erasmus MC Cancer Institute University Hospital Rotterdam, 3015 GD Rotterdam, The Netherlands
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Zhu N, Meng X, Wang Z, Hu Y, Zhao T, Fan H, Niu F, Han J. Radiomics in Diagnosis, Grading, and Treatment Response Assessment of Soft Tissue Sarcomas: A Systematic Review and Meta-analysis. Acad Radiol 2024:S1076-6332(24)00193-4. [PMID: 38772802 DOI: 10.1016/j.acra.2024.03.029] [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: 01/20/2024] [Revised: 03/12/2024] [Accepted: 03/22/2024] [Indexed: 05/23/2024]
Abstract
RATIONALE AND OBJECTIVES To evaluate radiomics in soft tissue sarcomas (STSs) for diagnostic accuracy, grading, and treatment response assessment, with a focus on clinical relevance. METHODS In this diagnostic accuracy study, radiomics was applied using multiple MRI sequences and AI classifiers, with histopathological diagnosis as the reference standard. Statistical analysis involved meta-analysis, random-effects model, and Deeks' funnel plot asymmetry test. RESULTS Among 579 unique titles and abstracts, 24 articles were included in the systematic review, with 21 used for meta-analysis. Radiomics demonstrated a pooled sensitivity of 84% (95% CI: 80-87) and specificity of 63% (95% CI: 56-70), AUC of 0.93 for diagnosis, sensitivity of 84% (95% CI: 82-87) and specificity of 73% (95% CI: 68-77), AUC of 0.91 for grading, and sensitivity of 83% (95% CI: 67-94) and specificity of 67% (95% CI: 59-74), AUC of 0.87 for treatment response assessment. CONCLUSION Radiomics exhibits potential for accurate diagnosis, grading, and treatment response assessment in STSs, emphasizing the need for standardization and prospective trials. CLINICAL RELEVANCE STATEMENT Radiomics offers precise tools for STS diagnosis, grading, and treatment response assessment, with implications for optimizing patient care and treatment strategies in this complex malignancy.
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Affiliation(s)
- Nana Zhu
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Xianghong Meng
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China
| | - Zhi Wang
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China.
| | - Yongcheng Hu
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Tingting Zhao
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Hongxing Fan
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Feige Niu
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Jun Han
- The Department of Radiology, Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin University, Tianjin, China
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Dai X, Zhao B, Zang J, Wang X, Liu Z, Sun T, Yu H, Sui X. Diagnostic Performance of Radiomics and Deep Learning to Identify Benign and Malignant Soft Tissue Tumors: A Systematic Review and Meta-analysis. Acad Radiol 2024:S1076-6332(24)00197-1. [PMID: 38614826 DOI: 10.1016/j.acra.2024.03.033] [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: 02/25/2024] [Revised: 03/24/2024] [Accepted: 03/24/2024] [Indexed: 04/15/2024]
Abstract
RATIONALE AND OBJECTIVES To systematically evaluate the application value of radiomics and deep learning (DL) in the differential diagnosis of benign and malignant soft tissue tumors (STTs). MATERIALS AND METHODS A systematic review was conducted on studies published up to December 11, 2023, that utilized radiomics and DL methods for the diagnosis of STTs. The methodological quality and risk of bias were evaluated using the Radiomics Quality Score (RQS) 2.0 system and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool, respectively. A bivariate random-effects model was used to calculate the summarized sensitivity and specificity. To identify factors contributing to heterogeneity, meta-regression and subgroup analyses were performed to assess the following covariates: diagnostic modality, region/volume of interest, imaging examination, study design, and pathology type. The asymmetry of Deeks' funnel plot was used to assess publication bias. RESULTS A total of 21 studies involving 3866 patients were included, with 13 studies using independent test/validation sets included in the quantitative statistical analysis. The average RQS was 21.31, with substantial or near-perfect inter-rater agreement. The combined sensitivity and specificity were 0.84 (95% CI: 0.76-0.89) and 0.88 (95% CI: 0.69-0.96), respectively. Meta-regression and subgroup analyses showed that study design and the region/volume of interest were significant factors affecting study heterogeneity (P < 0.05). No publication bias was observed. CONCLUSION Radiomics and DL can accurately distinguish between benign and malignant STTs. Future research should concentrate on enhancing the rigor of study designs, conducting multicenter prospective validations, amplifying the interpretability of DL models, and integrating multimodal data to elevate the diagnostic accuracy and clinical utility of soft tissue tumor assessments.
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Affiliation(s)
- Xinpeng Dai
- Department of Ultrasound, Hebei Medical University Third Hospital, Hebei, China
| | - Bingxin Zhao
- Department of Ultrasound, Hebei Medical University Third Hospital, Hebei, China
| | - Jiangnan Zang
- Hebei Medical University, Shijiazhuang, Hebei, China
| | - Xinying Wang
- Department of Ultrasound, Hebei Medical University Third Hospital, Hebei, China
| | - Zongjie Liu
- Department of Ultrasound, Hebei Medical University Third Hospital, Hebei, China
| | - Tao Sun
- Department of Orthopaedic Oncology, Hebei Medical University Third Hospital, Hebei, China
| | - Hong Yu
- Department of CT/MR, Hebei Medical University Third Hospital, Hebei, China
| | - Xin Sui
- Department of Ultrasound, Hebei Medical University Third Hospital, No.139 Ziqiang road, Qiaoxi Area, Shijiazhuang, Hebei Province, China.
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Starmans MPA, Miclea RL, Vilgrain V, Ronot M, Purcell Y, Verbeek J, Niessen WJ, Ijzermans JNM, de Man RA, Doukas M, Klein S, Thomeer MG. Automated Assessment of T2-Weighted MRI to Differentiate Malignant and Benign Primary Solid Liver Lesions in Noncirrhotic Livers Using Radiomics. Acad Radiol 2024; 31:870-879. [PMID: 37648580 DOI: 10.1016/j.acra.2023.07.024] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 07/06/2023] [Accepted: 07/25/2023] [Indexed: 09/01/2023]
Abstract
RATIONALE AND OBJECTIVES Distinguishing malignant from benign liver lesions based on magnetic resonance imaging (MRI) is an important but often challenging task, especially in noncirrhotic livers. We developed and externally validated a radiomics model to quantitatively assess T2-weighted MRI to distinguish the most common malignant and benign primary solid liver lesions in noncirrhotic livers. MATERIALS AND METHODS Data sets were retrospectively collected from three tertiary referral centers (A, B, and C) between 2002 and 2018. Patients with malignant (hepatocellular carcinoma and intrahepatic cholangiocarcinoma) and benign (hepatocellular adenoma and focal nodular hyperplasia) lesions were included. A radiomics model based on T2-weighted MRI was developed in data set A using a combination of machine learning approaches. The model was internally evaluated on data set A through cross-validation, externally validated on data sets B and C, and compared to visual scoring of two experienced abdominal radiologists on data set C. RESULTS The overall data set included 486 patients (A: 187, B: 98, and C: 201). The radiomics model had a mean area under the curve (AUC) of 0.78 upon internal validation on data set A and a similar AUC in external validation (B: 0.74 and C: 0.76). In data set C, the two radiologists showed moderate agreement (Cohen's κ: 0.61) and achieved AUCs of 0.86 and 0.82. CONCLUSION Our T2-weighted MRI radiomics model shows potential for distinguishing malignant from benign primary solid liver lesions. External validation indicated that the model is generalizable despite substantial MRI acquisition protocol differences. Pending further optimization and generalization, this model may aid radiologists in improving the diagnostic workup of patients with liver lesions.
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Affiliation(s)
- Martijn P A Starmans
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands (M.P.A.S., W.J.N., S.K., M.G.T.).
| | - Razvan L Miclea
- Department of Radiology and Nuclear Medicine, Maastricht UMC+, Maastricht, the Netherlands (R.L.M.)
| | - Valerie Vilgrain
- Université de Paris, INSERM U 1149, CRI, Paris, France (V.V., M.R.); Département de Radiologie, Hôpital Beaujon, APHP.Nord, Clichy, France (V.V., M.R.)
| | - Maxime Ronot
- Université de Paris, INSERM U 1149, CRI, Paris, France (V.V., M.R.); Département de Radiologie, Hôpital Beaujon, APHP.Nord, Clichy, France (V.V., M.R.)
| | - Yvonne Purcell
- Department of Radiology, Hôpital Fondation Rothschild, Paris, France (Y.P.)
| | - Jef Verbeek
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium (J.V.); Department of Gastroenterology and Hepatology, Maastricht UMC+, Maastricht, the Netherlands (J.V.)
| | - Wiro J Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands (M.P.A.S., W.J.N., S.K., M.G.T.); Faculty of Applied Sciences, Delft University of Technology, the Netherlands (W.J.N.)
| | - Jan N M Ijzermans
- Department of Surgery, Erasmus MC, Rotterdam, the Netherlands (J.N.M.I.)
| | - Rob A de Man
- Department of Gastroenterology & Hepatology, Erasmus MC, Rotterdam, the Netherlands (R.A.d.M.)
| | - Michael Doukas
- Department of Pathology, Erasmus MC, Rotterdam, the Netherlands (M.D.)
| | - Stefan Klein
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands (M.P.A.S., W.J.N., S.K., M.G.T.)
| | - Maarten G Thomeer
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands (M.P.A.S., W.J.N., S.K., M.G.T.)
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Wilson MP, Haidey J, Murad MH, Sept L, Low G. Diagnostic accuracy of CT and MR features for detecting atypical lipomatous tumors and malignant liposarcomas: a systematic review and meta-analysis. Eur Radiol 2023; 33:8605-8616. [PMID: 37439933 DOI: 10.1007/s00330-023-09916-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 04/22/2023] [Accepted: 05/14/2023] [Indexed: 07/14/2023]
Abstract
OBJECTIVES This systematic review and meta-analysis evaluated the diagnostic accuracy of CT and MRI for differentiating atypical lipomatous tumors and malignant liposarcomas from benign lipomatous lesions. METHODS MEDLINE, EMBASE, Scopus, the Cochrane Library, and the gray literature from inception to January 2022 were systematically evaluated. Original studies with > 5 patients evaluating the accuracy of CT and/or MRI for detecting liposarcomas with a histopathological reference standard were included. Meta-analysis was performed using a bivariate mixed-effects regression model. Risk of bias was evaluated using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). This study is registered on PROSPERO, number CRD42022306479. RESULTS Twenty-six studies with a total of 2613 patients were included. Mean/median reported patient ages ranged between 50 and 63 years. The summary sensitivity and specificity of radiologist gestalt for detecting liposarcomas was 85% (79-90% 95% CI) and 63% (52-72%), respectively. Deep depth to fascia, thickened septations, enhancing components, and lesion size (≥ 10 cm) all demonstrated sensitivities ≥ 85%. Other imaging characteristics including heterogenous/amorphous signal intensity, irregular tumor margin, and nodules present demonstrated lower sensitivities ranging from 43 to 65%. Inter-reader reliability for radiologist gestalt within studies ranged from fair to substantial (k = 0.23-0.7). Risk of bias was predominantly mixed for patient selection, low for index test and reference standard, and unclear for flow and timing. CONCLUSION Higher sensitivities for detecting liposarcomas were achieved with radiologist gestalt, deep depth to fascia, thickened septations, enhancing components, and large size. Combined clinical and imaging scoring and/or radiomics both show promise for optimal performance, though require further analysis with prospective study designs. CLINICAL RELEVANCE This pooled analysis evaluates the accuracy of CT and MRI for detecting atypical lipomatous tumors and malignant liposarcomas. Radiologist gestalt, deep depth to fascia, thickened septations, enhancing components, and large size demonstrate the highest overall sensitivities. KEY POINTS • The summary sensitivity and specificity of radiologist gestalt for detecting liposarcomas was 85% (79-90% 95% CI) and 63% (52-72%), respectively. • Radiologist gestalt, deep depth to fascia, thickened septations, enhancing components, and large tumor size (≥ 10 cm) showed the highest sensitivities for detecting atypical lipomatous tumors/well-differentiated liposarcomas and malignant liposarcomas. • A combined clinical and imaging scoring system and/or radiomics is likely to provide the best overall diagnostic accuracy, although currently proposed scoring systems and radiomic feature analysis require further study with prospective study designs.
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Affiliation(s)
- Mitchell P Wilson
- Department of Radiology and Diagnostic Imaging, University of Alberta, 2B2.41 WMC, 8440-112 Street NW, Edmonton, AB, T6G 2B7, Canada.
| | - Jordan Haidey
- Department of Radiology and Diagnostic Imaging, University of Alberta, 2B2.41 WMC, 8440-112 Street NW, Edmonton, AB, T6G 2B7, Canada
| | - Mohammad H Murad
- Evidence-Based Practice Center, Mayo Clinic, Room 2-54, 2053Rd Ave SW, Rochester, MN, 55905, USA
| | - Logan Sept
- Department of Radiology and Diagnostic Imaging, University of Alberta, 2B2.41 WMC, 8440-112 Street NW, Edmonton, AB, T6G 2B7, Canada
| | - Gavin Low
- Department of Radiology and Diagnostic Imaging, University of Alberta, 2B2.41 WMC, 8440-112 Street NW, Edmonton, AB, T6G 2B7, Canada
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Crombé A, Spinnato P, Italiano A, Brisse HJ, Feydy A, Fadli D, Kind M. Radiomics and artificial intelligence for soft-tissue sarcomas: Current status and perspectives. Diagn Interv Imaging 2023; 104:567-583. [PMID: 37802753 DOI: 10.1016/j.diii.2023.09.005] [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: 07/10/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 10/08/2023]
Abstract
This article proposes a summary of the current status of the research regarding the use of radiomics and artificial intelligence to improve the radiological assessment of patients with soft tissue sarcomas (STS), a heterogeneous group of rare and ubiquitous mesenchymal malignancies. After a first part explaining the principle of radiomics approaches, from raw image post-processing to extraction of radiomics features mined with unsupervised and supervised machine-learning algorithms, and the current research involving deep learning algorithms in STS, especially convolutional neural networks, this review details their main research developments since the formalisation of 'radiomics' in oncologic imaging in 2010. This review focuses on CT and MRI and does not involve ultrasonography. Radiomics and deep radiomics have been successfully applied to develop predictive models to discriminate between benign soft-tissue tumors and STS, to predict the histologic grade (i.e., the most important prognostic marker of STS), the response to neoadjuvant chemotherapy and/or radiotherapy, and the patients' survivals and probability for presenting distant metastases. The main findings, limitations and expectations are discussed for each of these outcomes. Overall, after a first decade of publications emphasizing the potential of radiomics through retrospective proof-of-concept studies, almost all positive but with heterogeneous and often non-replicable methods, radiomics is now at a turning point in order to provide robust demonstrations of its clinical impact through open-science, independent databases, and application of good and standardized practices in radiomics such as those provided by the Image Biomarker Standardization Initiative, without forgetting innovative research paths involving other '-omics' data to better understand the relationships between imaging of STS, gene-expression profiles and tumor microenvironment.
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Affiliation(s)
- Amandine Crombé
- Department of Radiology, Pellegrin University Hospital, 33000 Bordeaux, France; Department of Oncologic Imaging, Bergonié Institute, 33076 Bordeaux, France; 'Sarcotarget' team, BRIC INSERM U1312 and Bordeaux University, 33000 Bordeaux France.
| | - Paolo Spinnato
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna 40136, Italy
| | | | | | - Antoine Feydy
- Department of Radiology, Hopital Cochin-AP-HP, 75014 Paris, France; Université Paris Cité, Faculté de Médecine, 75006 Paris, France
| | - David Fadli
- Department of Radiology, Pellegrin University Hospital, 33000 Bordeaux, France
| | - Michèle Kind
- Department of Oncologic Imaging, Bergonié Institute, 33076 Bordeaux, France
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Zhang X, Peng J, Ji G, Li T, Li B, Xiong H. Research status and progress of radiomics in bone and soft tissue tumors: A review. Medicine (Baltimore) 2023; 102:e36196. [PMID: 38013345 PMCID: PMC10681559 DOI: 10.1097/md.0000000000036198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 10/27/2023] [Indexed: 11/29/2023] Open
Abstract
Bone and soft tissue tumors are diverse, accompanying by complex histological components and significantly divergent biological behaviors. It is a challenge to address the demand for qualitative imaging as traditional imaging is restricted to the detection of anatomical structures and aberrant signals. With the improvement of digitalization in hospitals and medical centers, the introduction of electronic medical records and easier access to large amounts of information coupled with the improved computational power, traditional medicine has evolved into the combination of human brain, minimal data, and artificial intelligence. Scholars are committed to mining deeper levels of imaging data, and radiomics is worthy of promotion. Radiomics extracts subvisual quantitative features, analyzes them based on medical images, and quantifies tumor heterogeneity by outlining the region of interest and modeling. Two observers separately examined PubMed, Web of Science and CNKI to find existing studies, case reports, and clinical guidelines about research status and progress of radiomics in bone and soft tissue tumors from January 2010 to February 2023. When evaluating the literature, factors such as patient age, medical history, and severity of the condition will be considered. This narrative review summarizes the application and progress of radiomics in bone and soft tissue tumors.
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Affiliation(s)
- Xiaohan Zhang
- Department of Radiology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
| | - Jie Peng
- Department of Radiology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
| | - Guanghai Ji
- Department of Radiology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
| | - Tian Li
- Department of Radiology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
| | - Bo Li
- Department of Radiology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
| | - Hao Xiong
- Department of Radiology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
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Dubin AH, Johnson Q, Horowitz J. Primary Cutaneous Adenoid Cystic Carcinoma of the Back: A Case Report. Cureus 2023; 15:e49099. [PMID: 38125236 PMCID: PMC10731834 DOI: 10.7759/cureus.49099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/20/2023] [Indexed: 12/23/2023] Open
Abstract
Adenoid cystic carcinoma (ACC) is a rare type of carcinoma that arises from the salivary glands. When ACC is present on the skin with no other primary site of malignancy in the body, it is termed primary cutaneous adenoid cystic carcinoma (PCACC). The only way to differentiate between ACC and other benign cutaneous masses is through the use of histopathology and immunohistochemistry. This case report describes a 67-year-old Asian female with a history of an epidermal inclusion cyst. She was seen in consultation with general surgery for the removal of a mass on her lower back. The initial excision's pathology revealed an ACC with perineural invasion. However, there were positive margins, as the mass was originally thought to be benign. Consequently, she underwent a second procedure for the total excision of the mass, resulting in subsequent negative margins. The patient was referred to radiation oncology; however, she ultimately opted to defer postoperative adjuvant radiation therapy, with the understanding that she would undergo biannual screening examinations.
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Affiliation(s)
- Aimee H Dubin
- Surgery, Campbell University School of Osteopathic Medicine, Lillington, USA
| | | | - Joel Horowitz
- Surgery, Cape Fear Valley Medical Center, Fayetteville, USA
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10
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Natella R, Varriano G, Brunese MC, Zappia M, Bruno M, Gallo M, Fazioli F, Simonetti I, Granata V, Brunese L, Santone A. Increasing differential diagnosis between lipoma and liposarcoma through radiomics: a narrative review. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:498-510. [PMID: 37455823 PMCID: PMC10344889 DOI: 10.37349/etat.2023.00147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 03/06/2023] [Indexed: 07/18/2023] Open
Abstract
Soft tissue sarcomas (STSs) are rare, heterogeneous, and very often asymptomatic diseases. Their diagnosis is fundamental, as is the identification of the degree of malignancy, which may be high, medium, or low. The Italian Medical Oncology Association and European Society of Medical Oncology (ESMO) guidelines recommend magnetic resonance imaging (MRI) because the clinical examination is typically ineffective. The diagnosis of these rare diseases with artificial intelligence (AI) techniques presents reduced datasets and therefore less robust methods. However, the combination of AI techniques with radiomics may be a new angle in diagnosing rare diseases such as STSs. Results obtained are promising within the literature, not only for the performance but also for the explicability of the data. In fact, one can make tumor classification, site localization, and prediction of the risk of developing metastasis. Thanks to the synergy between computer scientists and radiologists, linking numerical features to radiological evidence with excellent performance could be a new step forward for the diagnosis of rare diseases.
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Affiliation(s)
- Raffaele Natella
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy
| | - Giulia Varriano
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy
| | - Marcello Zappia
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy
| | - Michela Bruno
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy
| | - Michele Gallo
- Orthopedics Oncology, National Cancer Institute IRCCS “Fondazione G. Pascale”, 80100 Naples, Italy
| | - Flavio Fazioli
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy
| | - Igino Simonetti
- Radiology Division, National Cancer Institute IRCCS “Fondazione G. Pascale”, 80100 Naples, Italy
| | - Vincenza Granata
- Radiology Division, National Cancer Institute IRCCS “Fondazione G. Pascale”, 80100 Naples, Italy
| | - Luca Brunese
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy
| | - Antonella Santone
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy
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11
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Gitto S, Interlenghi M, Cuocolo R, Salvatore C, Giannetta V, Badalyan J, Gallazzi E, Spinelli MS, Gallazzi M, Serpi F, Messina C, Albano D, Annovazzi A, Anelli V, Baldi J, Aliprandi A, Armiraglio E, Parafioriti A, Daolio PA, Luzzati A, Biagini R, Castiglioni I, Sconfienza LM. MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor of the extremities. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01657-y. [PMID: 37335422 DOI: 10.1007/s11547-023-01657-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/26/2023] [Indexed: 06/21/2023]
Abstract
PURPOSE To determine diagnostic performance of MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor (ALT) of the extremities. MATERIAL AND METHODS This retrospective study was performed at three tertiary sarcoma centers and included 150 patients with surgically treated and histology-proven lesions. The training-validation cohort consisted of 114 patients from centers 1 and 2 (n = 64 lipoma, n = 50 ALT). The external test cohort consisted of 36 patients from center 3 (n = 24 lipoma, n = 12 ALT). 3D segmentation was manually performed on T1- and T2-weighted MRI. After extraction and selection of radiomic features, three machine learning classifiers were trained and validated using nested fivefold cross-validation. The best-performing classifier according to previous analysis was evaluated and compared to an experienced musculoskeletal radiologist in the external test cohort. RESULTS Eight features passed feature selection and were incorporated into the machine learning models. After training and validation (74% ROC-AUC), the best-performing classifier (Random Forest) showed 92% sensitivity and 33% specificity in the external test cohort with no statistical difference compared to the radiologist (p = 0.474). CONCLUSION MRI radiomics-based machine learning may classify deep-seated lipoma and ALT of the extremities with high sensitivity and negative predictive value, thus potentially serving as a non-invasive screening tool to reduce unnecessary referral to tertiary tumor centers.
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Affiliation(s)
- Salvatore Gitto
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy
| | | | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
- Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
| | - Christian Salvatore
- DeepTrace Technologies, Milan, Italy
- Department of Science, Technology and Society, University School for Advanced Studies IUSS Pavia, Pavia, Italy
| | - Vincenzo Giannetta
- Diagnostic and Interventional Radiology Department, IRCCS Ospedale San Raffaele-Turro, Università Vita-Salute San Raffaele, Milan, Italy
| | - Julietta Badalyan
- Scuola di Specializzazione in Statistica Sanitaria e Biometria, Università Degli Studi Di Milano, Milan, Italy
| | - Enrico Gallazzi
- UOC Patologia Vertebrale e Scoliosi, ASST Gaetano Pini - CTO, Milan, Italy
| | | | - Mauro Gallazzi
- UOC Radiodiagnostica, ASST Gaetano Pini - CTO, Milan, Italy
| | - Francesca Serpi
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy
| | - Carmelo Messina
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy
| | | | - Alessio Annovazzi
- Nuclear Medicine Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Vincenzo Anelli
- Radiology and Diagnostic Imaging Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Jacopo Baldi
- Oncological Orthopaedics Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | | | | | | | | | | | - Roberto Biagini
- Oncological Orthopaedics Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Isabella Castiglioni
- Department of Physics, Università degli Studi di Milano-Bicocca, Milan, Italy
- Institute of Biomedical Imaging and Physiology, Consiglio Nazionale Delle Ricerche, Segrate, Italy
| | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy.
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12
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Liu CC, Abdelhafez YG, Yap SP, Acquafredda F, Schirò S, Wong AL, Sarohia D, Bateni C, Darrow MA, Guindani M, Lee S, Zhang M, Moawad AW, Ng QKT, Shere L, Elsayes KM, Maroldi R, Link TM, Nardo L, Qi J. AI-Based Automated Lipomatous Tumor Segmentation in MR Images: Ensemble Solution to Heterogeneous Data. J Digit Imaging 2023; 36:1049-1059. [PMID: 36854923 PMCID: PMC10287587 DOI: 10.1007/s10278-023-00785-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 01/20/2023] [Accepted: 01/20/2023] [Indexed: 03/02/2023] Open
Abstract
Deep learning (DL) has been proposed to automate image segmentation and provide accuracy, consistency, and efficiency. Accurate segmentation of lipomatous tumors (LTs) is critical for correct tumor radiomics analysis and localization. The major challenge of this task is data heterogeneity, including tumor morphological characteristics and multicenter scanning protocols. To mitigate the issue, we aimed to develop a DL-based Super Learner (SL) ensemble framework with different data correction and normalization methods. Pathologically proven LTs on pre-operative T1-weighted/proton-density MR images of 185 patients were manually segmented. The LTs were categorized by tumor locations as distal upper limb (DUL), distal lower limb (DLL), proximal upper limb (PUL), proximal lower limb (PLL), or Trunk (T) and grouped by 80%/9%/11% for training, validation and testing. Six configurations of correction/normalization were applied to data for fivefold-cross-validation trainings, resulting in 30 base learners (BLs). A SL was obtained from the BLs by optimizing SL weights. The performance was evaluated by dice-similarity-coefficient (DSC), sensitivity, specificity, and Hausdorff distance (HD95). For predictions of the BLs, the average DSC, sensitivity, and specificity from the testing data were 0.72 [Formula: see text] 0.16, 0.73 [Formula: see text] 0.168, and 0.99 [Formula: see text] 0.012, respectively, while for SL predictions were 0.80 [Formula: see text] 0.184, 0.78 [Formula: see text] 0.193, and 1.00 [Formula: see text] 0.010. The average HD95 of the BLs were 11.5 (DUL), 23.2 (DLL), 25.9 (PUL), 32.1 (PLL), and 47.9 (T) mm, whereas of SL were 1.7, 8.4, 15.9, 2.2, and 36.6 mm, respectively. The proposed method could improve the segmentation accuracy and mitigate the performance instability and data heterogeneity aiding the differential diagnosis of LTs in real clinical situations.
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Affiliation(s)
- Chih-Chieh Liu
- Department of Biomedical Engineering, University of California, Davis, CA, USA
| | - Yasser G Abdelhafez
- Department of Radiology, UC Davis Health, Sacramento, CA, USA
- Radiotherapy and Nuclear Medicine Department, South Egypt Cancer Institute, Assiut University, Assiut, Egypt
| | - S Paran Yap
- Department of Radiology, UC Davis Health, Sacramento, CA, USA
| | | | - Silvia Schirò
- Section of Radiology, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Andrew L Wong
- Department of Radiology, UC Davis Health, Sacramento, CA, USA
| | - Dani Sarohia
- Department of Radiology, UC Davis Health, Sacramento, CA, USA
| | - Cyrus Bateni
- Department of Radiology, UC Davis Health, Sacramento, CA, USA
| | - Morgan A Darrow
- Pathology and Laboratory Medicine, University of California Davis, Sacramento, CA, USA
| | - Michele Guindani
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Sonia Lee
- Department of Radiological Sciences, University of California, Irvine, CA, USA
| | - Michelle Zhang
- Department of Diagnostic Radiology, McGill University Health Center, Montreal, Canada
| | - Ahmed W Moawad
- Department of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Diagnostic Radiology, Mercy Catholic Medical Center, Darby, PA, USA
| | | | - Layla Shere
- Department of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Khaled M Elsayes
- Department of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Thomas M Link
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Lorenzo Nardo
- Department of Radiology, UC Davis Health, Sacramento, CA, USA
| | - Jinyi Qi
- Department of Biomedical Engineering, University of California, Davis, CA, USA.
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13
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Foreman SC, Llorián-Salvador O, David DE, Rösner VKN, Rischewski JF, Feuerriegel GC, Kramp DW, Luiken I, Lohse AK, Kiefer J, Mogler C, Knebel C, Jung M, Andrade-Navarro MA, Rost B, Combs SE, Makowski MR, Woertler K, Peeken JC, Gersing AS. Development and Evaluation of MR-Based Radiogenomic Models to Differentiate Atypical Lipomatous Tumors from Lipomas. Cancers (Basel) 2023; 15:cancers15072150. [PMID: 37046811 PMCID: PMC10093205 DOI: 10.3390/cancers15072150] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/10/2023] [Accepted: 03/27/2023] [Indexed: 04/08/2023] Open
Abstract
Background: The aim of this study was to develop and validate radiogenomic models to predict the MDM2 gene amplification status and differentiate between ALTs and lipomas on preoperative MR images. Methods: MR images were obtained in 257 patients diagnosed with ALTs (n = 65) or lipomas (n = 192) using histology and the MDM2 gene analysis as a reference standard. The protocols included T2-, T1-, and fat-suppressed contrast-enhanced T1-weighted sequences. Additionally, 50 patients were obtained from a different hospital for external testing. Radiomic features were selected using mRMR. Using repeated nested cross-validation, the machine-learning models were trained on radiomic features and demographic information. For comparison, the external test set was evaluated by three radiology residents and one attending radiologist. Results: A LASSO classifier trained on radiomic features from all sequences performed best, with an AUC of 0.88, 70% sensitivity, 81% specificity, and 76% accuracy. In comparison, the radiology residents achieved 60–70% accuracy, 55–80% sensitivity, and 63–77% specificity, while the attending radiologist achieved 90% accuracy, 96% sensitivity, and 87% specificity. Conclusion: A radiogenomic model combining features from multiple MR sequences showed the best performance in predicting the MDM2 gene amplification status. The model showed a higher accuracy compared to the radiology residents, though lower compared to the attending radiologist.
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Affiliation(s)
- Sarah C. Foreman
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Oscar Llorián-Salvador
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
- Department of Informatics, Bioinformatics and Computational Biology—i12, Technische Universität München, Boltzmannstr. 3, 85748 Munich, Germany
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Hanns-Dieter-Hüsch-Weg 15, 55128 Mainz, Germany
| | - Diana E. David
- Department of Informatics, Bioinformatics and Computational Biology—i12, Technische Universität München, Boltzmannstr. 3, 85748 Munich, Germany
| | - Verena K. N. Rösner
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Jon F. Rischewski
- Department of Diagnostic and Interventional Neuroradiology, University Hospital Munich (LMU), Marchioninistrasse 15, 81377 Munich, Germany
| | - Georg C. Feuerriegel
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Daniel W. Kramp
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Ina Luiken
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Ann-Kathrin Lohse
- Department of Radiology, University Hospital Munich (LMU), Marchioninistrasse 15, 81377 Munich, Germany
| | - Jurij Kiefer
- Department of Plastic Surgery, University Hospital Freiburg, University of Freiburg, Hugstetterstraße 55, 79106 Freiburg im Breisgau, Germany
| | - Carolin Mogler
- Institute of Pathology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Carolin Knebel
- Department of Orthopedics and Sport Orthopedics, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Matthias Jung
- Department of Radiology, University Hospital Freiburg, University of Freiburg, Hugstetterstraße 55, 79106 Freiburg im Breisgau, Germany
| | - Miguel A. Andrade-Navarro
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Hanns-Dieter-Hüsch-Weg 15, 55128 Mainz, Germany
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology—i12, Technische Universität München, Boltzmannstr. 3, 85748 Munich, Germany
| | - Stephanie E. Combs
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Marcus R. Makowski
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Klaus Woertler
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
| | - Jan C. Peeken
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675 Munich, Germany
- Helmholtz Zentrum München, Deutsches Forschungszentrum für Umwelt und Gesundheit, Institute of Radiation Medicine Neuherberg, 85764 Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, 69120 Heidelberg, Germany
| | - Alexandra S. Gersing
- Department of Diagnostic and Interventional Neuroradiology, University Hospital Munich (LMU), Marchioninistrasse 15, 81377 Munich, Germany
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14
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Sudjai N, Siriwanarangsun P, Lektrakul N, Saiviroonporn P, Maungsomboon S, Phimolsarnti R, Asavamongkolkul A, Chandhanayingyong C. Tumor-to-bone distance and radiomic features on MRI distinguish intramuscular lipomas from well-differentiated liposarcomas. J Orthop Surg Res 2023; 18:255. [PMID: 36978182 PMCID: PMC10044811 DOI: 10.1186/s13018-023-03718-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 03/15/2023] [Indexed: 03/30/2023] Open
Abstract
Background To develop a machine learning model based on tumor-to-bone distance and radiomic features derived from preoperative MRI images to distinguish intramuscular (IM) lipomas and atypical lipomatous tumors/well-differentiated liposarcomas (ALTs/WDLSs) and compared with radiologists. Methods The study included patients with IM lipomas and ALTs/WDLSs diagnosed between 2010 and 2022, and with MRI scans (sequence/field strength: T1-weighted (T1W) imaging at 1.5 or 3.0 Tesla MRI). Manual segmentation of tumors based on the three-dimensional T1W images was performed by two observers to appraise the intra- and interobserver variability. After radiomic features and tumor-to-bone distance were extracted, it was used to train a machine learning model to distinguish IM lipomas and ALTs/WDLSs. Both feature selection and classification steps were performed using Least Absolute Shrinkage and Selection Operator logistic regression. The performance of the classification model was assessed using a tenfold cross-validation strategy and subsequently evaluated using the receiver operating characteristic curve (ROC) analysis. The classification agreement of two experienced musculoskeletal (MSK) radiologists was assessed using the kappa statistics. The diagnosis accuracy of each radiologist was evaluated using the final pathological results as the gold standard. Additionally, we compared the performance of the model and two radiologists in terms of the area under the receiver operator characteristic curves (AUCs) using the Delong’s test. Results There were 68 tumors (38 IM lipomas and 30 ALTs/WDLSs). The AUC of the machine learning model was 0.88 [95% CI 0.72–1] (sensitivity, 91.6%; specificity, 85.7%; and accuracy, 89.0%). For Radiologist 1, the AUC was 0.94 [95% CI 0.87–1] (sensitivity, 97.4%; specificity, 90.9%; and accuracy, 95.0%), and as to Radiologist 2, the AUC was 0.91 [95% CI 0.83–0.99] (sensitivity, 100%; specificity, 81.8%; and accuracy, 93.3%). The classification agreement of the radiologists was 0.89 of kappa value (95% CI 0.76–1). Although the AUC of the model was lower than of two experienced MSK radiologists, there was no statistically significant difference between the model and two radiologists (all P > 0.05). Conclusions The novel machine learning model based on tumor-to-bone distance and radiomic features is a noninvasive procedure that has the potential for distinguishing IM lipomas from ALTs/WDLSs. The predictive features that suggested malignancy were size, shape, depth, texture, histogram, and tumor-to-bone distance. Supplementary Information The online version contains supplementary material available at 10.1186/s13018-023-03718-4.
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Affiliation(s)
- Narumol Sudjai
- grid.10223.320000 0004 1937 0490Department of Orthopaedic Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700 Thailand
| | - Palanan Siriwanarangsun
- grid.10223.320000 0004 1937 0490Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700 Thailand
| | - Nittaya Lektrakul
- grid.10223.320000 0004 1937 0490Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700 Thailand
| | - Pairash Saiviroonporn
- grid.10223.320000 0004 1937 0490Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700 Thailand
| | - Sorranart Maungsomboon
- grid.10223.320000 0004 1937 0490Department of Pathology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700 Thailand
| | - Rapin Phimolsarnti
- grid.10223.320000 0004 1937 0490Department of Orthopaedic Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700 Thailand
| | - Apichat Asavamongkolkul
- grid.10223.320000 0004 1937 0490Department of Orthopaedic Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700 Thailand
| | - Chandhanarat Chandhanayingyong
- grid.10223.320000 0004 1937 0490Department of Orthopaedic Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700 Thailand
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15
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Evaluation of Classic and Quantitative Imaging Features in the Differentiation of Benign and Atypical Lipomatous Soft Tissue Tumors Using a Standardized Multiparametric MRI Protocol: A Prospective Single-Centre Study in 45 Patients. Curr Oncol 2023; 30:3315-3328. [PMID: 36975465 PMCID: PMC10047222 DOI: 10.3390/curroncol30030252] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 02/24/2023] [Accepted: 03/07/2023] [Indexed: 03/16/2023] Open
Abstract
Background: Discrimination between benign and atypical lipomatous tumors (ALT) is important due to potential local complications and recurrence of ALT but can be difficult due to the often-similar imaging appearance. Using a standardized MRI protocol, this study aimed to rank established and quantitative MRI features by diagnostic value in the differentiation of benign and atypical lipomatous tumors and to develop a robust scoring system. Methods: Patients with clinical or sonographic suspicion of a lipomatous tumor were prospectively and consecutively enrolled from 2015 to 2019 after ethic review board approval. Histology was confirmed for all ALT and 85% of the benign cases. Twenty-one demographic and morphologic and twenty-three quantitative features were extracted from a standardized MRI protocol (T1/T2-proton-density-weighting, turbo-inversion recovery magnitude, T2* multi-echo gradient-echo imaging, qDIXON-Vibe fat-quantification, T1 relaxometry, T1 mapping, diffusion-weighted and post-contrast sequences). A ranking of these features was generated through a Bayes network analysis with gain-ratio feature evaluation. Results: Forty-five patients were included in the analysis (mean age, 61.2 ± 14.2 years, 27 women [60.0%]). The highest-ranked ALT predictors were septation thickness (gain ratio merit [GRM] 0.623 ± 0.025, p = 0.0055), intra- and peritumoral STIR signal discrepancy (GRM 0.458 ± 0.046, p < 0.0001), orthogonal diameter (GRM 0.554 ± 0.188, p = 0.0013), contrast enhancement (GRM 0.235 ± 0.015, p = 0.0010) and maximum diameter (GRM 0.221 ± 0.075, p = 0.0009). The quantitative features did not provide a significant discriminatory value. The highest-ranked predictors were used to generate a five-tiered score for the identification of ALTs (correct classification rate 95.7% at a cut-off of three positive items, sensitivity 100.0%, specificity 94.9%, likelihood ratio 19.5). Conclusions: Several single MRI features have a substantial diagnostic value in the identification of ALT, yet a multiparametric approach by a simple combination algorithm may support radiologists in the identification of lipomatous tumors in need for further histological assessment.
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16
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Robustness of Radiomic Features: Two-Dimensional versus Three-Dimensional MRI-Based Feature Reproducibility in Lipomatous Soft-Tissue Tumors. Diagnostics (Basel) 2023; 13:diagnostics13020258. [PMID: 36673068 PMCID: PMC9858448 DOI: 10.3390/diagnostics13020258] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/10/2022] [Accepted: 01/07/2023] [Indexed: 01/13/2023] Open
Abstract
This retrospective study aimed to compare the intra- and inter-observer manual-segmentation variability in the feature reproducibility between two-dimensional (2D) and three-dimensional (3D) magnetic-resonance imaging (MRI)-based radiomic features. The study included patients with lipomatous soft-tissue tumors that were diagnosed with histopathology and underwent MRI scans. Tumor segmentation based on the 2D and 3D MRI images was performed by two observers to assess the intra- and inter-observer variability. In both the 2D and the 3D segmentations, the radiomic features were extracted from the normalized images. Regarding the stability of the features, the intraclass correlation coefficient (ICC) was used to evaluate the intra- and inter-observer segmentation variability. Features with ICC > 0.75 were considered reproducible. The degree of feature robustness was classified as low, moderate, or high. Additionally, we compared the efficacy of 2D and 3D contour-focused segmentation in terms of the effects of the stable feature rate, sensitivity, specificity, and diagnostic accuracy of machine learning on the reproducible features. In total, 93 and 107 features were extracted from the 2D and 3D images, respectively. Only 35 features from the 2D images and 63 features from the 3D images were reproducible. The stable feature rate for the 3D segmentation was more significant than for the 2D segmentation (58.9% vs. 37.6%, p = 0.002). The majority of the features for the 3D segmentation had moderate-to-high robustness, while 40.9% of the features for the 2D segmentation had low robustness. The diagnostic accuracy of the machine-learning model for the 2D segmentation was close to that for the 3D segmentation (88% vs. 90%). In both the 2D and the 3D segmentation, the specificity values were equal to 100%. However, the sensitivity for the 2D segmentation was lower than for the 3D segmentation (75% vs. 83%). For the 2D + 3D radiomic features, the model achieved a diagnostic accuracy of 87% (sensitivity, 100%, and specificity, 80%). Both 2D and 3D MRI-based radiomic features of lipomatous soft-tissue tumors are reproducible. With a higher stable feature rate, 3D contour-focused segmentation should be selected for the feature-extraction process.
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Tang Y, Cui J, Zhu J, Fan G. Differentiation Between Lipomas and Atypical Lipomatous Tumors of the Extremities Using Radiomics. J Magn Reson Imaging 2022; 56:1746-1754. [PMID: 35348280 DOI: 10.1002/jmri.28167] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 03/11/2022] [Accepted: 03/11/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND The differentiation of soft tissue lipomas from atypical lipoma tumors (ALTs) of the extremities is important because of the distinction of the cytogenetic profiles and the treatment decisions. PURPOSE To investigate a radiomics method to differentiate between lipomas and ALTs of the extremities. STUDY TYPE Retrospective. POPULATION Imaging data of 122 patients including 90 cases of lipomas and 32 cases of ALTs. FIELD STRENGTH/SEQUENCE Axial T1-weighted imaging and fat suppressed T2-weighted imaging at 3.0T MRI. ASSESSMENT Analysis of variance and the least absolute shrinkage and selection operator methods were used for feature selection and the random forest method was used to build three radiomics models based on T1WI, FS T2WI, and their combination (T1&T2WI). Three independent radiologists classified the tumors based on the subjective assessments. STATISTICAL TESTS The area under the curve (AUC) of the receiver operating characteristic curve, accuracy, F1-score, specificity, and sensitivity were employed. The differences of the classifiers and discriminating ability of the radiologists and the radiomics model were compared by Delong test. A P value <0.05 was considered significant. Kappa test was used to determine the inter-reader agreements between the radiologists. RESULT The AUCs were 0.952 (95% confidence interval [CI]: 0.785-0.998), 0.944 (95% CI: 0.774-0.997), and 0.968 (95% CI: 0.809-1) for T1WI, FS T2WI, and T1&T2WI models in testing sets respectively. Delong test showed there were no significant difference between the different radiomics models (P > 0.05). The AUCs of the radiologists were 0.893 (95% CI: 0.824-0.942), 0.831 (95% CI: 0.752-0.893), and 0.893 (95% CI: 0.824-0.94), respectively. There were significant difference between radiomics model and radiologists' model in the training and entire cohorts (P < 0.05) while there were no significant difference in the testing sets (P > 0.05). DATA CONCLUSION Radiomics has the potential to distinguish between lipomas and ALTs of the extremities and their discrimination ability is no weaker than the senor radiologists. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Yaozhou Tang
- Department of Radiology, The First Affiliated Hospital, China Medical University, Shenyang, Liaoning, China
| | - Jingjing Cui
- Department of Research and Development, Shanghai United Imaging Intelligence, Shanghai, China
| | - Jingyi Zhu
- Department of Radiology, The First Affiliated Hospital, China Medical University, Shenyang, Liaoning, China
| | - Guoguang Fan
- Department of Radiology, The First Affiliated Hospital, China Medical University, Shenyang, Liaoning, China
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Haidey J, Low G, Wilson MP. Radiomics-based approaches outperform visual analysis for differentiating lipoma from atypical lipomatous tumors: a review. Skeletal Radiol 2022; 52:1089-1100. [PMID: 36385583 DOI: 10.1007/s00256-022-04232-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 11/02/2022] [Accepted: 11/06/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND Differentiating atypical lipomatous tumors (ALTs) and well-differentiated liposarcomas (WDLs) from benign lipomatous lesions is important for guiding clinical management, though conventional visual analysis of these lesions is challenging due to overlap of imaging features. Radiomics-based approaches may serve as a promising alternative and/or supplementary diagnostic approach to conventional imaging. PURPOSE The purpose of this study is to review the practice of radiomics-based imaging and systematically evaluate the literature available for studies evaluating radiomics applied to differentiating ALTs/WDLs from benign lipomas. REVIEW A background review of the radiomic workflow is provided, outlining the steps of image acquisition, segmentation, feature extraction, and model development. Subsequently, a systematic review of MEDLINE, EMBASE, Scopus, the Cochrane Library, and the grey literature was performed from inception to June 2022 to identify size studies using radiomics for differentiating ALTs/WDLs from benign lipomas. Radiomic models were shown to outperform conventional analysis in all but one model with a sensitivity ranging from 68 to 100% and a specificity ranging from 84 to 100%. However, current approaches rely on user input and no studies used a fully automated method for segmentation, contributing to interobserver variability and decreasing time efficiency. CONCLUSION Radiomic models may show improved performance for differentiating ALTs/WDLs from benign lipomas compared to conventional analysis. However, considerable variability between radiomic approaches exists and future studies evaluating a standardized radiomic model with a multi-institutional study design and preferably fully automated segmentation software are needed before clinical application can be more broadly considered.
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Affiliation(s)
- Jordan Haidey
- Department of Radiology and Diagnostic Imaging, University of Alberta, 2B2.41 WMC, 8440-112 Street NW, Edmonton, Alberta, T6G 2B7, Canada.
| | - Gavin Low
- Department of Radiology and Diagnostic Imaging, University of Alberta, 2B2.41 WMC, 8440-112 Street NW, Edmonton, Alberta, T6G 2B7, Canada
| | - Mitchell P Wilson
- Department of Radiology and Diagnostic Imaging, University of Alberta, 2B2.41 WMC, 8440-112 Street NW, Edmonton, Alberta, T6G 2B7, Canada
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Crombé A, Roulleau‐Dugage M, Italiano A. The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy. CANCER COMMUNICATIONS (LONDON, ENGLAND) 2022; 42:1288-1313. [PMID: 36260064 PMCID: PMC9759765 DOI: 10.1002/cac2.12373] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 09/20/2022] [Accepted: 10/08/2022] [Indexed: 01/25/2023]
Abstract
Soft-tissue sarcomas (STS) represent a group of rare and heterogeneous tumors associated with several challenges, including incorrect or late diagnosis, the lack of clinical expertise, and limited therapeutic options. Digital pathology and radiomics represent transformative technologies that appear promising for improving the accuracy of cancer diagnosis, characterization and monitoring. Herein, we review the potential role of the application of digital pathology and radiomics in managing patients with STS. We have particularly described the main results and the limits of the studies using radiomics to refine diagnosis or predict the outcome of patients with soft-tissue sarcomas. We also discussed the current limitation of implementing radiomics in routine settings. Standard management approaches for STS have not improved since the early 1970s. Immunotherapy has revolutionized cancer treatment; nonetheless, immuno-oncology agents have not yet been approved for patients with STS. However, several lines of evidence indicate that immunotherapy may represent an efficient therapeutic strategy for this group of diseases. Thus, we emphasized the remarkable potential of immunotherapy in sarcoma treatment by focusing on recent data regarding the immune landscape of these tumors. We have particularly emphasized the fact that the development of immunotherapy for sarcomas is not an aspect of histology (except for alveolar soft-part sarcoma) but rather that of the tumor microenvironment. Future studies investigating immunotherapy strategies in sarcomas should incorporate at least the presence of tertiary lymphoid structures as a stratification factor in their design, besides including a strong translational program that will allow for a better understanding of the determinants involved in sensitivity and treatment resistance to immune-oncology agents.
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Affiliation(s)
- Amandine Crombé
- Department of ImagingInstitut BergoniéBordeauxNouvelle‐AquitaineF‐33076France,Faculty of MedicineUniversity of BordeauxBordeauxNouvelle‐AquitaineF‐33000France
| | | | - Antoine Italiano
- Faculty of MedicineUniversity of BordeauxBordeauxNouvelle‐AquitaineF‐33000France,Early Phase Trials and Sarcoma UnitInstitut BergoniéBordeauxNouvelle‐AquitaineF‐33076France
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Fradet G, Ayde R, Bottois H, El Harchaoui M, Khaled W, Drapé JL, Pilleul F, Bouhamama A, Beuf O, Leporq B. Prediction of lipomatous soft tissue malignancy on MRI: comparison between machine learning applied to radiomics and deep learning. Eur Radiol Exp 2022; 6:41. [PMID: 36071368 PMCID: PMC9452614 DOI: 10.1186/s41747-022-00295-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 07/05/2022] [Indexed: 12/02/2022] Open
Abstract
Objectives Malignancy of lipomatous soft-tissue tumours diagnosis is suspected on magnetic resonance imaging (MRI) and requires a biopsy. The aim of this study is to compare the performances of MRI radiomic machine learning (ML) analysis with deep learning (DL) to predict malignancy in patients with lipomas oratypical lipomatous tumours. Methods Cohort include 145 patients affected by lipomatous soft tissue tumours with histology and fat-suppressed gadolinium contrast-enhanced T1-weighted MRI pulse sequence. Images were collected between 2010 and 2019 over 78 centres with non-uniform protocols (three different magnetic field strengths (1.0, 1.5 and 3.0 T) on 16 MR systems commercialised by four vendors (General Electric, Siemens, Philips, Toshiba)). Two approaches have been compared: (i) ML from radiomic features with and without batch correction; and (ii) DL from images. Performances were assessed using 10 cross-validation folds from a test set and next in external validation data. Results The best DL model was obtained using ResNet50 (resulting into an area under the curve (AUC) of 0.87 ± 0.11 (95% CI 0.65−1). For ML/radiomics, performances reached AUCs equal to 0.83 ± 0.12 (95% CI 0.59−1) and 0.99 ± 0.02 (95% CI 0.95−1) on test cohort using gradient boosting without and with batch effect correction, respectively. On the external cohort, the AUC of the gradient boosting model was equal to 0.80 and for an optimised decision threshold sensitivity and specificity were equal to 100% and 32% respectively. Conclusions In this context of limited observations, batch-effect corrected ML/radiomics approaches outperformed DL-based models. Supplementary Information The online version contains supplementary material available at 10.1186/s41747-022-00295-9.
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Affiliation(s)
| | | | | | | | - Wassef Khaled
- Service de Radiologie B, Groupe Hospitalier Cochin, AP-HP Centre, Université de Paris, Paris, France
| | - Jean-Luc Drapé
- Service de Radiologie B, Groupe Hospitalier Cochin, AP-HP Centre, Université de Paris, Paris, France
| | - Frank Pilleul
- Université Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220 U1206, Villeurbanne, France.,Department of Radiology, Centre de lutte contre le cancer Léon Bérard, Lyon, France
| | - Amine Bouhamama
- Université Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220 U1206, Villeurbanne, France.,Department of Radiology, Centre de lutte contre le cancer Léon Bérard, Lyon, France
| | - Olivier Beuf
- Université Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220 U1206, Villeurbanne, France
| | - Benjamin Leporq
- Université Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220 U1206, Villeurbanne, France
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Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies—a scoping review. Eur Radiol 2022; 32:7173-7184. [PMID: 35852574 PMCID: PMC9474640 DOI: 10.1007/s00330-022-08981-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 05/31/2022] [Accepted: 06/22/2022] [Indexed: 11/16/2022]
Abstract
Abstract Musculoskeletal malignancies are a rare type of cancer. Consequently, sufficient imaging data for machine learning (ML) applications is difficult to obtain. The main purpose of this review was to investigate whether ML is already having an impact on imaging-driven diagnosis of musculoskeletal malignancies and what the respective reasons for this might be. A scoping review was conducted by a radiologist, an orthopaedic surgeon and a data scientist to identify suitable articles based on the PRISMA statement. Studies meeting the following criteria were included: primary malignant musculoskeletal tumours, machine/deep learning application, imaging data or data retrieved from images, human/preclinical, English language and original research. Initially, 480 articles were found and 38 met the eligibility criteria. Several continuous and discrete parameters related to publication, patient distribution, tumour specificities, ML methods, data and metrics were extracted from the final articles. For the synthesis, diagnosis-oriented studies were further examined by retrieving the number of patients and labels and metric scores. No significant correlations between metrics and mean number of samples were found. Several studies presented that ML could support imaging-driven diagnosis of musculoskeletal malignancies in distinct cases. However, data quality and quantity must be increased to achieve clinically relevant results. Compared to the experience of an expert radiologist, the studies used small datasets and mostly included only one type of data. Key to critical advancement of ML models for rare diseases such as musculoskeletal malignancies is a systematic, structured data collection and the establishment of (inter)national networks to obtain substantial datasets in the future. Key Points • Machine learning does not yet significantly impact imaging-driven diagnosis for musculoskeletal malignancies compared to other disciplines such as lung, breast or CNS cancer. • Research in the area of musculoskeletal tumour imaging and machine learning is still very limited. • Machine learning in musculoskeletal tumour imaging is impeded by insufficient availability of data and rarity of the disease.
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22
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Starmans MPA, Ho LS, Smits F, Beije N, de Kruijff I, de Jong JJ, Somford DM, Boevé ER, te Slaa E, Cauberg ECC, Klaver S, van der Heijden AG, Wijburg CJ, van de Luijtgaarden ACM, van Melick HHE, Cauffman E, de Vries P, Jacobs R, Niessen WJ, Visser JJ, Klein S, Boormans JL, van der Veldt AAM. Optimization of Preoperative Lymph Node Staging in Patients with Muscle-Invasive Bladder Cancer Using Radiomics on Computed Tomography. J Pers Med 2022; 12:726. [PMID: 35629148 PMCID: PMC9147130 DOI: 10.3390/jpm12050726] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 04/27/2022] [Accepted: 04/27/2022] [Indexed: 12/10/2022] Open
Abstract
Approximately 25% of the patients with muscle-invasive bladder cancer (MIBC) who are clinically node negative have occult lymph node metastases at radical cystectomy (RC) and pelvic lymph node dissection. The aim of this study was to evaluate preoperative CT-based radiomics to differentiate between pN+ and pN0 disease in patients with clinical stage cT2-T4aN0-N1M0 MIBC. Patients with cT2-T4aN0-N1M0 MIBC, of whom preoperative CT scans and pathology reports were available, were included from the prospective, multicenter CirGuidance trial. After manual segmentation of the lymph nodes, 564 radiomics features were extracted. A combination of different machine-learning methods was used to develop various decision models to differentiate between patients with pN+ and pN0 disease. A total of 209 patients (159 pN0; 50 pN+) were included, with a total of 3153 segmented lymph nodes. None of the individual radiomics features showed significant differences between pN+ and pN0 disease, and none of the radiomics models performed substantially better than random guessing. Hence, CT-based radiomics does not contribute to differentiation between pN+ and pN0 disease in patients with cT2-T4aN0-N1M0 MIBC.
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Affiliation(s)
- Martijn P. A. Starmans
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (L.S.H.); (F.S.); (W.J.N.); (J.J.V.); (S.K.); (A.A.M.v.d.V.)
| | - Li Shen Ho
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (L.S.H.); (F.S.); (W.J.N.); (J.J.V.); (S.K.); (A.A.M.v.d.V.)
| | - Fokko Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (L.S.H.); (F.S.); (W.J.N.); (J.J.V.); (S.K.); (A.A.M.v.d.V.)
| | - Nick Beije
- Department of Medical Oncology, Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands; (N.B.); (I.d.K.)
| | - Inge de Kruijff
- Department of Medical Oncology, Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands; (N.B.); (I.d.K.)
| | - Joep J. de Jong
- Department of Urology, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (J.J.d.J.); (J.L.B.)
| | - Diederik M. Somford
- Department of Urology, Canisius-Wilhelmina Hospital, 6532 SZ Nijmegen, The Netherlands;
| | - Egbert R. Boevé
- Department of Urology, Franciscus Gasthuis & Vlietland, 3045 PM Rotterdam, The Netherlands;
| | - Ed te Slaa
- Department of Urology, Isala, 8025 AB Zwolle, The Netherlands; (E.t.S.); (E.C.C.C.)
| | | | - Sjoerd Klaver
- Department of Urology, Maasstad, 3079 DZ Rotterdam, The Netherlands;
| | | | - Carl J. Wijburg
- Department of Urology, Rijnstate, 6815 AD Arnhem, The Netherlands;
| | | | - Harm H. E. van Melick
- Department of Urology, St Antonius Ziekenhuis, Nieuwegein, 3543 AZ Utrecht, The Netherlands;
| | - Ella Cauffman
- Department of Urology, Zuyderland, 6162 BG Sittard, The Netherlands; (E.C.); (P.d.V.); (R.J.)
| | - Peter de Vries
- Department of Urology, Zuyderland, 6162 BG Sittard, The Netherlands; (E.C.); (P.d.V.); (R.J.)
| | - Rens Jacobs
- Department of Urology, Zuyderland, 6162 BG Sittard, The Netherlands; (E.C.); (P.d.V.); (R.J.)
| | - Wiro J. Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (L.S.H.); (F.S.); (W.J.N.); (J.J.V.); (S.K.); (A.A.M.v.d.V.)
| | - Jacob J. Visser
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (L.S.H.); (F.S.); (W.J.N.); (J.J.V.); (S.K.); (A.A.M.v.d.V.)
| | - Stefan Klein
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (L.S.H.); (F.S.); (W.J.N.); (J.J.V.); (S.K.); (A.A.M.v.d.V.)
| | - Joost L. Boormans
- Department of Urology, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (J.J.d.J.); (J.L.B.)
| | - Astrid A. M. van der Veldt
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (L.S.H.); (F.S.); (W.J.N.); (J.J.V.); (S.K.); (A.A.M.v.d.V.)
- Department of Medical Oncology, Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands; (N.B.); (I.d.K.)
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Discrimination of lipoma from atypical lipomatous tumor/well-differentiated liposarcoma using magnetic resonance imaging radiomics combined with machine learning. Jpn J Radiol 2022; 40:951-960. [DOI: 10.1007/s11604-022-01278-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 03/25/2022] [Indexed: 10/18/2022]
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Yang Y, Zhou Y, Zhou C, Ma X. Novel computer aided diagnostic models on multimodality medical images to differentiate well differentiated liposarcomas from lipomas approached by deep learning methods. Orphanet J Rare Dis 2022; 17:158. [PMID: 35392952 PMCID: PMC8991509 DOI: 10.1186/s13023-022-02304-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 03/23/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Deep learning methods have great potential to predict tumor characterization, such as histological diagnosis and genetic aberration. The objective of this study was to evaluate and validate the predictive performance of multimodality imaging-derived models using computer-aided diagnostic (CAD) methods for prediction of MDM2 gene amplification to identify well-differentiated liposarcoma (WDLPS) and lipoma. MATERIALS AND METHODS All 127 patients from two institutions were included with 89 patients in one institution for model training and 38 patients in the other institution for external validation between January 2012 and December 2018. For each modality, handcrafted radiomics analysis with manual segmentation was applied to extract 851 features for each modality, and six pretrained convolutional neural networks (CNNs) extracted 512-2048 deep learning features automatically. Extracted imaging-based features were selected via univariate filter selection methods and the recursive feature elimination algorithm, which were then classified by support vector machine for model construction. Integrated with two significant clinical variables, age and LDH level, a clinical-radiological model was constructed for identification WDLPS and lipoma. All differentiation models were evaluated using the area under the receiver operating characteristics curve (AUC) and their 95% confidence interval (CI). RESULTS The multimodality model on deep learning features extracted from ResNet50 algorithm (RN-DL model) performed great differentiation performance with an AUC of 0.995 (95% CI 0.987-1.000) for the training cohort, and an AUC of 0.950 (95% CI 0.886-1.000), accuracy of 92.11%, sensitivity of 95.00% (95% CI 73.06-99.74%), specificity of 88.89% (95% CI 63.93-98.05%) in external validation. The integrated clinical-radiological model represented an AUC of 0.996 (95% CI 0.989-1.000) for the training cohort, and an AUC of 0.942 (95% CI 0.867-1.000), accuracy of 86.84%, sensitivity of 95.00% (95% CI 73.06-99.74%), and specificity of 77.78% (95% CI 51.92-92.63%) in external validation. CONCLUSIONS Imaging-based multimodality models represent effective discrimination abilities between WDLPS and lipoma via CAD methods, and might be a practicable approach in assistance of treatment decision.
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Affiliation(s)
- Yuhan Yang
- Department of Pediatric Surgery, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Yin Zhou
- Department of Pediatric Surgery, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Chen Zhou
- Department of Pediatric Surgery, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Xuelei Ma
- Department of Biotherapy and Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China.
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Erginoz E, Uludag SS, Cavus GH, Zengin K, Ozcelik MF. Clinicopathological features and management of colonic lipomas: Case reports. Medicine (Baltimore) 2022; 101:e29004. [PMID: 35451395 PMCID: PMC8913087 DOI: 10.1097/md.0000000000029004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 02/16/2022] [Indexed: 01/04/2023] Open
Abstract
INTRODUCTION Colonic lipomas are benign tumors of adipose tissue that are often asymptomatic, but they may present with rectal bleeding or obstructive symptoms. These tumors are unique in that they are rarely encountered within the gastrointestinal system and can mimic malignant tumors in appearance. Surgical resection and endoscopic removal of tumors have been shown to be successful in their management. PATIENT CONCERNS In this report, we present 3 cases of colonic lipomas, 2 of which are located in the cecum and the other within the sigmoid colon. The presenting symptoms of the patients included abdominal pain, constipation, and dyspepsia. DIAGNOSIS Patients typically presented with anemia and an elevated C-reactive protein count. Colonoscopic and computerized tomography findings were used for diagnosis. INTERVENTIONS Hemicolectomy was performed, depending on the localization, and the pathologic specimens were consistent with lipoma. OUTCOMES Surgical resection was curative in all patients. The postoperative period was uneventful in all patients and all patients are symptom-free and alive at 3 years follow-up. CONCLUSION Colonic lipomas are benign mesenchymal tumors of the gastrointestinal system with a male predominance and are observed within the fourth to sixth decades of life. Various genetic abnormalities have been reported and they have been linked to the formation of intussusception. The squeeze sign on radiological imaging, cushion sign and tenting sign in colonoscopy, and naked fat sign during pathologic examination is helpful towards reaching a diagnosis. Surgical resection is the treatment of choice but minimally invasive endoscopic approaches have also been shown to be successful.
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Affiliation(s)
- Ergin Erginoz
- Department of General Surgery, Istanbul University Cerrahpasa – Cerrahpasa School of Medicine, Istanbul, Turkey
| | - Server Sezgin Uludag
- Department of General Surgery, Istanbul University Cerrahpasa – Cerrahpasa School of Medicine, Istanbul, Turkey
| | - Gokce Hande Cavus
- Department of Pathology, Istanbul University Cerrahpasa – Cerrahpasa School of Medicine, Istanbul, Turkey
| | - Kagan Zengin
- Department of General Surgery, Istanbul University Cerrahpasa – Cerrahpasa School of Medicine, Istanbul, Turkey
| | - Mehmet Faik Ozcelik
- Department of General Surgery, Istanbul University Cerrahpasa – Cerrahpasa School of Medicine, Istanbul, Turkey
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Application Values of 2D and 3D Radiomics Models Based on CT Plain Scan in Differentiating Benign from Malignant Ovarian Tumors. BIOMED RESEARCH INTERNATIONAL 2022; 2022:5952296. [PMID: 35224097 PMCID: PMC8872698 DOI: 10.1155/2022/5952296] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 01/25/2022] [Indexed: 12/23/2022]
Abstract
Background Accurate identification of ovarian tumors as benign or malignant is highly crucial. Radiomics is a new branch of imaging that has emerged in recent years to replace the traditional naked eye qualitative diagnosis. Objective This study is aimed at exploring the difference in the application potential of two- (2D) and three-dimensional (3D) radiomics models based on CT plain scan in differentiating benign from malignant ovarian tumors. Method A retrospective analysis was performed on 140 patients with ovarian tumors confirmed by surgery and pathology in our hospital from July 2017 to August 2020. These 140 patients were divided into benign group and malignant group according to the pathological results. The ITK-SNAP software was used to outline the regions-of-interest (ROI) of 2D or 3D tumors on the CT plain scan image of each patient; the texture features were extracted through analysis kit (AK), and the cases were randomly divided into training groups (n = 99) and validation group (n = 41) in a ratio of 7 : 3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to perform dimensionality reduction, followed by the construction of the radiomics nomogram model using the logistic regression method. The receiver operating characteristic (ROC) curve was drawn, and the calibration curve and decision curve analysis (DCA) were used to evaluate and verify the results of the radiomics nomogram and compare the differences between 2D and 3D diagnostic performance. Results There were 396 quantitative radiomics feature parameters extracted from 2D group and the 3D group, respectively. The area under the curve (AUC) of the radiomics nomogram of the 2D training group and the validation group were 0.96 and 0.97, respectively. The accuracy, specificity, and sensitivity of the training set were 92.9%, 88.9%, and 96.3%, respectively, and those of the validation set were 90.2%, 82.6%, and 100.0%, respectively. The AUCs of the radiomics nomogram of the 3D training group and validation group were 0.96% and 0.99%, respectively. The accuracy, sensitivity, and specificity of the training set were 92.9%, 96.3%, and 88.9%, respectively, and those of the validation set were 97.6%, 95.7%, and 100.0%, respectively. DeLong's test indicated that there was no statistical significance between the two sets (P > 0.05). Conclusions For the differential diagnosis of benign and malignant ovarian tumors, the 2D and 3D radiomics nomogram models exhibited comparable diagnostic performance. Considering that the 2D model was cost-effective and time-efficient, it was more recommended to use 2D features in future research.
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Starmans MPA, Timbergen MJM, Vos M, Renckens M, Grünhagen DJ, van Leenders GJLH, Dwarkasing RS, Willemssen FEJA, Niessen WJ, Verhoef C, Sleijfer S, Visser JJ, Klein S. Differential Diagnosis and Molecular Stratification of Gastrointestinal Stromal Tumors on CT Images Using a Radiomics Approach. J Digit Imaging 2022; 35:127-136. [PMID: 35088185 PMCID: PMC8921463 DOI: 10.1007/s10278-022-00590-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 01/05/2022] [Accepted: 01/14/2022] [Indexed: 12/21/2022] Open
Abstract
Treatment planning of gastrointestinal stromal tumors (GISTs) includes distinguishing GISTs from other intra-abdominal tumors and GISTs’ molecular analysis. The aim of this study was to evaluate radiomics for distinguishing GISTs from other intra-abdominal tumors, and in GISTs, predict the c-KIT, PDGFRA, BRAF mutational status, and mitotic index (MI). Patients diagnosed at the Erasmus MC between 2004 and 2017, with GIST or non-GIST intra-abdominal tumors and a contrast-enhanced venous-phase CT, were retrospectively included. Tumors were segmented, from which 564 image features were extracted. Prediction models were constructed using a combination of machine learning approaches. The evaluation was performed in a 100 × random-split cross-validation. Model performance was compared to that of three radiologists. One hundred twenty-five GISTs and 122 non-GISTs were included. The GIST vs. non-GIST radiomics model had a mean area under the curve (AUC) of 0.77. Three radiologists had an AUC of 0.69, 0.76, and 0.84, respectively. The radiomics model had an AUC of 0.52 for c-KIT, 0.56 for c-KIT exon 11, and 0.52 for the MI. The numbers of PDGFRA, BRAF, and other c-KIT mutations were too low for analysis. Our radiomics model was able to distinguish GISTs from non-GISTs with a performance similar to three radiologists, but less observer dependent. Therefore, it may aid in the early diagnosis of GIST, facilitating rapid referral to specialized treatment centers. As the model was not able to predict any genetic or molecular features, it cannot aid in treatment planning yet.
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Affiliation(s)
- Martijn P A Starmans
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands.
| | - Milea J M Timbergen
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Melissa Vos
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Michel Renckens
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Dirk J Grünhagen
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | - Roy S Dwarkasing
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | - Wiro J Niessen
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands
- Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | - Cornelis Verhoef
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Stefan Sleijfer
- Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Jacob J Visser
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Stefan Klein
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands
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Castillo T. JM, Arif M, Starmans MPA, Niessen WJ, Bangma CH, Schoots IG, Veenland JF. Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics. Cancers (Basel) 2021; 14:12. [PMID: 35008177 PMCID: PMC8749796 DOI: 10.3390/cancers14010012] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/01/2021] [Accepted: 12/03/2021] [Indexed: 12/16/2022] Open
Abstract
The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning- and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods, using various external data sets is crucial. While both deep-learning and radiomics approaches have been compared based on the same data set of one center, the comparison of the performances of both approaches on various data sets from different centers and different scanners is lacking. The goal of this study was to compare the performance of a deep-learning model with the performance of a radiomics model for the significant-PCa diagnosis of the cohorts of various patients. We included the data from two consecutive patient cohorts from our own center (n = 371 patients), and two external sets of which one was a publicly available patient cohort (n = 195 patients) and the other contained data from patients from two hospitals (n = 79 patients). Using multiparametric MRI (mpMRI), the radiologist tumor delineations and pathology reports were collected for all patients. During training, one of our patient cohorts (n = 271 patients) was used for both the deep-learning- and radiomics-model development, and the three remaining cohorts (n = 374 patients) were kept as unseen test sets. The performances of the models were assessed in terms of their area under the receiver-operating-characteristic curve (AUC). Whereas the internal cross-validation showed a higher AUC for the deep-learning approach, the radiomics model obtained AUCs of 0.88, 0.91 and 0.65 on the independent test sets compared to AUCs of 0.70, 0.73 and 0.44 for the deep-learning model. Our radiomics model that was based on delineated regions resulted in a more accurate tool for significant-PCa classification in the three unseen test sets when compared to a fully automated deep-learning model.
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Affiliation(s)
- Jose M. Castillo T.
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (J.M.C.T.); (M.A.); (M.P.A.S.); (W.J.N.); (I.G.S.)
| | - Muhammad Arif
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (J.M.C.T.); (M.A.); (M.P.A.S.); (W.J.N.); (I.G.S.)
| | - Martijn P. A. Starmans
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (J.M.C.T.); (M.A.); (M.P.A.S.); (W.J.N.); (I.G.S.)
| | - Wiro J. Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (J.M.C.T.); (M.A.); (M.P.A.S.); (W.J.N.); (I.G.S.)
- Faculty of Applied Sciences, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands
| | - Chris H. Bangma
- Department of Urology, Erasmus MC, 3015 GD Rotterdam, The Netherlands;
| | - Ivo G. Schoots
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (J.M.C.T.); (M.A.); (M.P.A.S.); (W.J.N.); (I.G.S.)
| | - Jifke F. Veenland
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (J.M.C.T.); (M.A.); (M.P.A.S.); (W.J.N.); (I.G.S.)
- Department of Medical Informatics, Erasmus MC, 3015 GD Rotterdam, The Netherlands
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Williams TL, Saadat LV, Gonen M, Wei A, Do RKG, Simpson AL. Radiomics in surgical oncology: applications and challenges. Comput Assist Surg (Abingdon) 2021; 26:85-96. [PMID: 34902259 DOI: 10.1080/24699322.2021.1994014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
Surgery is a curative treatment option for many patients with malignant tumors. Increased attention has focused on the combination of surgery with chemotherapy, as multimodality treatment has been associated with promising results in certain cancer types. Despite these data, there remains clinical equipoise on optimal timing and patient selection for neoadjuvant or adjuvant strategies. Radiomics, an emerging field involving the extraction of advanced features from radiographic images, has the potential to revolutionize oncologic treatment and contribute to the advance of personalized therapy by helping predict tumor behavior and response to therapy. This review analyzes and summarizes studies that use radiomics with machine learning in patients who have received neoadjuvant and/or adjuvant chemotherapy to predict prognosis, recurrence, survival, and therapeutic response for various cancer types. While studies in both neoadjuvant and adjuvant settings demonstrate above average performance on ability to predict progression-free and overall survival, there remain many challenges and limitations to widespread implementation of this technology. The lack of standardization of common practices to analyze radiomics, limited data sharing, and absence of auto-segmentation have hindered the inclusion and rapid adoption of radiomics in prospective, clinical studies.
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Affiliation(s)
- Travis L Williams
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lily V Saadat
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mithat Gonen
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alice Wei
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Amber L Simpson
- School of Computing, Queen's University, Kingston, ON, Canada.,Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
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Role of FUS-CHOP in Myxoid Liposarcoma via miR-486/CDK4 Axis. Biochem Genet 2021; 60:1095-1106. [PMID: 34792704 DOI: 10.1007/s10528-021-10151-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: 03/19/2021] [Accepted: 10/28/2021] [Indexed: 10/19/2022]
Abstract
This study aimed to explore the roles and relationship between FUsed in Sarcoma (FUS)-C/EBP HOmologous Protein (CHOP), microRNA (miR)-486 and cyclin dependent kinase 4 (CDK4) in myxoid liposarcoma, and determined whether FUS-CHOP can regulate proliferation and apoptosis of myxoid liposarcoma cells by regulating miR-486/CDK4 axis. The levels of miR-486, CDK4 and FUS-CHOP in myxoid liposarcoma samples/adjacent normal muscle tissues and myxoid liposarcoma/human adipose-derived stem cell line were evaluated using reverse transcription-quantitative polymerase chain reaction and western blotting. Cell proliferation and apoptosis were performed using 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide and flow cytometry, respectively. Furthermore, the apoptosis-related proteins were determined using Western blot assay. We found that miR-486 was down-regulated, FUS-CHOP and CDK4 were up-regulated in myxoid liposarcoma tissues and myxoid liposarcoma cell lines. Moreover, FUS-CHOP-siRNA distinctly suppressed FUS-CHOP level and increased miR-486 levels in 1955/91 cells. Our results demonstrated that knockdown of FUS-CHOP by siRNA inhibited 1955/91 growth, promoted cell apoptosis and enhanced cleaved Caspase3 protein expression. However, all these data were reversed by miR-486 inhibitor. Similarly, compared to mimic control, miR-486 mimic markedly reduced 1955/91 cells growth, induced cell apoptosis and fortified cleaved Caspase3 level, while these results were abolished by CDK4-plasmid. Collectively, our observations clearly suggested that FUS-CHOP regulated myxoid liposarcoma cell proliferation and apoptosis by the regulation of miR-486/CDK4 axis, indicating the potential use of FUS-CHOP-siRNA as a promising therapy for myxoid liposarcoma.
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Diagnosis strategy of adipocytic soft-tissue tumors in adults: a consensus from European experts. Eur J Surg Oncol 2021; 48:518-525. [PMID: 34688512 DOI: 10.1016/j.ejso.2021.10.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/08/2021] [Accepted: 10/11/2021] [Indexed: 02/07/2023] Open
Abstract
Fat-containing tumors are very commonly found in daily practice with benign lipoma accounting for the majority of superficial tumors. Overlap in imaging findings between benign and intermediate or malignant fat-containing tumor is common. Guidelines recommend a core needle biopsy (CNB) for all deep tumors, and superficial tumors over 3 cm. However, specific strategy for diagnosis and referral to a sarcoma center should be applied on adipocytic tumors. The aim of this consensus statement is to provide a specific algorithm for adipocytic tumors, to discriminate patients who do require a CNB for preoperative diagnosis from those who can simply undergo active surveillance or a simple excision.
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Starmans MPA, Buisman FE, Renckens M, Willemssen FEJA, van der Voort SR, Groot Koerkamp B, Grünhagen DJ, Niessen WJ, Vermeulen PB, Verhoef C, Visser JJ, Klein S. Distinguishing pure histopathological growth patterns of colorectal liver metastases on CT using deep learning and radiomics: a pilot study. Clin Exp Metastasis 2021; 38:483-494. [PMID: 34533669 PMCID: PMC8510954 DOI: 10.1007/s10585-021-10119-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 08/23/2021] [Indexed: 02/05/2023]
Abstract
Histopathological growth patterns (HGPs) are independent prognosticators for colorectal liver metastases (CRLM). Currently, HGPs are determined postoperatively. In this study, we evaluated radiomics for preoperative prediction of HGPs on computed tomography (CT), and its robustness to segmentation and acquisition variations. Patients with pure HGPs [i.e. 100% desmoplastic (dHGP) or 100% replacement (rHGP)] and a CT-scan who were surgically treated at the Erasmus MC between 2003-2015 were included retrospectively. Each lesion was segmented by three clinicians and a convolutional neural network (CNN). A prediction model was created using 564 radiomics features and a combination of machine learning approaches by training on the clinician's and testing on the unseen CNN segmentations. The intra-class correlation coefficient (ICC) was used to select features robust to segmentation variations; ComBat was used to harmonize for acquisition variations. Evaluation was performed through a 100 × random-split cross-validation. The study included 93 CRLM in 76 patients (48% dHGP; 52% rHGP). Despite substantial differences between the segmentations of the three clinicians and the CNN, the radiomics model had a mean area under the curve of 0.69. ICC-based feature selection or ComBat yielded no improvement. Concluding, the combination of a CNN for segmentation and radiomics for classification has potential for automatically distinguishing dHGPs from rHGP, and is robust to segmentation and acquisition variations. Pending further optimization, including extension to mixed HGPs, our model may serve as a preoperative addition to postoperative HGP assessment, enabling further exploitation of HGPs as a biomarker.
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Affiliation(s)
- Martijn P A Starmans
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
| | - Florian E Buisman
- Department of Surgery, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Michel Renckens
- Department of Surgery, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | | | | | - Bas Groot Koerkamp
- Department of Surgery, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Dirk J Grünhagen
- Department of Surgery, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Wiro J Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | - Peter B Vermeulen
- Translational Cancer Research Unit, Department of Oncological Research, Oncology Center, GZA Hospitals Campus Sint-Augustinus and University of Antwerp, Antwerp, Belgium
| | - Cornelis Verhoef
- Department of Surgery, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Jacob J Visser
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Stefan Klein
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
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Nardone V, Boldrini L, Grassi R, Franceschini D, Morelli I, Becherini C, Loi M, Greto D, Desideri I. Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment. Cancers (Basel) 2021; 13:cancers13143590. [PMID: 34298803 PMCID: PMC8303203 DOI: 10.3390/cancers13143590] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 07/12/2021] [Accepted: 07/14/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary This review based on a literature search aims at showing the impact of Texture Analysis in the prediction of response to neoadjuvant radiotherapy and/or chemoradiotherapy. The manuscript explores radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma and rectal cancer in order to shed a light in the setting of neoadjuvant radiotherapy that can be used to tailor the best subsequent therapeutical strategy. Abstract Introduction: Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy. Materials and Methods: Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used. Results: This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer. Conclusions: Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.
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Affiliation(s)
- Valerio Nardone
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy; (V.N.); (R.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Luca Boldrini
- Radiation Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy; (V.N.); (R.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Davide Franceschini
- Radiotherapy and Radiosurgery Department, IRCCS Humanitas Research Hospital, via Manzoni 56, 20089 Milan, Italy;
| | - Ilaria Morelli
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy;
- Correspondence: ; Tel.: +39-055-7947719
| | - Carlotta Becherini
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy;
| | - Mauro Loi
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
| | - Daniela Greto
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
| | - Isacco Desideri
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy
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Gómez-Ramírez J, González-Rosa JJ. Intra- and interhemispheric symmetry of subcortical brain structures: a volumetric analysis in the aging human brain. Brain Struct Funct 2021; 227:451-462. [PMID: 34089103 DOI: 10.1007/s00429-021-02305-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 05/19/2021] [Indexed: 12/20/2022]
Abstract
Here, we address the hemispheric interdependency of subcortical structures in the aging human brain. In particular, we investigated whether subcortical volume variations can be explained by the adjacency of structures in the same hemisphere or are due to the interhemispheric development of mirror subcortical structures in the brain. Seven subcortical structures in each hemisphere were automatically segmented in a large sample of 3312 magnetic resonance imaging (MRI) studies of elderly individuals in their 70s and 80s. We performed Eigenvalue analysis, and found that anatomic volumes in the limbic system and basal ganglia show similar statistical dependency whether considered in the same hemisphere (intrahemispherically) or different hemispheres (interhemispherically). Our results indicate that anatomic bilaterality of subcortical volumes is preserved in the aging human brain, supporting the hypothesis that coupling between non-adjacent subcortical structures might act as a mechanism to compensate for the deleterious effects of aging.
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Affiliation(s)
| | - Javier J González-Rosa
- Department of Psychology, Universidad de Cádiz, Cádiz, Spain
- Instituto de Investigación Biomédica de Cádiz (INIBICA), Cádiz, Spain
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Gitto S, Cuocolo R, Albano D, Morelli F, Pescatori LC, Messina C, Imbriaco M, Sconfienza LM. CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies. Insights Imaging 2021; 12:68. [PMID: 34076740 PMCID: PMC8172744 DOI: 10.1186/s13244-021-01008-3] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 05/05/2021] [Indexed: 02/07/2023] Open
Abstract
Background Feature reproducibility and model validation are two main challenges of radiomics. This study aims to systematically review radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas. The ultimate goal is to promote achieving a consensus on these aspects in radiomic workflows and facilitate clinical transferability. Results Out of 278 identified papers, forty-nine papers published between 2008 and 2020 were included. They dealt with radiomics of bone (n = 12) or soft-tissue (n = 37) tumors. Eighteen (37%) studies included a feature reproducibility analysis. Inter-/intra-reader segmentation variability was the theme of reproducibility analysis in 16 (33%) investigations, outnumbering the analyses focused on image acquisition or post-processing (n = 2, 4%). The intraclass correlation coefficient was the most commonly used statistical method to assess reproducibility, which ranged from 0.6 and 0.9. At least one machine learning validation technique was used for model development in 25 (51%) papers, and K-fold cross-validation was the most commonly employed. A clinical validation of the model was reported in 19 (39%) papers. It was performed using a separate dataset from the primary institution (i.e., internal validation) in 14 (29%) studies and an independent dataset related to different scanners or from another institution (i.e., independent validation) in 5 (10%) studies. Conclusions The issues of radiomic feature reproducibility and model validation varied largely among the studies dealing with musculoskeletal sarcomas and should be addressed in future investigations to bring the field of radiomics from a preclinical research area to the clinical stage.
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Affiliation(s)
- Salvatore Gitto
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Riccardo Galeazzi 4, 20161, Milan, Italy.
| | - Renato Cuocolo
- Dipartimento di Medicina Clinica e Chirurgia, Università degli Studi di Napoli "Federico II", Naples, Italy.,Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.,Sezione di Scienze Radiologiche, Dipartimento di Biomedicina, Neuroscienze e Diagnostica Avanzata, Università degli Studi di Palermo, Palermo, Italy
| | | | - Lorenzo Carlo Pescatori
- Assistance Publique - Hôpitaux de Paris (AP-HP), Service d'Imagerie Médicale, CHU Henri Mondor, Créteil, France
| | - Carmelo Messina
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Riccardo Galeazzi 4, 20161, Milan, Italy.,IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Massimo Imbriaco
- Dipartimento di Scienze Biomediche Avanzate, Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Luca Maria Sconfienza
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Riccardo Galeazzi 4, 20161, Milan, Italy.,IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
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Caruso D, Polici M, Zerunian M, Pucciarelli F, Guido G, Polidori T, Landolfi F, Nicolai M, Lucertini E, Tarallo M, Bracci B, Nacci I, Rucci C, Eid M, Iannicelli E, Laghi A. Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications. Cancers (Basel) 2021; 13:cancers13112681. [PMID: 34072366 PMCID: PMC8197789 DOI: 10.3390/cancers13112681] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/26/2021] [Accepted: 05/27/2021] [Indexed: 01/08/2023] Open
Abstract
Simple Summary This Part II is an overview of the main applications of Radiomics in oncologic imaging with a focus on diagnosis, prognosis prediction and assessment of response to therapy in thoracic, genito-urinary, breast, neurologic, hematologic and musculoskeletal oncology. In this part II we describe the radiomic applications, limitations and future perspectives for each pre-eminent tumor. In the future, Radiomics could have a pivotal role in management of cancer patients as an imaging tool to support clinicians in decision making process. However, further investigations need to obtain some stable results and to standardize radiomic analysis (i.e., image acquisitions, segmentation and model building) in clinical routine. Abstract Radiomics has the potential to play a pivotal role in oncological translational imaging, particularly in cancer detection, prognosis prediction and response to therapy evaluation. To date, several studies established Radiomics as a useful tool in oncologic imaging, able to support clinicians in practicing evidence-based medicine, uniquely tailored to each patient and tumor. Mineable data, extracted from medical images could be combined with clinical and survival parameters to develop models useful for the clinicians in cancer patients’ assessment. As such, adding Radiomics to traditional subjective imaging may provide a quantitative and extensive cancer evaluation reflecting histologic architecture. In this Part II, we present an overview of radiomic applications in thoracic, genito-urinary, breast, neurological, hematologic and musculoskeletal oncologic applications.
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Affiliation(s)
- Damiano Caruso
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Michela Polici
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Marta Zerunian
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Francesco Pucciarelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Gisella Guido
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Tiziano Polidori
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Federica Landolfi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Matteo Nicolai
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Elena Lucertini
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Mariarita Tarallo
- Department of Surgery “Pietro Valdoni”, Sapienza University of Rome, 00161 Rome, Italy;
| | - Benedetta Bracci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Ilaria Nacci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Carlotta Rucci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Marwen Eid
- Internal Medicine, Northwell Health Staten Island University Hospital, Staten Island, New York, NY 10305, USA;
| | - Elsa Iannicelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Andrea Laghi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
- Correspondence: ; Tel.: +39-0633775285
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Martin E, Geitenbeek RTJ, Coert JH, Hanff DF, Graven LH, Grünhagen DJ, Verhoef C, Taal W. A Bayesian approach for diagnostic accuracy of malignant peripheral nerve sheath tumors: a systematic review and meta-analysis. Neuro Oncol 2021; 23:557-571. [PMID: 33326583 PMCID: PMC8041346 DOI: 10.1093/neuonc/noaa280] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Background Malignant peripheral nerve sheath tumors (MPNST) carry a dismal prognosis and require early detection and complete resection. However, MPNSTs are prone to sampling errors and biopsies or resections are cumbersome and possibly damaging in benign peripheral nerve sheath tumor (BPNST). This study aimed to systematically review and quantify the diagnostic accuracy of noninvasive tests for distinguishing MPNST from BPNST. Methods Studies on accuracy of MRI, FDG-PET (fluorodeoxyglucose positron emission tomography), and liquid biopsies were identified in PubMed and Embase from 2000 to 2019. Pooled accuracies were calculated using Bayesian bivariate meta-analyses. Individual level-patient data were analyzed for ideal maximum standardized uptake value (SUVmax) threshold on FDG-PET. Results Forty-three studies were selected for qualitative synthesis including data on 1875 patients and 2939 lesions. Thirty-five studies were included for meta-analyses. For MRI, the absence of target sign showed highest sensitivity (0.99, 95% CI: 0.94-1.00); ill-defined margins (0.94, 95% CI: 0.88-0.98); and perilesional edema (0.95, 95% CI: 0.83-1.00) showed highest specificity. For FDG-PET, SUVmax and tumor-to-liver ratio show similar accuracy; sensitivity 0.94, 95% CI: 0.91-0.97 and 0.93, 95% CI: 0.87-0.97, respectively, specificity 0.81, 95% CI: 0.76-0.87 and 0.79, 95% CI: 0.70-0.86, respectively. SUVmax ≥3.5 yielded the best accuracy with a sensitivity of 0.99 (95% CI: 0.93-1.00) and specificity of 0.75 (95% CI: 0.56-0.90). Conclusions Biopsies may be omitted in the presence of a target sign and the absence of ill-defined margins or perilesional edema. Because of diverse radiological characteristics of MPNST, biopsies may still commonly be required. In neurofibromatosis type 1, FDG-PET scans may further reduce biopsies. Ideal SUVmax threshold is ≥3.5.
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Affiliation(s)
- Enrico Martin
- Department of Plastic and Reconstructive Surgery, University Medical Center Utrecht, Utrecht, the Netherlands.,Department of Surgical Oncology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Ritchie T J Geitenbeek
- Department of Plastic and Reconstructive Surgery, University Medical Center Utrecht, Utrecht, the Netherlands.,Department of Surgical Oncology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - J Henk Coert
- Department of Plastic and Reconstructive Surgery, University Medical Center Utrecht, Utrecht, the Netherlands
| | - David F Hanff
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Laura H Graven
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Dirk J Grünhagen
- Department of Surgical Oncology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Cornelis Verhoef
- Department of Surgical Oncology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Walter Taal
- Department of Neuro-Oncology/Neurology, Erasmus Medical Center Cancer Institute, Rotterdam, the Netherlands
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Chianca V, Albano D, Messina C, Vincenzo G, Rizzo S, Del Grande F, Sconfienza LM. An update in musculoskeletal tumors: from quantitative imaging to radiomics. Radiol Med 2021; 126:1095-1105. [PMID: 34009541 DOI: 10.1007/s11547-021-01368-2] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 05/02/2021] [Indexed: 02/08/2023]
Abstract
In the last two decades, relevant progress has been made in the diagnosis of musculoskeletal tumors due to the development of new imaging tools, such as diffusion-weighted imaging, diffusion kurtosis imaging, magnetic resonance spectroscopy, and diffusion tensor imaging. Another important role has been played by the development of artificial intelligence software based on complex algorithms, which employ computing power in the detection of specific tumor types. The aim of this article is to report the most advanced imaging techniques focusing on their advantages in clinical practice.
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Affiliation(s)
- Vito Chianca
- Clinica di Radiologia EOC IIMSI, Lugano, Switzerland. .,Ospedale Evangelico Betania, Napoli, Italy. .,Master in Oncologic Imaging, Diagnostic and Interventional Radiology Department of Translational Research, University of Pisa, Via Roma, 67, 56126, Pisa, Italy.
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milano, Italy.,Sezione di Scienze Radiologiche, Dipartimento Di Biomedicina, Neuroscienze e Diagnostica Avanzata, Università degli Studi di Palermo, Palermo, Italy
| | - Carmelo Messina
- IRCCS Istituto Ortopedico Galeazzi, Milano, Italy.,Dipartimento di Scienze Biomediche Per La Salute, Università degli Studi di Milano, Milano, Italy
| | | | | | | | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, Milano, Italy.,Dipartimento di Scienze Biomediche Per La Salute, Università degli Studi di Milano, Milano, Italy
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Li S, Liu J, Xiong Y, Pang P, Lei P, Zou H, Zhang M, Fan B, Luo P. A radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography. Sci Rep 2021; 11:8730. [PMID: 33888749 PMCID: PMC8062553 DOI: 10.1038/s41598-021-87775-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 04/05/2021] [Indexed: 12/13/2022] Open
Abstract
This paper develops a two-dimensional (2D) radiomics approach with computed tomography (CT) to differentiate between benign and malignant ovarian neoplasms. A retrospective study was conducted from July 2017 to June 2019 for 134 patients with surgically-verified benign or malignant ovarian tumors. The patients were randomly divided in a ratio of 7:3 into two sets, namely a training set (of n = 95) and a test set (of n = 39). The ITK-SNAP software was used to delineate the regions of interest (ROI) associated with lesions of the largest diameters in plain CT image slices. Texture features were extracted by the Analysis Kit (AK) software. The training set was used to select the best features according to the maximum-relevance minimum-redundancy (mRMR) criterion, in addition to the algorithm of the least absolute shrinkage and selection operator (LASSO). Then, we employed a radiomics model for classification via multivariate logistic regression. Finally, we evaluated the overall performance of our method using the receiver operating characteristics (ROC), the DeLong test. and tested in an external validation test sample of patients of ovarian neoplasm. We created a radiomics prediction model from 14 selected features. The radiomic signature was found to be highly discriminative according to the area under the ROC curve (AUC) for both the training set (AUC = 0.88), and the test set (AUC = 0.87). The radiomics nomogram also demonstrated good calibration and differentiation for both the training (AUC = 0.95) and test (AUC = 0.96) samples. External validation tests gave a good performance in radiomic signature (AUC = 0.83) and radiomics nomogram (AUC = 0.95). The decision curve explicitly indicated the clinical usefulness of our nomogram method in the sense that it can influence major clinical events such as the ordering or abortion of other tests, treatments or invasive procedures. Our radiomics model based on plain CT images has a high diagnostic efficiency, which is helpful for the identification and prediction of benign and malignant ovarian neoplasms.
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Affiliation(s)
- Shiyun Li
- Department of Gynecology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China
| | - Jiaqi Liu
- Department of Radiology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China
| | - Yuanhuan Xiong
- Department of Gynecology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China
| | | | - Pinggui Lei
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550000, China
| | - Huachun Zou
- Department of Radiology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China
| | - Mei Zhang
- Department of Radiology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China.
| | - Puying Luo
- Department of Gynecology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China.
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Shu S, Yuan H, Kong X, Wang J, Wang J, Zheng C. The value of multimodality imaging in diagnosis and treatment of cardiac lipoma. BMC Med Imaging 2021; 21:71. [PMID: 33858367 PMCID: PMC8048252 DOI: 10.1186/s12880-021-00603-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 04/01/2021] [Indexed: 02/07/2023] Open
Abstract
Background Cardiac lipoma is a rare primary tumor in the heart and pericardium. Multimodality imaging methods, especially magnetic resonance imaging (MRI), are crucial in detecting and diagnosing cardiac lipomas. Besides, they are of significant importance in management of cardiac lipomas. The aim of this study was to evaluate the value of multimodality imaging methods in diagnosing and treatment of cardiac lipoma by describing a series of cases of cardiac lipoma. Materials and methods Data of patients with cardiac lipoma at a local institution were retrospectively collected. Their imaging findings on echocardiography, computed tomography (CT), and cardiac MRI and clinical management were described in detail. Results 12 patients with cardiac lipoma were retrospectively included with thirteen lipomas found within heart and pericardium. Two patients’ lipoma were symptomatic, while lipomas in other 10 patients were found incidentally. Most lipomas were sensitively detected with echocardiography. Accurate diagnoses were achieved with CT and MRI in all cases. Surgical resection was performed in one symptomatic patient due to the obstruction of the left ventricular outflow tract, while the removal of pericardial lipoma in another symptomatic patient was not possible due to diffuse myocardial infiltration observed in MRI. Based on MRI findings, two patients without clinical symptoms also underwent surgery to prevent the risk of detachment of ventricular lipoma with a narrow pedicle in one patient and potential further thinning of the myocardium by pericardial lipoma growth in another patient. Conclusions Cardiac lipoma could be sensitively detected and accurately diagnosed with multiple noninvasive imaging tools. Comprehensive evaluation with multimodality imaging methods should also be conducted for better management planning and follow-up in all patients.
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Affiliation(s)
- Shenglei Shu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1277 Jiefang Ave, Wuhan, 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Hongliang Yuan
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiangchuang Kong
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1277 Jiefang Ave, Wuhan, 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Jiazheng Wang
- Clinical and Technical Solutions, Philips Healthcare, Beijing, China
| | - Jing Wang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1277 Jiefang Ave, Wuhan, 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Chuansheng Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1277 Jiefang Ave, Wuhan, 430022, China. .,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
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Angus L, Starmans MPA, Rajicic A, Odink AE, Jalving M, Niessen WJ, Visser JJ, Sleijfer S, Klein S, van der Veldt AAM. The BRAF P.V600E Mutation Status of Melanoma Lung Metastases Cannot Be Discriminated on Computed Tomography by LIDC Criteria nor Radiomics Using Machine Learning. J Pers Med 2021; 11:257. [PMID: 33915880 PMCID: PMC8066683 DOI: 10.3390/jpm11040257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/18/2021] [Accepted: 03/24/2021] [Indexed: 11/05/2022] Open
Abstract
Patients with BRAF mutated (BRAF-mt) metastatic melanoma benefit significantly from treatment with BRAF inhibitors. Currently, the BRAF status is determined on archival tumor tissue or on fresh tumor tissue from an invasive biopsy. The aim of this study was to evaluate whether radiomics can predict the BRAF status in a non-invasive manner. Patients with melanoma lung metastases, known BRAF status, and a pretreatment computed tomography scan were included. After semi-automatic annotation of the lung lesions (maximum two per patient), 540 radiomics features were extracted. A chest radiologist scored all segmented lung lesions according to the Lung Image Database Consortium (LIDC) criteria. Univariate analysis was performed to assess the predictive value of each feature for BRAF mutation status. A combination of various machine learning methods was used to develop BRAF decision models based on the radiomics features and LIDC criteria. A total of 169 lung lesions from 103 patients (51 BRAF-mt; 52 BRAF wild type) were included. There were no features with a significant discriminative value in the univariate analysis. Models based on radiomics features and LIDC criteria both performed as poorly as guessing. Hence, the BRAF mutation status in melanoma lung metastases cannot be predicted using radiomics features or visually scored LIDC criteria.
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Affiliation(s)
- Lindsay Angus
- Department of Medical Oncology, Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands; (A.R.); (S.S.); (A.A.M.v.d.V.)
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (M.P.A.S.); (A.E.O.); (W.J.N.); (J.J.V.); (S.K.)
| | - Martijn P. A. Starmans
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (M.P.A.S.); (A.E.O.); (W.J.N.); (J.J.V.); (S.K.)
- Department of Medical Informatics, Erasmus MC, 3015 GD Rotterdam, The Netherlands
| | - Ana Rajicic
- Department of Medical Oncology, Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands; (A.R.); (S.S.); (A.A.M.v.d.V.)
| | - Arlette E. Odink
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (M.P.A.S.); (A.E.O.); (W.J.N.); (J.J.V.); (S.K.)
| | - Mathilde Jalving
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands;
| | - Wiro J. Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (M.P.A.S.); (A.E.O.); (W.J.N.); (J.J.V.); (S.K.)
- Department of Medical Informatics, Erasmus MC, 3015 GD Rotterdam, The Netherlands
- Faculty of Applied Sciences, Delft University of Technology, 2628 CJ Delft, The Netherlands
| | - Jacob J. Visser
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (M.P.A.S.); (A.E.O.); (W.J.N.); (J.J.V.); (S.K.)
| | - Stefan Sleijfer
- Department of Medical Oncology, Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands; (A.R.); (S.S.); (A.A.M.v.d.V.)
| | - Stefan Klein
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (M.P.A.S.); (A.E.O.); (W.J.N.); (J.J.V.); (S.K.)
- Department of Medical Informatics, Erasmus MC, 3015 GD Rotterdam, The Netherlands
| | - Astrid A. M. van der Veldt
- Department of Medical Oncology, Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands; (A.R.); (S.S.); (A.A.M.v.d.V.)
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (M.P.A.S.); (A.E.O.); (W.J.N.); (J.J.V.); (S.K.)
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Torresan F, Crimì F, Ceccato F, Zavan F, Barbot M, Lacognata C, Motta R, Armellin C, Scaroni C, Quaia E, Campi C, Iacobone M. Radiomics: a new tool to differentiate adrenocortical adenoma from carcinoma. BJS Open 2021; 5:6157086. [PMID: 33677483 PMCID: PMC7937424 DOI: 10.1093/bjsopen/zraa061] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 12/01/2020] [Indexed: 01/06/2023] Open
Abstract
Background The main challenge in the management of indeterminate incidentally discovered adrenal tumours is to differentiate benign from malignant lesions. In the absence of clear signs of invasion or metastases, imaging techniques do not always precisely define the nature of the mass. The present pilot study aimed to determine whether radiomics may predict malignancy in adrenocortical tumours. Methods CT images in unenhanced, arterial, and venous phases from 19 patients who had undergone resection of adrenocortical tumours and a cohort who had undergone surveillance for at least 5 years for incidentalomas were reviewed. A volume of interest was drawn for each lesion using dedicated software, and, for each phase, first-order (histogram) and second-order (grey-level colour matrix and run-length matrix) radiological features were extracted. Data were revised by an unsupervised machine learning approach using the K-means clustering technique. Results Of operated patients, nine had non-functional adenoma and 10 carcinoma. There were 11 patients in the surveillance group. Two first-order features in unenhanced CT and one in arterial CT, and 14 second-order parameters in unenhanced and venous CT and 10 second-order features in arterial CT, were able to differentiate adrenocortical carcinoma from adenoma (P < 0.050). After excluding two malignant outliers, the unsupervised machine learning approach correctly predicted malignancy in seven of eight adrenocortical carcinomas in all phases. Conclusion Radiomics with CT texture analysis was able to discriminate malignant from benign adrenocortical tumours, even by an unsupervised machine learning approach, in nearly all patients.
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Affiliation(s)
- F Torresan
- Endocrine Surgery Unit, Department of Surgery, Oncology and Gastroenterology DISCOG, University Hospital of Padova, Padua, Italy
| | - F Crimì
- Radiology Unit, Department of Medicine DIMED, University Hospital of Padua, Padua, Italy
| | - F Ceccato
- Endocrinology Unit, Department of Medicine DIMED, University Hospital of Padua, Padua, Italy
| | - F Zavan
- Radiology Unit, Department of Medicine DIMED, University Hospital of Padua, Padua, Italy
| | - M Barbot
- Endocrinology Unit, Department of Medicine DIMED, University Hospital of Padua, Padua, Italy
| | - C Lacognata
- Radiology Department, University Hospital of Padua, Padua, Italy
| | - R Motta
- Radiology Unit, Department of Medicine DIMED, University Hospital of Padua, Padua, Italy
| | - C Armellin
- Endocrine Surgery Unit, Department of Surgery, Oncology and Gastroenterology DISCOG, University Hospital of Padova, Padua, Italy
| | - C Scaroni
- Endocrinology Unit, Department of Medicine DIMED, University Hospital of Padua, Padua, Italy
| | - E Quaia
- Radiology Unit, Department of Medicine DIMED, University Hospital of Padua, Padua, Italy
| | - C Campi
- Department of Mathematics 'Tullio Levi-Civita', University of Padua, Padua, Italy
| | - M Iacobone
- Endocrine Surgery Unit, Department of Surgery, Oncology and Gastroenterology DISCOG, University Hospital of Padova, Padua, Italy
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Lee YJ, Cha WJ, Kim Y, Oh DY. The recurrence of well-differentiated liposarcoma from benign giant intramuscular lipoma: A case (CARE-compliant) report. Medicine (Baltimore) 2021; 100:e24711. [PMID: 33578611 PMCID: PMC10545130 DOI: 10.1097/md.0000000000024711] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 01/18/2021] [Accepted: 01/21/2021] [Indexed: 01/15/2023] Open
Abstract
RATIONALE Recurrent liposarcoma, previously confirmed as lipoma, has rarely been reported. However, the risk factors for recurrence and the correlation between benign lipoma and malignant liposarcoma remain unclear. In this case study, we suggest a precise diagnostic strategy to minimize recurrence and malignant transformation. PATIENT CONCERNS A 60-year-old male patient with a history of left chest wall swelling without any symptoms underwent excisional surgery, and the mass was confirmed as a benign lipoma in 2015. In 2019, the patient returned to the hospital with symptoms of a palpable mass on the left chest wall. DIAGNOSIS The mass was considered a recurrent lipomatous tumor with the possibility of malignant transformation. Magnetic resonance imaging (MRI) revealed a deep-seated, septate, intramuscular, irregular margin, and large lipomatous tumor invading the ribs, pleura, and adjacent muscle, suggestive of malignancy. The MRI findings were similar to those 4 years ago, except for margin irregularity and invasion to adjacent tissue. INTERVENTIONS Wide en bloc excisions encompassing the 5th to 7th ribs, pleura, and adjacent muscle were followed by reconstruction with a pedicled latissimus dorsi muscle flap. OUTCOMES The recurrent large lipomatous tumor was confirmed as well-differentiated liposarcomas through histological and MDM2-FISH immunohistochemical staining. Postoperatively, follow-up visits continued for 1.5 years without recurrence. LESSONS We suggest that deep-seated, septate, and giant lipomatous tumors should be considered as risk factors for recurrence with the possibility of malignancy and misdiagnosis. It is important to inform patients of all these possibilities and plan close and long-term follow-up.
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Affiliation(s)
- Yeon Ji Lee
- Department of Plastic and Reconstructive Surgery, St. Vincent hospital, College of Medicine, The Catholic University of Korea
| | - Won Jin Cha
- Department of Plastic and Reconstructive Surgery, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea
| | - Yesol Kim
- Department of Plastic and Reconstructive Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Deuk Young Oh
- Department of Plastic and Reconstructive Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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Pressney I, Khoo M, Endozo R, Ganeshan B, O'Donnell P. Pilot study to differentiate lipoma from atypical lipomatous tumour/well-differentiated liposarcoma using MR radiomics-based texture analysis. Skeletal Radiol 2020; 49:1719-1729. [PMID: 32424704 DOI: 10.1007/s00256-020-03454-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 04/19/2020] [Accepted: 04/22/2020] [Indexed: 02/07/2023]
Abstract
AIMS This pilot study aims to determine if tumour heterogeneity assessed using magnetic resonance imaging (MRI) radiomics-based texture analysis (TA) can differentiate between lipoma and atypical lipomatous tumour (ALT)/well-differentiated liposarcoma (WDL). MATERIALS AND METHODS Thirty consecutive ALT/WDLs and 30 lipomas were included in the study, cases diagnosed both histologically and with murine double minute 2 (MDM2) gene amplification by fluorescence in situ hybridisation (FISH) in excision specimens. Multiple patient, MRI and MRTA factors were assessed. Heterogeneity was evaluated using a filtration-histogram technique-based textural analysis on single axial proton density (PD) and coronal T1-W images of the most homogenously fatty component of the lesion. RESULTS Thirty-three percent of the diagnoses of ALT/WDL vs lipoma were confirmed using FISH MDM2 analysis. ALT/WDLs were statistically different from lipomas in location (site in the body and depth from skin surface) and fat content, with p values of 0.021, 0.001, and 0.021 respectively. Nine of 36 (25%) texture parameters had significant differences between ALT/WDLs and lipomas on axial PD MRTA, with the most significant results at medium and coarse texture scales particularly mean intensity (p = 0.003) at SSF = 6, and kurtosis (p = 0.012) at SSF = 5. A cut-off value of < 304 for coarse-filtered texture on axial PD MRI identified ALT from lipoma with a sensitivity and specificity of 70% (AUC = 0.73, p = 0.003). CONCLUSIONS Texture heterogeneity quantified at fine, medium, and coarse texture scales are significant differentiators of lipoma and ALT/WDL with the difference particularly marked in medium and coarse texture scales for two MR TA parameters: mean and kurtosis.
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Affiliation(s)
- Ian Pressney
- Clinical Radiology, Royal National Orthopaedic Hospital, Stanmore, UK. .,Department of Radiology, Royal National Orthopaedic Hospital, Brockley Hill, Stanmore, Middlesex, HA7 4LP, UK.
| | - Michael Khoo
- Clinical Radiology, Royal National Orthopaedic Hospital, Stanmore, UK
| | - Raymond Endozo
- Nuclear Medicine, University College Hospital, London, UK
| | | | - Paul O'Donnell
- Clinical Radiology, Royal National Orthopaedic Hospital, Stanmore, UK.,Institute of Orthopaedics and Musculoskeletal Science, University College, London, London, UK
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Crombé A, Fadli D, Italiano A, Saut O, Buy X, Kind M. Systematic review of sarcomas radiomics studies: Bridging the gap between concepts and clinical applications? Eur J Radiol 2020; 132:109283. [PMID: 32980727 DOI: 10.1016/j.ejrad.2020.109283] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 08/29/2020] [Accepted: 09/08/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVES Sarcomas are a model for intra- and inter-tumoral heterogeneities making them particularly suitable for radiomics analyses. Our purposes were to review the aims, methods and results of radiomics studies involving sarcomas METHODS: Pubmed and Web of Sciences databases were searched for radiomics or textural studies involving bone, soft-tissues and visceral sarcomas until June 2020. Two radiologists evaluated their objectives, results and quality of their methods, imaging pre-processing and machine-learning workflow helped by the items of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2), Image Biomarker Standardization Initiative (IBSI) and 'Radiomics Quality Score' (RQS). Statistical analyses included inter-reader agreements, correlations between methodological assessments, scientometrics indices, and their changes over years, and between RQS, number of patients and models performance. RESULTS Fifty-two studies were included involving: soft-tissue sarcomas (29/52, 55.8 %), bone sarcomas (15/52, 28.8 %), gynecological sarcomas (6/52, 11.5 %) and mixed sarcomas (2/52, 3.8 %), mostly imaged with MRI (36/52, 69.2 %), for a total of distinct patients. Median RQS was 4.5 (28.4 % of the maximum, range: -7 - 17). Performances of predictive models and number of patients negatively correlated (p = 0.027). None of the studies detailed all the items from the IBSI guidelines. There was a significant increase in studies' impact factors since the establishing of the RQS in 2017 (p = 0.038). CONCLUSION Although showing promising results, further efforts are needed to make sarcoma radiomics studies reproducible with an acceptable level of evidence. A better knowledge of the RQS and IBSI reporting guidelines could improve the quality of sarcoma radiomics studies and accelerate clinical applications.
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Affiliation(s)
- Amandine Crombé
- Department of Radiology, Institut Bergonie, F-33000, Bordeaux, France; Modelisation in Oncology (MOnc) Team, INRIA Bordeaux-Sud-Ouest, CNRS UMR 5251, Université De Bordeaux, F-33405, Talence, France; University of Bordeaux, F-33000, Bordeaux, France.
| | - David Fadli
- Department of Radiology, Institut Bergonie, F-33000, Bordeaux, France
| | - Antoine Italiano
- University of Bordeaux, F-33000, Bordeaux, France; Department of Medical Oncology, Institut Bergonie, F-33000, Bordeaux, France
| | - Olivier Saut
- Modelisation in Oncology (MOnc) Team, INRIA Bordeaux-Sud-Ouest, CNRS UMR 5251, Université De Bordeaux, F-33405, Talence, France
| | - Xavier Buy
- Department of Radiology, Institut Bergonie, F-33000, Bordeaux, France
| | - Michèle Kind
- Department of Radiology, Institut Bergonie, F-33000, Bordeaux, France
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46
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Timbergen MJM, Starmans MPA, Padmos GA, Grünhagen DJ, van Leenders GJLH, Hanff DF, Verhoef C, Niessen WJ, Sleijfer S, Klein S, Visser JJ. Differential diagnosis and mutation stratification of desmoid-type fibromatosis on MRI using radiomics. Eur J Radiol 2020; 131:109266. [PMID: 32971431 DOI: 10.1016/j.ejrad.2020.109266] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 07/18/2020] [Accepted: 08/31/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE Diagnosing desmoid-type fibromatosis (DTF) requires an invasive tissue biopsy with β-catenin staining and CTNNB1 mutational analysis, and is challenging due to its rarity. The aim of this study was to evaluate radiomics for distinguishing DTF from soft tissue sarcomas (STS), and in DTF, for predicting the CTNNB1 mutation types. METHODS Patients with histologically confirmed extremity STS (non-DTF) or DTF and at least a pretreatment T1-weighted (T1w) MRI scan were retrospectively included. Tumors were semi-automatically annotated on the T1w scans, from which 411 features were extracted. Prediction models were created using a combination of various machine learning approaches. Evaluation was performed through a 100x random-split cross-validation. The model for DTF vs. non-DTF was compared to classification by two radiologists on a location matched subset. RESULTS The data included 203 patients (72 DTF, 131 STS). The T1w radiomics model showed a mean AUC of 0.79 on the full dataset. Addition of T2w or T1w post-contrast scans did not improve the performance. On the location matched cohort, the T1w model had a mean AUC of 0.88 while the radiologists had an AUC of 0.80 and 0.88, respectively. For the prediction of the CTNNB1 mutation types (S45 F, T41A and wild-type), the T1w model showed an AUC of 0.61, 0.56, and 0.74. CONCLUSIONS Our radiomics model was able to distinguish DTF from STS with high accuracy similar to two radiologists, but was not able to predict the CTNNB1 mutation status.
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Affiliation(s)
- Milea J M Timbergen
- Department of Surgical Oncology, Erasmus MC Cancer Institute Rotterdam, the Netherlands; Department of Medical Oncology, Erasmus MC Cancer Institute Rotterdam, the Netherlands.
| | - Martijn P A Starmans
- Department of Medical Informatics, Erasmus MC, Rotterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands.
| | - Guillaume A Padmos
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands.
| | - Dirk J Grünhagen
- Department of Surgical Oncology, Erasmus MC Cancer Institute Rotterdam, the Netherlands.
| | | | - D F Hanff
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands.
| | - Cornelis Verhoef
- Department of Surgical Oncology, Erasmus MC Cancer Institute Rotterdam, the Netherlands.
| | - Wiro J Niessen
- Department of Medical Informatics, Erasmus MC, Rotterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands; Faculty of Applied Sciences, Delft University of Technology, Delft, the Netherlands.
| | - Stefan Sleijfer
- Department of Medical Oncology, Erasmus MC Cancer Institute Rotterdam, the Netherlands.
| | - Stefan Klein
- Department of Medical Informatics, Erasmus MC, Rotterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands.
| | - Jacob J Visser
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands.
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