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Wagner S, Ewald C, Freitag D, Herrmann KH, Koch A, Bauer J, Vogl TJ, Kemmling A, Gufler H. Radiomics and visual analysis for predicting success of transplantation of heterotopic glioblastoma in mice with MRI. J Neurooncol 2024:10.1007/s11060-024-04725-z. [PMID: 38960965 DOI: 10.1007/s11060-024-04725-z] [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: 03/31/2024] [Accepted: 05/25/2024] [Indexed: 07/05/2024]
Abstract
BACKGROUND Quantifying tumor growth and treatment response noninvasively poses a challenge to all experimental tumor models. The aim of our study was, to assess the value of quantitative and visual examination and radiomic feature analysis of high-resolution MR images of heterotopic glioblastoma xenografts in mice to determine tumor cell proliferation (TCP). METHODS Human glioblastoma cells were injected subcutaneously into both flanks of immunodeficient mice and followed up on a 3 T MR scanner. Volumes and signal intensities were calculated. Visual assessment of the internal tumor structure was based on a scoring system. Radiomic feature analysis was performed using MaZda software. The results were correlated with histopathology and immunochemistry. RESULTS 21 tumors in 14 animals were analyzed. The volumes of xenografts with high TCP (H-TCP) increased, whereas those with low TCP (L-TCP) or no TCP (N-TCP) continued to decrease over time (p < 0.05). A low intensity rim (rim sign) on unenhanced T1-weighted images provided the highest diagnostic accuracy at visual analysis for assessing H-TCP (p < 0.05). Applying radiomic feature analysis, wavelet transform parameters were best for distinguishing between H-TCP and L-TCP / N-TCP (p < 0.05). CONCLUSION Visual and radiomic feature analysis of the internal structure of heterotopically implanted glioblastomas provide reproducible and quantifiable results to predict the success of transplantation.
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Affiliation(s)
- Sabine Wagner
- Department of Neuroradiology, Marburg University Hospital - Philipps University, 35043, Marburg, Germany.
- Department of Neuroradiology, Institute for Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University, 07747, Jena, Germany.
| | - Christian Ewald
- Department of Neurosurgery, Brandenburg Medical School, Theodor Fontane, University Hospital Brandenburg/Havel, 14770, Brandenburg/Havel, Germany
| | - Diana Freitag
- Department of Neurosurgery, Section of Experimental Neurooncology, Jena University Hospital - Friedrich Schiller University, 07747, Jena, Germany
| | - Karl-Heinz Herrmann
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University, 07743, Jena, Germany
| | - Arend Koch
- Department of Neuropathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, and Berlin Institute of Health, Charité University Medicine, 10117, Berlin, Germany
| | - Johannes Bauer
- Department of Neurosurgery, Brandenburg Medical School, Theodor Fontane, University Hospital Brandenburg/Havel, 14770, Brandenburg/Havel, Germany
| | - Thomas J Vogl
- Department of Diagnostic and Interventional Radiology, Goethe University Hospital Frankfurt, 60590, Frankfurt Am Main, Germany
| | - André Kemmling
- Department of Neuroradiology, Marburg University Hospital - Philipps University, 35043, Marburg, Germany
| | - Hubert Gufler
- Department of Diagnostic and Interventional Radiology, Goethe University Hospital Frankfurt, 60590, Frankfurt Am Main, Germany
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Gambacorta MA, Chiloiro G, Masciocchi C, Mariani S, Romano A, Gonnelli A, Gerard JP, Ngan S, Rödel C, Bujko K, Glynne-Jones R, van Soest J, Dekker A, Damiani A, Valentini V. pCR and 2-Year Disease-Free Survival: A Combination of the Two Endpoints as a New Classification for Locally Advanced Rectal Cancer Patients-An Updated Pooled Analysis of Eleven International Randomized Trials. Cancers (Basel) 2023; 15:3209. [PMID: 37370819 DOI: 10.3390/cancers15123209] [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: 04/29/2023] [Revised: 06/13/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023] Open
Abstract
LARC is managed by multimodal treatments whose intensity can be highly modulated. In this context, we need surrogate endpoints to help predict long-term outcomes and better personalize treatments. A previous study identified 2yDFS as a stronger predictor of OS than pCR in LARC patients undergoing neoadjuvant RT. The aim of this pooled analysis was to assess the role of pCR and 2yDFS as surrogate endpoints for OS in a larger cohort. The pooled and subgroup analyses were performed on large rectal cancer randomized trial cohorts who received long-course RT. Our analysis focused on the evaluation of OS in relation to the pCR and 2-year disease status. A total of 4600 patients were analyzed. Four groups were identified according to intermediate outcomes: 12% had both pCR and 2yDFS (the better); 67% achieved 2yDFS but not pCR (the good); 1% had pCR but not 2yDFS; and 20% had neither pCR nor 2yDFS (the bad). The pCR and 2yDFS were favorably associated with OS in the univariate analysis, and 2yDFS maintained a statistically significant association in the multivariate analysis independently of the pCR status. The combination of the pCR and 2yDFS results in a strong predictor of OS, whereas failure to achieve 2yDFS carries a poor prognosis regardless of the pCR status. This new stratification of LARC patients could help design predictive models where the combination of 2yDFS and pCR should be employed as the primary outcome.
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Affiliation(s)
| | - Giuditta Chiloiro
- Radiation Oncology Department, Fondazione Policlinico Universitario A. Gemelli-IRCCS, 00168 Rome, Italy
| | - Carlotta Masciocchi
- Radiation Oncology Department, Fondazione Policlinico Universitario A. Gemelli-IRCCS, 00168 Rome, Italy
| | - Silvia Mariani
- Radiation Oncology Department, Fondazione Policlinico Universitario A. Gemelli-IRCCS, 00168 Rome, Italy
| | - Angela Romano
- Radiation Oncology Department, Fondazione Policlinico Universitario A. Gemelli-IRCCS, 00168 Rome, Italy
| | - Alessandra Gonnelli
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Pisana, 56124 Pisa, Italy
| | | | - Samuel Ngan
- Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia
| | - Claus Rödel
- Department of Radiotherapy of Oncology, University of Frankfurt, 60590 Frankfurt, Germany
- German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK), Partner Site, 60528 Frankfurt, Germany
- Frankfurt Cancer Institute (FCI), 60596 Frankfurt, Germany
| | - Krzysztof Bujko
- Department of Radiotherapy I, Maria Skłodowska-Curie National Research Institute of Oncology, 02-781 Warsaw, Poland
| | - Robert Glynne-Jones
- Department of Radiotherapy, Mount Vernon Centre for Cancer Treatment, Northwood, London HA6 2RN, UK
| | - Johan van Soest
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
| | - Andrea Damiani
- Radiation Oncology Department, Fondazione Policlinico Universitario A. Gemelli-IRCCS, 00168 Rome, Italy
| | - Vincenzo Valentini
- Radiation Oncology Department, Fondazione Policlinico Universitario A. Gemelli-IRCCS, 00168 Rome, Italy
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Inchingolo R, Maino C, Cannella R, Vernuccio F, Cortese F, Dezio M, Pisani AR, Giandola T, Gatti M, Giannini V, Ippolito D, Faletti R. Radiomics in colorectal cancer patients. World J Gastroenterol 2023; 29:2888-2904. [PMID: 37274803 PMCID: PMC10237092 DOI: 10.3748/wjg.v29.i19.2888] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/07/2023] [Accepted: 04/25/2023] [Indexed: 05/16/2023] Open
Abstract
The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.
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Affiliation(s)
- Riccardo Inchingolo
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Federica Vernuccio
- Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
| | - Francesco Cortese
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Michele Dezio
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Antonio Rosario Pisani
- Interdisciplinary Department of Medicine, Section of Nuclear Medicine, University of Bari “Aldo Moro”, Bari 70121, Italy
| | - Teresa Giandola
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
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Kong Y, Xu M, Wei X, Qian D, Yin Y, Huang Z, Gu W, Zhou L. CT imaging-based radiomics signatures improve prognosis prediction in postoperative colorectal cancer. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:1281-1294. [PMID: 37638470 DOI: 10.3233/xst-230090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
OBJECTIVE To investigate the use of non-contrast-enhanced (NCE) and contrast-enhanced (CE) CT radiomics signatures (Rad-scores) as prognostic factors to help improve the prediction of the overall survival (OS) of postoperative colorectal cancer (CRC) patients. METHODS A retrospective analysis was performed on 65 CRC patients who underwent surgical resection in our hospital as the training set, and 19 patient images retrieved from The Cancer Imaging Archive (TCIA) as the external validation set. In training, radiomics features were extracted from the preoperative NCE/CE-CT, then selected through 5-fold cross validation LASSO Cox method and used to construct Rad-scores. Models derived from Rad-scores and clinical factors were constructed and compared. Kaplan-Meier analyses were also used to compare the survival probability between the high- and low-risk Rad-score groups. Finally, a nomogram was developed to predict the OS. RESULTS In training, a clinical model achieved a C-index of 0.796 (95% CI: 0.722-0.870), while clinical and two Rad-scores combined model performed the best, achieving a C-index of 0.821 (95% CI: 0.743-0.899). Furthermore, the models with the CE-CT Rad-score yielded slightly better performance than that of NCE-CT in training. For the combined model with CE-CT Rad-scores, a C-index of 0.818 (95% CI: 0.742-0.894) and 0.774 (95% CI: 0.556-0.992) were achieved in both the training and validation sets. Kaplan-Meier analysis demonstrated a significant difference in survival probability between the high- and low-risk groups. Finally, the areas under the receiver operating characteristics (ROC) curves for the model were 0.904, 0.777, and 0.843 for 1, 3, and 5-year survival, respectively. CONCLUSION NCE-CT or CE-CT radiomics and clinical combined models can predict the OS for CRC patients, and both Rad-scores are recommended to be included when available.
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Affiliation(s)
- Yan Kong
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Muchen Xu
- Department of Radiation Oncology, Dushu Lake Hospital Affiliated to Soochow University, Medical Center of Soochow University, Suzhou, Jiangsu, China
| | - Xianding Wei
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Danqi Qian
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Yuan Yin
- Wuxi Cancer Institute, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Zhaohui Huang
- Wuxi Cancer Institute, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Wenchao Gu
- Department of Diagnostic and Interventional Radiology, University of Tsukuba, Ibaraki, Japan
| | - Leyuan Zhou
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
- Department of Radiation Oncology, Dushu Lake Hospital Affiliated to Soochow University, Medical Center of Soochow University, Suzhou, Jiangsu, China
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Muacevic A, Adler JR, Issa M, Ali O, Noureldin K, Gaber A, Mahgoub A, Ahmed M, Yousif M, Zeinaldine A. Textural Analysis as a Predictive Biomarker in Rectal Cancer. Cureus 2022; 14:e32241. [PMID: 36620843 PMCID: PMC9813797 DOI: 10.7759/cureus.32241] [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: 12/06/2022] [Indexed: 12/12/2022] Open
Abstract
Colorectal cancer (CRC) is a common deadly cancer. Early detection and accurate staging of CRC enhance good prognosis and better treatment outcomes. Rectal cancer staging is the cornerstone for selecting the best treatment approach. The standard gold method for rectal cancer staging is pelvic MRI. After staging, combining surgery and chemoradiation is the standard management aiming for a curative outcome. Textural analysis (TA) is a radiomic process that quantifies lesions' heterogenicity by measuring pixel distribution in digital imaging. MRI textural analysis (MRTA) of rectal cancer images is growing in current literature as a future predictor of outcomes of rectal cancer management, such as pathological response to neoadjuvant chemoradiotherapy (NCRT), survival, and tumour recurrence. MRTA techniques could validate alternative approaches in rectal cancer treatment, such as the wait-and-watch (W&W) approach in pathologically complete responders (pCR) following NCRT. We consider this a significant step towards implementing precision management in rectal cancer. In this narrative review, we summarize the current knowledge regarding the potential role of TA in rectal cancer management in predicting the prognosis and clinical outcomes, as well as aim to delineate the challenges which obstruct the implementing of this new modality in clinical practice.
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Liu Z, Wang Y, Shen F, Zhang Z, Gong J, Fu C, Shen C, Li R, Jing G, Cai S, Zhang Z, Sun Y, Tong T. Radiomics based on readout-segmented echo-planar imaging (RS-EPI) diffusion-weighted imaging (DWI) for prognostic risk stratification of patients with rectal cancer: a two-centre, machine learning study using the framework of predictive, preventive, and personalized medicine. EPMA J 2022; 13:633-647. [PMID: 36505889 PMCID: PMC9727035 DOI: 10.1007/s13167-022-00303-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/01/2022] [Indexed: 11/14/2022]
Abstract
Background Currently, the rate of recurrence or metastasis (ROM) remains high in rectal cancer (RC) patients treated with the standard regimen. The potential of diffusion-weighted imaging (DWI) in predicting ROM risk has been reported, but the efficacy is insufficient. Aims This study investigated the potential of a new sequence called readout-segmented echo-planar imaging (RS-EPI) DWI in predicting the ROM risk of patients with RC using machine learning methods to achieve the principle of predictive, preventive, and personalized medicine (PPPM) application in RC treatment. Methods A total of 195 RC patients from two centres who directly received total mesorectal excision were retrospectively enrolled in our study. Machine learning methods, including recursive feature elimination (RFE), the synthetic minority oversampling technique (SMOTE), and the support vector machine (SVM) classifier, were used to construct models based on clinical-pathological factors (clinical model), radiomic features from RS-EPI DWI (radiomics model), and their combination (merged model). The Harrell concordance index (C-index) and the area under the time-dependent receiver operating characteristic curve (AUC) were calculated to evaluate the predictive performance at 1 year, 3 years, and 5 years. Kaplan‒Meier analysis was performed to evaluate the ability to stratify patients according to the risk of ROM. Findings The merged model performed well in predicting tumour ROM in patients with RC at 1 year, 3 years, and 5 years in both cohorts (AUC = 0.887/0.813/0.794; 0.819/0.795/0.783) and was significantly superior to the clinical model (AUC = 0.87 [95% CI: 0.80-0.93] vs. 0.71 [95% CI: 0.59-0.81], p = 0.009; C-index = 0.83 [95% CI: 0.76-0.90] vs. 0.68 [95% CI: 0.56-0.79], p = 0.002). It also had a significant ability to differentiate patients with a high and low risk of ROM (HR = 12.189 [95% CI: 4.976-29.853], p < 0.001; HR = 6.427 [95% CI: 2.265-13.036], p = 0.002). Conclusion Our developed merged model based on RS-EPI DWI accurately predicted and effectively stratified patients with RC according to the ROM risk at an early stage with an individualized profile, which may be able to assist physicians in individualizing the treatment protocols and promote a meaningful paradigm shift in RC treatment from traditional reactive medicine to PPPM. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-022-00303-3.
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Affiliation(s)
- Zonglin Liu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yueming Wang
- Department of Anatomy and Physiology, Shanghai JiaoTong University School of Medicine, Shanghai, China
| | - Fu Shen
- Department of Radiology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Zhiyuan Zhang
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Caixia Fu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, China
| | - Changqing Shen
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Rong Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Guodong Jing
- Department of Radiology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Sanjun Cai
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Zhen Zhang
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yiqun Sun
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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Bellini D, Carbone I, Rengo M, Vicini S, Panvini N, Caruso D, Iannicelli E, Tombolini V, Laghi A. Performance of Machine Learning and Texture Analysis for Predicting Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer with 3T MRI. Tomography 2022; 8:2059-2072. [PMID: 36006071 PMCID: PMC9416446 DOI: 10.3390/tomography8040173] [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] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/17/2022] [Accepted: 08/17/2022] [Indexed: 11/23/2022] Open
Abstract
Background: To evaluate the diagnostic performance of a Machine Learning (ML) algorithm based on Texture Analysis (TA) parameters in the prediction of Pathological Complete Response (pCR) to Neoadjuvant Chemoradiotherapy (nChRT) in Locally Advanced Rectal Cancer (LARC) patients. Methods: LARC patients were prospectively enrolled to undergo pre- and post-nChRT 3T MRI for initial loco-regional staging. TA was performed on axial T2-Weighted Images (T2-WI) to extract specific parameters, including skewness, kurtosis, entropy, and mean of positive pixels. For the assessment of TA parameter diagnostic performance, all patients underwent complete surgical resection, which served as a reference standard. ROC curve analysis was carried out to determine the discriminatory accuracy of each quantitative TA parameter to predict pCR. A ML-based decisional tree was implemented combining all TA parameters in order to improve diagnostic accuracy. Results: Forty patients were considered for final study population. Entropy, kurtosis and MPP showed statistically significant differences before and after nChRT in patients with pCR; in particular, when patients with Pathological Partial Response (pPR) and/or Pathological Non-Response (pNR) were considered, entropy and skewness showed significant differences before and after nChRT (all p < 0.05). In terms of absolute value changes, pre- and post-nChRT entropy, and kurtosis showed significant differences (0.31 ± 0.35, in pCR, −0.02 ± 1.28 in pPR/pNR, (p = 0.04); 1.87 ± 2.19, in pCR, −0.06 ± 3.78 in pPR/pNR (p = 0.0005); 107.91 ± 274.40, in pCR, −28.33 ± 202.91 in pPR/pNR, (p = 0.004), respectively). According to ROC curve analysis, pre-treatment kurtosis with an optimal cut-off value of ≤3.29 was defined as the best discriminative parameter, resulting in a sensitivity and specificity in predicting pCR of 81.5% and 61.5%, respectively. Conclusions: TA parameters extracted from T2-WI MRI images could play a key role as imaging biomarkers in the prediction of response to nChRT in LARC patients. ML algorithms can be used to efficiently combine all TA parameters in order to improve diagnostic accuracy.
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Affiliation(s)
- Davide Bellini
- Department of Radiological Sciences, Oncology and Pathology, “Sapienza” University of Rome—I.C.O.T. Hospital, Via Franco Faggiana, 1668, 04100 Latina, Italy
| | - Iacopo Carbone
- Department of Radiological Sciences, Oncology and Pathology, “Sapienza” University of Rome—I.C.O.T. Hospital, Via Franco Faggiana, 1668, 04100 Latina, Italy
- Correspondence: ; Tel.: +39-351836065
| | - Marco Rengo
- Department of Radiological Sciences, Oncology and Pathology, “Sapienza” University of Rome—I.C.O.T. Hospital, Via Franco Faggiana, 1668, 04100 Latina, Italy
| | - Simone Vicini
- Department of Radiological Sciences, Oncology and Pathology, “Sapienza” University of Rome—I.C.O.T. Hospital, Via Franco Faggiana, 1668, 04100 Latina, Italy
| | - Nicola Panvini
- Department of Radiological Sciences, Oncology and Pathology, “Sapienza” University of Rome—I.C.O.T. Hospital, Via Franco Faggiana, 1668, 04100 Latina, Italy
| | - Damiano Caruso
- Department of Surgical and Medical Sciences and Translational Medicine, “Sapienza” University of Rome—Diagnostic Imaging Unit, Sant′Andrea University Hospital, Via di Grottarossa 1035, 00189 Rome, Italy
| | - Elsa Iannicelli
- Department of Surgical and Medical Sciences and Translational Medicine, “Sapienza” University of Rome—Diagnostic Imaging Unit, Sant′Andrea University Hospital, Via di Grottarossa 1035, 00189 Rome, Italy
| | - Vincenzo Tombolini
- Department of Radiotherapy, Policlinico Umberto I, “Sapienza” University of Rome, 00161 Rome, Italy
| | - Andrea Laghi
- Department of Surgical and Medical Sciences and Translational Medicine, “Sapienza” University of Rome—Diagnostic Imaging Unit, Sant′Andrea University Hospital, Via di Grottarossa 1035, 00189 Rome, Italy
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Capelli G, Campi C, Bao QR, Morra F, Lacognata C, Zucchetta P, Cecchin D, Pucciarelli S, Spolverato G, Crimì F. 18F-FDG-PET/MRI texture analysis in rectal cancer after neoadjuvant chemoradiotherapy. Nucl Med Commun 2022; 43:815-822. [PMID: 35471653 PMCID: PMC9177153 DOI: 10.1097/mnm.0000000000001570] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 04/05/2022] [Indexed: 12/27/2022]
Abstract
OBJECTIVE Reliable markers to predict the response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) are lacking. We aimed to assess the ability of 18F-FDG PET/MRI to predict response to nCRT among patients undergoing curative-intent surgery. METHODS Patients with histological-confirmed LARC who underwent curative-intent surgery following nCRT and restaging with 18F-FDG PET/MRI were included. Statistical correlation between radiomic features extracted in PET, apparent diffusion coefficient (ADC) and T2w images and patients' histopathologic response to chemoradiotherapy using a multivariable logistic regression model ROC-analysis. RESULTS Overall, 50 patients were included in the study. A pathological complete response was achieved in 28.0% of patients. Considering second-order textural features, nine parameters showed a statistically significant difference between the two groups in ADC images, six parameters in PET images and four parameters in T2w images. Combining all the features selected for the three techniques in the same multivariate ROC curve analysis, we obtained an area under ROC curve of 0.863 (95% CI, 0.760-0.966), showing a sensitivity, specificity and accuracy at the Youden's index of 100% (14/14), 64% (23/36) and 74% (37/50), respectively. CONCLUSION PET/MRI texture analysis seems to represent a valuable tool in the identification of rectal cancer patients with a complete pathological response to nCRT.
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Affiliation(s)
- Giulia Capelli
- General Surgery 3, Department of Surgical, Oncological and Gastroenterological Sciences, University of Padova, Padova
| | | | - Quoc Riccardo Bao
- General Surgery 3, Department of Surgical, Oncological and Gastroenterological Sciences, University of Padova, Padova
| | - Francesco Morra
- Institute of Radiology, Department of Medicine, University of Padova
| | | | - Pietro Zucchetta
- Nuclear Medicine Unit, Department of Medicine, University of Padova, Padova, Italy
| | - Diego Cecchin
- Nuclear Medicine Unit, Department of Medicine, University of Padova, Padova, Italy
| | - Salvatore Pucciarelli
- General Surgery 3, Department of Surgical, Oncological and Gastroenterological Sciences, University of Padova, Padova
| | - Gaya Spolverato
- General Surgery 3, Department of Surgical, Oncological and Gastroenterological Sciences, University of Padova, Padova
| | - Filippo Crimì
- Institute of Radiology, Department of Medicine, University of Padova
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Azamat S, Karaman Ş, Azamat IF, Ertaş G, Kulle CB, Keskin M, Sakin RND, Bakır B, Oral EN, Kartal MG. Complete Response Evaluation of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiotherapy Using Textural Features Obtained from T2 Weighted Imaging and ADC Maps. Curr Med Imaging 2022; 18:1061-1069. [PMID: 35240976 DOI: 10.2174/1573405618666220303111026] [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: 08/20/2021] [Revised: 12/07/2021] [Accepted: 12/22/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND The prediction of pathological responses for locally advanced rectal cancer using magnetic resonance imaging (MRI) after neoadjuvant chemoradiotherapy (CRT) is a challenging task for radiologists, as residual tumor cells can be mistaken for fibrosis. Texture analysis of MR images has been proposed to understand the underlying pathology. OBJECTIVE This study aimed to assess the responses of lesions to CRT in patients with locally advanced rectal cancer using the first-order textural features of MRI T2-weighted imaging (T2-WI) and apparent diffusion coefficient (ADC) maps. METHODS Forty-four patients with locally advanced rectal cancer (median age: 57 years) who underwent MRI before and after CRT were enrolled in this retrospective study. The first-order textural parameters of tumors on T2-WI and ADC maps were extracted. The textural features of lesions in pathologic complete responders were compared to partial responders using Student's t- or Mann-Whitney U tests. A comparison of textural features before and after CRT for each group was performed using the Wilcoxon rank sum test. Receiver operating characteristic curves were calculated to detect the diagnostic performance of the ADC. RESULTS Of the 44 patients evaluated, 22 (50%) were placed in a partial response group and 50% were placed in a complete response group. The ADC changes of the complete responders were statistically more significant than those of the partial responders (P = 0.002). Pathologic total response was predicted with an ADC cut-off of 1310 x 10-6 mm2/s, with a sensitivity of 72%, a specificity of 77%, and an accuracy of 78.1% after neoadjuvant CRT. The skewness of the T2-WI before and after neoadjuvant CRT showed a significant difference in the complete response group compared to the partial response group (P = 0.001 for complete responders vs. P = 0.482 for partial responders). Also, relative T2-WI signal intensity in the complete response group was statistically lower than that of the partial response group after neoadjuvant CRT (P = 0.006). CONCLUSION As a result of the conversion of tumor cells to fibrosis, the skewness of the T2-WI before and after neoadjuvant CRT was statistically different in the complete response group compared to the partial response group, and the complete response group showed statistically lower relative T2-WI signal intensity than the partial response group after neoadjuvant CRT. Additionally, the ADC cut-off value of 1310 × 10-6 mm2/s could be used as a marker for complete response along with absolute ADC value changes within this dataset.
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Affiliation(s)
- Sena Azamat
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
- Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Şule Karaman
- Department of Radiation Oncology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Ibrahim Fethi Azamat
- Department of General Surgery, Faculty of Medicine, Koc University, Istanbul, Turke
| | - Gokhan Ertaş
- Biomedical Engineering Department, Yeditepe University, Istanbul, Turkey
| | - Cemil Burak Kulle
- Department of General Surgery, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Metin Keskin
- Department of General Surgery, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
- Department of General Surgery, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | | | - Barış Bakır
- Department of Radiology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Ethem Nezih Oral
- Department of Radiation Oncology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Merve Gulbiz Kartal
- Department of Radiology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
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Analysis of KRAS Mutation Status Prediction Model for Colorectal Cancer Based on Medical Imaging. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2021:3953442. [PMID: 34976107 PMCID: PMC8716224 DOI: 10.1155/2021/3953442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/02/2021] [Accepted: 11/09/2021] [Indexed: 12/09/2022]
Abstract
This study retrospectively included some patients with colorectal cancer diagnosed by histopathology, to explore the feasibility of CT medical image texture analysis in predicting KRAS gene mutations in patients with colorectal cancer. Before any surgical procedure, all patients received an enhanced CT scan of the abdomen and pelvis, as well as genetic testing. To define patient groups, divide all patients into test and validation sets based on the order of patient enrollment. A radiologist took a look at the plain axial CT image of the tumor, as well as the portal vein CT image, at the corresponding level. The physician points the computer's cursor to the relevant area in the image, and TexRAD software programs together texture parameters based on various spatial scale factors, also known as total mean, total variance, statistical entropy, overall total average, mean total, positive mean, skewness value, kurtosis value, and general skewness. Using the same method again two weeks later, the observer and another physician measured the image of each patient again to see if the method was consistent between observers. With regard to clinical information, the KRAS gene mutation group and the wild group of participants in the test set and validation set each had values for the texture parameter. In a study of patients with colorectal cancer, the results demonstrated that CT texture parameters were correlated with the presence of the KRAS gene mutation. The best CT prediction model includes the values of the medium texture image's slope and the other CT fine texture image's value of entropy, the medium texture image's slope and kurtosis, and the medium texture image's mean and the other CT fine texture image's value of entropy. Regardless of the training set or the validation set, patients with and without KRAS gene mutations did not differ significantly in clinical characteristics. This method can be used to identify mutations in the KRAS gene in patients with colorectal cancer, making it practical to implement CT medical image texture analysis technology for that purpose.
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Devoto L, Ganeshan B, Keller D, Groves A, Endozo R, Arulampalam T, Chand M. Using texture analysis in the development of a potential radiomic signature for early identification of hepatic metastasis in colorectal cancer. Eur J Radiol Open 2022; 9:100415. [PMID: 35340828 PMCID: PMC8942820 DOI: 10.1016/j.ejro.2022.100415] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 03/06/2022] [Accepted: 03/15/2022] [Indexed: 12/24/2022] Open
Abstract
Background Aim Methods Results Conclusion
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Affiliation(s)
- Laurence Devoto
- Wellcome / EPSRC Centre, for Interventional and Surgical Sciences, University College London, 1st Floor, Charles Bell House, 43-45 Foley Street, London W1W 7TS, United Kingdom
- Correspondence to: Wellcome / EPSRC Centre for Interventional and Surgical Sciences, Charles Bell House, 43-45 Foley Street, London W1W 7TS, United Kingdom.
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, 5th floor, Tower, University College London Hospital, 235 Euston Road, London NW1 2BU, United Kingdom
| | - Deborah Keller
- Wellcome / EPSRC Centre, for Interventional and Surgical Sciences, University College London, 1st Floor, Charles Bell House, 43-45 Foley Street, London W1W 7TS, United Kingdom
| | - Ashley Groves
- Institute of Nuclear Medicine, 5th floor, Tower, University College London Hospital, 235 Euston Road, London NW1 2BU, United Kingdom
| | - Raymond Endozo
- Institute of Nuclear Medicine, 5th floor, Tower, University College London Hospital, 235 Euston Road, London NW1 2BU, United Kingdom
| | - Tan Arulampalam
- ICENI Centre, Colchester Hospital, Turner Rd, Mile End, Colchester CO4 5JL, United Kingdom
| | - Manish Chand
- Wellcome / EPSRC Centre, for Interventional and Surgical Sciences, University College London, 1st Floor, Charles Bell House, 43-45 Foley Street, London W1W 7TS, United Kingdom
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Cui Y, Wang G, Ren J, Hou L, Li D, Wen Q, Xi Y, Yang X. Radiomics Features at Multiparametric MRI Predict Disease-Free Survival in Patients With Locally Advanced Rectal Cancer. Acad Radiol 2021; 29:e128-e138. [PMID: 34961658 DOI: 10.1016/j.acra.2021.11.024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/24/2021] [Accepted: 11/26/2021] [Indexed: 01/04/2023]
Abstract
OBJECTIVE To investigate the potential value of radiomics features based on preoperative multiparameter MRI in predicting disease-free survival (DFS) in patients with local advanced rectal cancer (LARC). METHODS We identified 234 patients with LARC who underwent preoperative MRI, including T2-weighted, diffusion kurtosis imaging, and contrast enhanced T1-weighted. All patients were randomly divided into the training (n = 164) and validation (n = 70) cohorts. 414 features were extracted from the tumor from above sequences and the radiomics signature was then generated, mainly based on feature stability and Cox proportional hazards model. Two models, integrating pre- and postoperative variables, were constructed to validate the radiomics signatures for DFS estimation. RESULTS The radiomics signature, composed of six DFS-related features, was significantly associated with DFS in the training and validation cohorts (both p < 0.001). The radiomics signature and MR-defined extramural venous invasion (mrEMVI) were identified as the independent predictor of DFS both in the pre- and postoperative models. In both cohorts, the two radiomics-based models exhibited better prediction performance (C-index ≥0.77, all p < 0.05) than the corresponding clinical models, with positive net reclassification improvement and lower Akaike information criterion (AIC). Decision curve analysis also confirmed their clinical usefulness. The radiomics-based models could categorize LARC patients into high- and low-risk groups with distinct profiles of DFS (all p < 0.05). CONCLUSION The proposed radiomics models with pre- and postoperative features have the potential to predict DFS, and may provide valuable guidance for the future individualized management in patients with LARC.
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Bonde A, Smith DA, Kikano E, Yoest JM, Tirumani SH, Ramaiya NH. Overview of serum and tissue markers in colorectal cancer: a primer for radiologists. Abdom Radiol (NY) 2021; 46:5521-5535. [PMID: 34415413 DOI: 10.1007/s00261-021-03243-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 08/05/2021] [Accepted: 08/07/2021] [Indexed: 12/17/2022]
Abstract
Serum and tissue tumor markers provide crucial information in the diagnosis, treatment, and follow-up of colorectal cancers. Tissue tumor markers are increasingly used for determination of targeted chemotherapy planning based on genotyping of tumor cells. Recently, plasma-based technique of liquid biopsy is being evaluated for providing tumor biomarkers in the management of colorectal cancer. Tumor markers are commonly used in conjunction with imaging during initial staging, treatment determination, response assessment, and determination of recurrence or metastatic disease. Knowledge of tumor markers and their association with radiological findings is thus crucial for radiologists. Additionally, various novel imaging techniques are being evaluated as potential noninvasive imaging biomarkers to predict tumor genotypes, features, and tumor response. We review and discuss the potential role of these newer imaging techniques.
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Affiliation(s)
- Apurva Bonde
- Department of Radiology, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX, 78229, USA.
| | - Daniel A Smith
- Department of Radiology, University Hospitals Cleveland Medical Center, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Elias Kikano
- Department of Radiology, University Hospitals Cleveland Medical Center, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Jennifer M Yoest
- Department of Pathology, University Hospitals Cleveland Medical Center, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Sree H Tirumani
- Department of Radiology, University Hospitals Cleveland Medical Center, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Nikhil H Ramaiya
- Department of Radiology, University Hospitals Cleveland Medical Center, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH, 44106, USA
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Tibermacine H, Rouanet P, Sbarra M, Forghani R, Reinhold C, Nougaret S. Radiomics modelling in rectal cancer to predict disease-free survival: evaluation of different approaches. Br J Surg 2021; 108:1243-1250. [PMID: 34423347 DOI: 10.1093/bjs/znab191] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 04/11/2021] [Indexed: 12/29/2022]
Abstract
BACKGROUND Radiomics may be useful in rectal cancer management. The aim of this study was to assess and compare different radiomics approaches over qualitative evaluation to predict disease-free survival (DFS) in patients with locally advanced rectal cancer treated with neoadjuvant therapy. METHODS Patients from a phase II, multicentre, randomized study (GRECCAR4; NCT01333709) were included retrospectively as a training set. An independent cohort of patients comprised the independent test set. For both time points and both sets, radiomic features were extracted from two-dimensional manual segmentation (MS), three-dimensional (3D) MS, and from bounding boxes. Radiomics predictive models of DFS were built using a hyperparameters-tuned random forests classifier. Additionally, radiomics models were compared with qualitative parameters, including sphincter invasion, extramural vascular invasion as determined by MRI (mrEMVI) at baseline, and tumour regression grade evaluated by MRI (mrTRG) after chemoradiotherapy (CRT). RESULTS In the training cohort of 98 patients, all three models showed good performance with mean(s.d.) area under the curve (AUC) values ranging from 0.77(0.09) to 0.89(0.09) for prediction of DFS. The 3D radiomics model outperformed qualitative analysis based on mrEMVI and sphincter invasion at baseline (P = 0.038 and P = 0.027 respectively), and mrTRG after CRT (P = 0.017). In the independent test cohort of 48 patients, at baseline and after CRT the AUC ranged from 0.67(0.09) to 0.76(0.06). All three models showed no difference compared with qualitative analysis in the independent set. CONCLUSION Radiomics models can predict DFS in patients with locally advanced rectal cancer.
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Affiliation(s)
- H Tibermacine
- Radiology Department, Institut du Cancer de Montpellier, University of Montpellier, Montpellier, France.,Institut de Recherche en Cancérologie de Montpellier, INSERM, U1194, Montpellier, France
| | - P Rouanet
- Surgical Oncology Department, Institut du Cancer de Montpellier, University of Montpellier, Montpellier, France
| | - M Sbarra
- Departmental Faculty of Medicine and Surgery, Unit of Diagnostic Imaging and Interventional Radiology, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - R Forghani
- Augmented Intelligence and Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
| | - C Reinhold
- Augmented Intelligence and Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
| | - S Nougaret
- Radiology Department, Institut du Cancer de Montpellier, University of Montpellier, Montpellier, France.,Institut de Recherche en Cancérologie de Montpellier, INSERM, U1194, Montpellier, France
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15
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Inoue A, Sheedy SP, Heiken JP, Mohammadinejad P, Graham RP, Lee HE, Kelley SR, Hansel SL, Bruining DH, Fidler JL, Fletcher JG. MRI-detected extramural venous invasion of rectal cancer: Multimodality performance and implications at baseline imaging and after neoadjuvant therapy. Insights Imaging 2021; 12:110. [PMID: 34370093 PMCID: PMC8353019 DOI: 10.1186/s13244-021-01023-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 05/27/2021] [Indexed: 12/25/2022] Open
Abstract
MRI is routinely used for rectal cancer staging to evaluate tumor extent and to inform decision-making regarding surgical planning and the need for neoadjuvant and adjuvant therapy. Extramural venous invasion (EMVI), which is intravenous tumor extension beyond the rectal wall on histopathology, is a predictor for worse prognosis. T2-weighted images (T2WI) demonstrate EMVI as a nodular-, bead-, or worm-shaped structure of intermediate T2 signal with irregular margins that arises from the primary tumor. Correlative diffusion-weighted images demonstrate intermediate to high signal corresponding to EMVI, and contrast enhanced T1-weighted images demonstrate tumor signal intensity in or around vessels. Diffusion-weighted and post contrast images may increase diagnostic performance but decrease inter-observer agreement. CT may also demonstrate obvious EMVI and is potentially useful in patients with a contraindication for MRI. This article aims to review the spectrum of imaging findings of EMVI of rectal cancer on MRI and CT, to summarize the diagnostic accuracy and inter-observer agreement of imaging modalities for its presence, to review other rectal neoplasms that may cause EMVI, and to discuss the clinical significance and role of MRI-detected EMVI in staging and restaging clinical scenarios.
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Affiliation(s)
- Akitoshi Inoue
- Department of Radiology, Mayo Clinic, Rochester, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Shannon P Sheedy
- Department of Radiology, Mayo Clinic, Rochester, 200 First Street SW, Rochester, MN, 55905, USA
| | - Jay P Heiken
- Department of Radiology, Mayo Clinic, Rochester, 200 First Street SW, Rochester, MN, 55905, USA
| | - Payam Mohammadinejad
- Department of Radiology, Mayo Clinic, Rochester, 200 First Street SW, Rochester, MN, 55905, USA
| | - Rondell P Graham
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, 200 First Street SW, Rochester, MN, 55905, USA
| | - Hee Eun Lee
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, 200 First Street SW, Rochester, MN, 55905, USA
| | - Scott R Kelley
- Division of Colon and Rectal Surgery, Mayo Clinic, Rochester, 200 First Street SW, Rochester, MN, 55905, USA
| | - Stephanie L Hansel
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, 200 First Street SW, Rochester, MN, 55905, USA
| | - David H Bruining
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, 200 First Street SW, Rochester, MN, 55905, USA
| | - Jeff L Fidler
- Department of Radiology, Mayo Clinic, Rochester, 200 First Street SW, Rochester, MN, 55905, USA
| | - Joel G Fletcher
- Department of Radiology, Mayo Clinic, Rochester, 200 First Street SW, Rochester, MN, 55905, USA
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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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The prognostic value of MRI-detected extramural vascular invasion (mrEMVI) for rectal cancer patients treated with neoadjuvant therapy: a meta-analysis. Eur Radiol 2021; 31:8827-8837. [PMID: 33993333 DOI: 10.1007/s00330-021-07981-z] [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: 01/27/2021] [Revised: 03/10/2021] [Accepted: 04/01/2021] [Indexed: 12/12/2022]
Abstract
OBJECTIVES The purpose of this meta-analysis was to evaluate the prognostic value of MRI-detected extramural vascular invasion (mrEMVI) and mrEMVI after neoadjuvant therapy (ymrEMVI) in rectal cancer patients receiving neoadjuvant therapy. METHODS A systematic search of the PubMed, Web of Science, Embase, and Cochrane Library databases was carried out up to June 2020. Studies that evaluated mrEMVI, used treatment with neoadjuvant therapy, and reported survival were included. The time-to-event outcomes (OS and DFS rates) are expressed as hazard ratios (HRs) with 95% confidence intervals (CIs). If the HR was not reported in the study, it was calculated from the survival curve using methods according to Parmar's recommendation. The Newcastle-Ottawa scale was used to assess the methodological quality of the studies included in the meta-analysis. RESULTS A total of 2237 patients from 11 studies were included, and the pooled analysis of the overall results from eight studies showed that patients who were mrEMVI positive at baseline had significantly worse disease-free survival (DFS) (random-effects model: HR = 2.50 [1.84, 3.14]; Z = 5.83, p < 0.00001). The pooled analysis of the overall results from six studies showed that patients who were ymrEMVI positive following neoadjuvant therapy had significantly worse DFS (random-effects model: HR = 2.24 [1.73, 2.90], Z = 6.12, p < 0.00001). Patients with mrEMVI positivity at baseline were also associated with worse overall survival (OS) (random-effects model: HR = 1.93 [1.36, 2.73]; Z = 3.71, p < 0.00001). CONCLUSION mrEMVI and ymrEMVI positivity are poor prognostic factors for rectal cancer patients treated with neoadjuvant therapy. The precise evaluation of EMVI may contribute to designing individualised treatments and improving patient outcomes. KEY POINTS • Extramural vascular invasion (EMVI) is a prognostic factor for rectal cancer. • MRI can be used to evaluate EMVI status before (mrEMVI) and after neoadjuvant therapy (ymrEMVI). • The evaluation of mrEMVI and ymrEMVI in neoadjuvant therapy would provide an early assessment of patient prognosis.
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Zhao L, Liang M, Shi Z, Xie L, Zhang H, Zhao X. Preoperative volumetric synthetic magnetic resonance imaging of the primary tumor for a more accurate prediction of lymph node metastasis in rectal cancer. Quant Imaging Med Surg 2021; 11:1805-1816. [PMID: 33936966 DOI: 10.21037/qims-20-659] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background An accurate assessment of lymph node (LN) status in patients with rectal cancer is important for treatment planning and an essential factor for predicting local recurrence and overall survival. In this study, we explored the potential value of histogram parameters of synthetic magnetic resonance imaging (SyMRI) in predicting LN metastasis in rectal cancer and compared their predictive performance with traditional morphological characteristics and chemical shift effect (CSE). Methods A total of 70 patients with pathologically proven rectal adenocarcinoma who received direct surgical resection were enrolled in this prospective study. Preoperative rectal MRI, including SyMRI, were performed, and morphological characteristics and CSE of LN were assessed. Histogram parameters were extracted on a T1 map, T2 map, and proton density (PD) map, including mean, variance, maximum, minimum, 10th percentile, median, 90th percentile, energy, kurtosis, entropy, and skewness. Receiver operating characteristic (ROC) curves were used to explore their predictive performance for assessing LN status. Results Significant differences in the energy of the T1, T2, and PD maps were observed between LN-negative and LN-positive groups [all P<0.001; the area under the ROC curve (AUC) was 0.838, 0.858, and 0.823, respectively]. The maximum and kurtosis of the T2 map, maximum, and variance of PD map could also predict LN metastasis with moderate diagnostic power (P=0.032, 0.045, 0.016, and 0.047, respectively). Energy of the T1 map [odds ratio (OR) =1.683, 95% confidence interval (CI): 1.207-2.346, P=0.002] and extramural venous invasion on MRI (mrEMVI) (OR =10.853, 95% CI: 2.339-50.364, P=0.002) were significant predictors of LN metastasis. Moreover, the T1 map energy significantly improved the predictive performance compared to morphological features and CSE (P=0.0002 and 0.0485). Conclusions The histogram parameters derived from SyMRI of the primary tumor were associated with LN metastasis in rectal cancer and could significantly improve the predictive performance compared with morphological features and CSE.
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Affiliation(s)
- Li Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Meng Liang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhuo Shi
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lizhi Xie
- GE Healthcare, Magnetic Resonance Research China, Beijing, China
| | - Hongmei Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Theodosi A, Ouzounis S, Kostopoulos S, Glotsos D, Kalatzis I, Asvestas P, Tzelepi V, Ravazoula P, Cavouras D, Sakellaropoulos G. Employing machine learning and microscopy images of AIB1-stained biopsy material to assess the 5-year survival of patients with colorectal cancer. Microsc Res Tech 2021; 84:2421-2433. [PMID: 33929071 DOI: 10.1002/jemt.23797] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/04/2021] [Accepted: 04/10/2021] [Indexed: 01/07/2023]
Abstract
Our purpose was to employ microscopy images of amplified in breast cancer 1 (AIB1)-stained biopsy material of patients with colorectal cancer (CRC) to: (a) find statistically significant differences (SSDs) in the texture and color of the epithelial gland tissue, between 5-year survivors and non-survivors after the first diagnosis and (b) employ machine learning (ML) methods for predicting the CRC-patient 5-year survival. We collected biopsy material from 54 patients with diagnosed CRC from the archives of the University Hospital of Patras, Greece. Twenty-six of the patients had survived 5 years after the first diagnosis. We selected regions of interest containing the epithelial gland at different microscope lens magnifications. We computed 69 textural and color features. Furthermore, we identified features with SSDs between the two groups of patients and we designed a supervised ML system for predicting the CRC-patient 5-year survival. Additionally, we employed the VGG16 pretrained convolution neural network to extract deep learning (DL) features, the support vector machines classifier, and the bootstrap cross-validation method for boosting the accuracy of predicting 5-year survival. Fourteen features sustained SSDs between the two groups of patients. The supervised ML system achieved 87% accuracy in predicting 5-year survival. In comparison, the DL system, using images from all magnifications, gave 97% classification accuracy. Glandular texture in 5-year non-survivors appeared to be of lower contrast, coarseness, roughness, local pixel correlation, and lower AIB1 variation, all indicating loss of textural definition. The supervised ML system revealed useful information regarding features that discriminate between 5-year survivors and non-survivors while the DL system displayed superior accuracy by employing DL features.
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Affiliation(s)
- Angeliki Theodosi
- Department of Medical Physics, School of Health Sciences, Faculty of Medicine, University of Patras, Patras, Greece
| | - Sotiris Ouzounis
- Department of Biomedical Engineering, Medical Image and Signal Processing Laboratory, University of West Attica, Athens, Greece
| | - Spiros Kostopoulos
- Department of Biomedical Engineering, Medical Image and Signal Processing Laboratory, University of West Attica, Athens, Greece
| | - Dimitris Glotsos
- Department of Biomedical Engineering, Medical Image and Signal Processing Laboratory, University of West Attica, Athens, Greece
| | - Ioannis Kalatzis
- Department of Biomedical Engineering, Medical Image and Signal Processing Laboratory, University of West Attica, Athens, Greece
| | - Pantelis Asvestas
- Department of Biomedical Engineering, Medical Image and Signal Processing Laboratory, University of West Attica, Athens, Greece
| | - Vassiliki Tzelepi
- Department of Pathology, University Hospital of Patras, Patras, Greece
| | | | - Dionisis Cavouras
- Department of Biomedical Engineering, Medical Image and Signal Processing Laboratory, University of West Attica, Athens, Greece
| | - George Sakellaropoulos
- Department of Medical Physics, School of Health Sciences, Faculty of Medicine, University of Patras, Patras, Greece
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Prognostic importance of circumferential resection margin in the era of evolving surgical and multidisciplinary treatment of rectal cancer: A systematic review and meta-analysis. Surgery 2021; 170:412-431. [PMID: 33838883 DOI: 10.1016/j.surg.2021.02.029] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 01/20/2021] [Accepted: 02/13/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND Circumferential resection margin is considered an important prognostic parameter after rectal cancer surgery, but its impact might have changed because of improved surgical quality and tailored multimodality treatment. The aim of this systematic review was to determine the prognostic importance of circumferential resection margin involvement based on the most recent literature. METHODS A systematic literature search of MEDLINE, Embase, and the Cochrane Library was performed for studies published between January 2006 and May 2019. Studies were included if 3- or 5-year oncological outcomes were reported depending on circumferential resection margin status. Outcome parameters were local recurrence, overall survival, disease-free survival, and distant metastasis rate. The Newcastle Ottawa Scale and Jadad score were used for quality assessment of the studies. Meta-analysis was performed using a random effects model and reported as a pooled odds ratio or hazard ratio with 95% confidence interval. RESULTS Seventy-five studies were included, comprising a total of 85,048 rectal cancer patients. Significant associations between circumferential resection margin involvement and all long-term outcome parameters were uniformly found, with varying odds ratios and hazard ratios depending on circumferential resection margin definition (<1 mm, ≤1 mm, otherwise), neoadjuvant treatment, study period, and geographical origin of the studies. CONCLUSION Circumferential resection margin involvement has remained an independent, poor prognostic factor for local recurrence and survival in most recent literature, indicating that circumferential resection margin status can still be used as a short-term surrogate endpoint.
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Jo SJ, Kim SH, Park SJ, Lee Y, Son JH. Association between Texture Analysis Parameters and Molecular Biologic KRAS Mutation in Non-Mucinous Rectal Cancer. TAEHAN YONGSANG UIHAKHOE CHI 2021; 82:406-416. [PMID: 36238732 PMCID: PMC9431938 DOI: 10.3348/jksr.2020.0065] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 06/03/2020] [Accepted: 06/23/2020] [Indexed: 11/15/2022]
Abstract
Purpose To evaluate the association between magnetic resonance imaging (MRI)-based texture parameters and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation in patients with non-mucinous rectal cancer. Materials and Methods Seventy-nine patients who had pathologically confirmed rectal non-mucinous adenocarcinoma with or without KRAS-mutation and had undergone rectal MRI were divided into a training (n = 46) and validation dataset (n = 33). A texture analysis was performed on the axial T2-weighted images. The association was statistically analyzed using the Mann-Whitney U test. To extract an optimal cut-off value for the prediction of KRAS mutation, a receiver operating characteristic curve analysis was performed. The cut-off value was verified using the validation dataset. Results In the training dataset, skewness in the mutant group (n = 22) was significantly higher than in the wild-type group (n = 24) (0.221 ± 0.283; -0.006 ± 0.178, respectively, p = 0.003). The area under the curve of the skewness was 0.757 (95% confidence interval, 0.606 to 0.872) with a maximum accuracy of 71%, a sensitivity of 64%, and a specificity of 78%. None of the other texture parameters were associated with KRAS mutation (p > 0.05). When a cut-off value of 0.078 was applied to the validation dataset, this had an accuracy of 76%, a sensitivity of 86%, and a specificity of 68%. Conclusion Skewness was associated with KRAS mutation in patients with non-mucinous rectal cancer.
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Park EJ, Kim SH, Park SJ, Baek TW. Texture Analysis of Gray-Scale Ultrasound Images for Staging of Hepatic Fibrosis. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2021; 82:116-127. [PMID: 36237456 PMCID: PMC9432409 DOI: 10.3348/jksr.2019.0185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 03/06/2020] [Accepted: 04/09/2020] [Indexed: 11/15/2022]
Abstract
Purpose Materials and Methods Results Conclusion
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Affiliation(s)
- Eun Joo Park
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, Korea
| | - Seung Ho Kim
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, Korea
| | - Sang Joon Park
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Tae Wook Baek
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, Korea
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Texture Analysis of Hepatocellular Carcinoma on Magnetic Resonance Imaging: Assessment for Performance in Predicting Histopathologic Grade. J Comput Assist Tomogr 2020; 44:901-910. [PMID: 32976263 DOI: 10.1097/rct.0000000000001087] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
OBJECTIVE The aim of the study was to evaluate the performance of texture analysis for discriminating the histopathological grade of hepatocellular carcinoma (HCC) on magnetic resonance imaging. METHODS Preoperative magnetic resonance imaging data from 101 patients with HCC, including T2-weighted imaging, arterial phase, and apparent diffusion coefficient mapping, were analyzed using texture analysis software (TexRAD). Differences among the histological groups were analyzed using the Mann-Whitney U test. The performance of texture features was evaluated using receiver operating characteristic analysis. RESULTS Entropy was the most significantly relevant texture feature for distinguishing each histological grade group of HCC (P < 0.05). In ROC analysis, entropy with spatial scale filter 3 (area under curve the receiver operating characteristic curve [AUC], 0.778), mean with coarse filter (spatial scale filter 5; AUC, 0.670), and skewness without filtration (AUC, 0.760) had the highest AUC value on T2-weighted imaging, arterial phase, and apparent diffusion coefficient maps, respectively. CONCLUSIONS Magnetic resonance imaging texture analysis demonstrated potential for predicting the histopathological grade of HCCs.
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Chiloiro G, Rodriguez-Carnero P, Lenkowicz J, Casà C, Masciocchi C, Boldrini L, Cusumano D, Dinapoli N, Meldolesi E, Carano D, Damiani A, Barbaro B, Manfredi R, Valentini V, Gambacorta MA. Delta Radiomics Can Predict Distant Metastasis in Locally Advanced Rectal Cancer: The Challenge to Personalize the Cure. Front Oncol 2020; 10:595012. [PMID: 33344243 PMCID: PMC7744725 DOI: 10.3389/fonc.2020.595012] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 11/02/2020] [Indexed: 02/05/2023] Open
Abstract
PURPOSE Distant metastases are currently the main cause of treatment failure in locally advanced rectal cancer (LARC) patients. The aim of this research is to investigate a correlation between the variation of radiomics features using pre- and post-neoadjuvant chemoradiation (nCRT) magnetic resonance imaging (MRI) with 2 years distant metastasis (2yDM) rate in LARC patients. METHODS AND MATERIALS Diagnostic pre- and post- nCRT MRI of LARC patients, treated in a single institution from May 2008 to June 2015 with an adequate follow-up time, were retrospectively collected. Gross tumor volumes (GTV) were contoured by an abdominal radiologist and blindly reviewed by a radiation oncologist expert in rectal cancer. The dataset was firstly randomly split into 90% training data, for features selection, and 10% testing data, for the validation. The final set of features after the selection was used to train 15 different classifiers using accuracy as target metric. The models' performance was then assessed on the testing data and the best performing classifier was then selected, maximising the confusion matrix balanced accuracy (BA). RESULTS Data regarding 213 LARC patients (36% female, 64% male) were collected. Overall 2yDM was 17%. A total of 2,606 features extracted from the pre- and post- nCRT GTV were tested and 4 features were selected after features selection process. Among the 15 tested classifiers, logistic regression proved to be the best performing one with a testing set BA, sensitivity and specificity of 78.5%, 71.4% and 85.7%, respectively. CONCLUSIONS This study supports a possible role of delta radiomics in predicting following occurrence of distant metastasis. Further studies including a consistent external validation are needed to confirm these results and allows to translate radiomics model in clinical practice. Future integration with clinical and molecular data will be mandatory to fully personalized treatment and follow-up approaches.
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Affiliation(s)
- Giuditta Chiloiro
- Dipartimento Diagnostica per Immagini, Radioterapia oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | | | - Jacopo Lenkowicz
- Dipartimento Diagnostica per Immagini, Radioterapia oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Calogero Casà
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Carlotta Masciocchi
- Dipartimento Diagnostica per Immagini, Radioterapia oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Luca Boldrini
- Dipartimento Diagnostica per Immagini, Radioterapia oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Davide Cusumano
- Dipartimento Diagnostica per Immagini, Radioterapia oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Nicola Dinapoli
- Dipartimento Diagnostica per Immagini, Radioterapia oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Elisa Meldolesi
- Dipartimento Diagnostica per Immagini, Radioterapia oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Davide Carano
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Andrea Damiani
- Dipartimento Diagnostica per Immagini, Radioterapia oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Brunella Barbaro
- Dipartimento Diagnostica per Immagini, Radioterapia oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Riccardo Manfredi
- Dipartimento Diagnostica per Immagini, Radioterapia oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Vincenzo Valentini
- Dipartimento Diagnostica per Immagini, Radioterapia oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Maria Antonietta Gambacorta
- Dipartimento Diagnostica per Immagini, Radioterapia oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
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Staal FCR, van der Reijd DJ, Taghavi M, Lambregts DMJ, Beets-Tan RGH, Maas M. Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review. Clin Colorectal Cancer 2020; 20:52-71. [PMID: 33349519 DOI: 10.1016/j.clcc.2020.11.001] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 09/03/2020] [Accepted: 11/02/2020] [Indexed: 02/07/2023]
Abstract
Prediction of outcome in patients with colorectal cancer (CRC) is challenging as a result of lack of a robust biomarker and heterogeneity between and within tumors. The aim of this review was to assess the current possibilities and limitations of radiomics (on computed tomography [CT], magnetic resonance imaging [MRI], and positron emission tomography [PET]) for the prediction of treatment outcome and long-term outcome in CRC. Medline/PubMed was searched up to August 2020 for studies that used radiomics for the prediction of response to treatment and survival in patients with CRC (based on pretreatment imaging). The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool and Radiomics Quality Score (RQS) were used for quality assessment. A total of 76 studies met the inclusion criteria and were included for further analysis. Radiomics analyses were performed on MRI in 41 studies, on CT in 30 studies, and on 18F-FDG-PET/CT in 10 studies. Heterogeneous results were reported regarding radiomics methods and included features. High-quality studies (n = 13), consisting mainly of MRI-based radiomics to predict response in rectal cancer, were able to predict response with good performance. Radiomics literature in CRC is highly heterogeneous, but it nonetheless holds promise for the prediction of outcome. The most evidence is available for MRI-based radiomics in rectal cancer. Future radiomics research in CRC should focus on independent validation of existing models rather than on developing new models.
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Affiliation(s)
- Femke C R Staal
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Denise J van der Reijd
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Marjaneh Taghavi
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Doenja M J Lambregts
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands; Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Monique Maas
- Department of Radiology, Antoni van Leeuwenhoek, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
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Cui Y, Yang W, Ren J, Li D, Du X, Zhang J, Yang X. Prognostic value of multiparametric MRI-based radiomics model: Potential role for chemotherapeutic benefits in locally advanced rectal cancer. Radiother Oncol 2020; 154:161-169. [PMID: 32976874 DOI: 10.1016/j.radonc.2020.09.039] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 08/29/2020] [Accepted: 09/17/2020] [Indexed: 12/29/2022]
Abstract
BACKGROUND AND PURPOSE We aimed to develop a radiomics model for the prediction of survival and chemotherapeutic benefits using pretreatment multiparameter MR images and clinicopathological features in patients with locally advanced rectal cancer (LARC). MATERIALS AND METHODS 186 consecutive patients with LARC underwent feature extraction from the whole tumor on T2-weighted, contrast enhanced T1-weighted, and ADC images. Feature selection was based on feature stability and the Boruta algorithm. Radiomics signatures for predicting DFS (disease-free survival) were then generated using the selected features. Combining clinical risk factors, a radiomics nomogram was constructed using Cox proportional hazards regression model. The predictive performance was evaluated by Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis. RESULTS Four features were selected to construct the radiomics signature, significantly associated with DFS (P < 0.001). The radiomics nomogram, incorporating radiomics signature and two clinicopathological variables (pN and tumor differentiation), exhibited better prediction performance for DFS than the clinicopathological model, with C-index of 0.780 (95%CI, 0.718-0.843) and 0.803 (95%CI, 0.717-0.889) in the training and validation cohorts, respectively. The radiomics nomogram-defined high-risk group had a shorter DFS, DMFS, and OS than those in the low-risk group (all P < 0.05). Further analysis showed that patients with higher nomogram-defined score exhibited a favorable response to adjuvant chemotherapy (AC) while the low-risk could not. CONCLUSION This study demonstrated that the newly developed pretreatment multiparameter MRI-based radiomics model could serve as a powerful predictor of prognosis, and may act as a potential indicator for guiding AC in patients with LARC.
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Affiliation(s)
- Yanfen Cui
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Wenhui Yang
- Shanxi Bethune Hospital Cancer Center, Taiyuan, China
| | | | - Dandan Li
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Xiaosong Du
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Junjie Zhang
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Xiaotang Yang
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China.
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Lu HC, Wang F, Yin JD. Texture Analysis Based on Sagittal Fat-Suppression and Transverse T2-Weighted Magnetic Resonance Imaging for Determining Local Invasion of Rectal Cancer. Front Oncol 2020; 10:1476. [PMID: 33014786 PMCID: PMC7461892 DOI: 10.3389/fonc.2020.01476] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 07/10/2020] [Indexed: 12/15/2022] Open
Abstract
Background: Accurate evaluation of local invasion (T-stage) of rectal cancer is essential for treatment planning. A search of PubMed database indicated that the correlation between texture features from T2-weighted magnetic resonance imaging (T2WI) (MRI) and T-stage has not been explored extensively. Purpose: To evaluate the performance of texture analysis using sagittal fat-suppression combined with transverse T2WI for determining T-stage of rectal cancer. Methods: One hundred and seventy-four rectal cancer cases who underwent preoperative MRI were retrospectively selected and divided into high (T3/4) and low (T1/2) T-stage groups. Texture features were, respectively, extracted from sagittal fat-suppression and transverse T2WI images. Univariate and multivariate analyses were conducted to determine T-stage. Discrimination performance was assessed by receiver operating characteristic (ROC) analysis. Results: For univariate analysis, the best performance in differentiating T1/2 from T3/4 tumors was achieved from transverse T2WI, and the area under the ROC curve (AUC) was 0.740. For multivariate analysis, the logical regression model incorporating the independent predictors achieved an AUC of 0.789. Conclusions: Texture features from sagittal fat-suppression combined with transverse T2WI presented moderate association with T-stage of rectal cancer. These findings may be valuable in selecting optimum treatment strategy.
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Affiliation(s)
- H C Lu
- School of Medicine and Bioinformatics Engineering, Northeastern University, Shenyang, China.,Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - F Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - J D Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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Song L, Yin J. Application of Texture Analysis Based on Sagittal Fat-Suppression and Oblique Axial T2-Weighted Magnetic Resonance Imaging to Identify Lymph Node Invasion Status of Rectal Cancer. Front Oncol 2020; 10:1364. [PMID: 32850437 PMCID: PMC7426518 DOI: 10.3389/fonc.2020.01364] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 06/29/2020] [Indexed: 12/18/2022] Open
Abstract
Objective: To investigate the value of texture features derived from T2-weighted magnetic resonance imaging (T2WI) for predicting preoperative lymph node invasion (N stage) in rectal cancer. Materials and Methods: One hundred and eighty-two patients with histopathologically confirmed rectal cancer and preoperative magnetic resonance imaging were retrospectively analyzed, who were divided into high (N1-2) and low N stage (N0). Texture features were calculated from histogram, gray-level co-occurrence matrix, and gray-level run-length matrix from sagittal fat-suppression and oblique axial T2WI. Independent sample t-test or Mann-Whitney U-test were used for statistical analysis. Multivariate logistic regression analysis was conducted to build the predictive models. Predictive performance was evaluated by receiver operating characteristic (ROC) analysis. Results: Energy (ENE), entropy (ENT), information correlation (INC), long-run emphasis (LRE), and short-run low gray-level emphasis (SRLGLE) extracted from sagittal fat-suppression T2WI, and ENE, ENT, INC, low gray-level run emphasis (LGLRE), and SRLGLE from oblique axial T2WI were significantly different between stage N0 and stage N1-2 tumors. The multivariate analysis for features from sagittal fat-suppression T2WI showed that higher SRLGLE and lower ENE were independent predictors of lymph node invasion. The model reached an area under ROC curve (AUC) of 0.759. The analysis for features from oblique axial T2WI showed that higher INC and SRLGLE were independent predictors. The model achieved an AUC of 0.747. The analysis for all extracted features showed that lower ENE from sagittal fat-suppression T2WI and higher INC and SRLGLE from oblique axial T2WI were independent predictors. The model showed an AUC of 0.772. Conclusions: Texture features derived from T2WI could provide valuable information for identifying the status of lymph node invasion in rectal cancer.
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Affiliation(s)
- Lirong Song
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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Thomas JV, Abou Elkassem AM, Ganeshan B, Smith AD. MR Imaging Texture Analysis in the Abdomen and Pelvis. Magn Reson Imaging Clin N Am 2020; 28:447-456. [PMID: 32624161 DOI: 10.1016/j.mric.2020.03.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Add "which is a" before "distribution"? Texture analysis (TA) is a form of radiomics that refers to quantitative measurements of the histogram, distribution and/or relationship of pixel intensities or gray scales within a region of interest on an image. TA can be applied to MR images of the abdomen and pelvis, with the main strength quantitative analysis of pixel intensities and heterogeneity rather than subjective/qualitative analysis. There are multiple limitations of MRTA. Despite these limitations, there is a growing body of literature supporting MRTA. This review discusses application of MRTA to the abdomen and pelvis.
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Affiliation(s)
- John V Thomas
- Body Imaging Section, Department of Radiology, University of Alabama at Birmingham, N355 Jefferson Tower, 619 19th Street South, Birmingham, AL 35249-6830, USA.
| | - Asser M Abou Elkassem
- Department of Radiology, University of Alabama at Birmingham, 619 19th Street South, Birmingham, AL 35249-6830, USA
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, University College of London, 5th Floor, Tower, 235 Euston Road, London NW1 2BU, UK
| | - Andrew D Smith
- Department of Radiology, University of Alabama at Birmingham, 619 19th Street South, Birmingham, AL 35249-6830, USA
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MRI T2-weighted sequences-based texture analysis (TA) as a predictor of response to neoadjuvant chemo-radiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). Radiol Med 2020; 125:1216-1224. [DOI: 10.1007/s11547-020-01215-w] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 04/27/2020] [Indexed: 12/13/2022]
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MRI texture features differentiate clinicopathological characteristics of cervical carcinoma. Eur Radiol 2020; 30:5384-5391. [PMID: 32382845 DOI: 10.1007/s00330-020-06913-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 04/23/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVES To evaluate MRI texture analysis in differentiating clinicopathological characteristics of cervical carcinoma (CC). METHODS Patients with newly diagnosed CC who underwent pre-treatment MRI were retrospectively reviewed. Texture analysis was performed using commercial software (TexRAD). Largest single-slice ROIs were manually drawn around the tumour on T2-weighted (T2W) images, apparent diffusion coefficient (ADC) maps and contrast-enhanced T1-weighted (T1c) images. First-order texture features were calculated and compared among histological subtypes, tumour grades, FIGO stages and nodal status using the Mann-Whitney U test. Feature selection was achieved by elastic net. Selected features from different sequences were used to build the multivariable support vector machine (SVM) models and the performances were assessed by ROC curves and AUC. RESULTS Ninety-five patients with FIGO stage IB~IVB were evaluated. A number of texture features from multiple sequences were significantly different among all the clinicopathological subgroups (p < 0.05). Texture features from different sequences were selected to build the SVM models. The AUCs of SVM models for discriminating histological subtypes, tumour grades, FIGO stages and nodal status were 0.841, 0.850, 0.898 and 0.879, respectively. CONCLUSIONS Texture features derived from multiple sequences were helpful in differentiating the clinicopathological signatures of CC. The SVM models with selected features from different sequences offered excellent diagnostic discrimination of the tumour characteristics in CC. KEY POINTS • First-order texture features are able to differentiate clinicopathological signatures of cervical carcinoma. • Combined texture features from different sequences can offer excellent diagnostic discrimination of the tumour characteristics in cervical carcinoma.
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MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer. Eur Radiol 2020; 30:4201-4211. [PMID: 32270317 DOI: 10.1007/s00330-020-06835-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 03/05/2020] [Accepted: 03/25/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVES This study aimed to evaluate the efficiency of imaging features and texture analysis (TA) based on baseline rectal MRI for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy (nCRT) and tumor recurrence in patients with locally advanced rectal cancer (LARC). METHODS Consecutive patients with LARC who underwent rectal MRI between January 2014 and December 2015 and surgical resection after completing nCRT were retrospectively enrolled. Imaging features were analyzed, and TA parameters were extracted from the tumor volume of interest (VOI) from baseline rectal MRI. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the optimal TA parameter cutoff values to stratify the patients. Logistic and Cox regression analyses were performed to assess the efficacy of each imaging feature and texture parameter in predicting tumor response and disease-free survival. RESULTS In total, 78 consecutive patients were enrolled. In the logistic regression, good treatment response was associated with lower tumor location (OR = 13.284, p = 0.012), low Conv_Min (OR = 0.300, p = 0.013) and high Conv_Std (OR = 3.174, p = 0.016), Shape_Sphericity (OR = 3.170, p = 0.015), and Shape_Compacity (OR = 2.779, p = 0.032). In the Cox regression, a greater risk of tumor recurrence was related to higher cT stage (HR = 5.374, p = 0.044), pelvic side wall lymph node positivity (HR = 2.721, p = 0.013), and gray-level run length matrix_long-run low gray-level emphasis (HR = 2.268, p = 0.046). CONCLUSIONS Imaging features and TA based on baseline rectal MRI could be valuable for predicting the treatment response to nCRT for rectal cancer and tumor recurrence. KEY POINTS • Imaging features and texture parameters of T2-weighted MR images of rectal cancer can help to predict treatment response and the risk for tumor recurrence. • Tumor location as well as conventional and shape indices of texture features can help to predict treatment response for rectal cancer. • Clinical T stage, positive pelvic side wall lymph nodes, and the high-order texture parameter, GLRLM_LRLGE, can help to predict tumor recurrence for rectal cancer.
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Fan S, Li X, Cui X, Zheng L, Ren X, Ma W, Ye Z. Computed Tomography-Based Radiomic Features Could Potentially Predict Microsatellite Instability Status in Stage II Colorectal Cancer: A Preliminary Study. Acad Radiol 2019; 26:1633-1640. [PMID: 30929999 DOI: 10.1016/j.acra.2019.02.009] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 02/11/2019] [Accepted: 02/12/2019] [Indexed: 12/20/2022]
Abstract
RATIONALE AND OBJECTIVES To investigate whether quantitative radiomics features extracted from computed tomography (CT) can predict microsatellite instability (MSI) status in an Asian cohort of patients with stage Ⅱ colorectal cancer (CRC). MATERIALS AND METHODS This retrospective study was approved by our institutional review board, and the informed consent requirement was waived. From March 2016 to March 2018, 119 Chinese patients with pathologically confirmed stage Ⅱ CRC, available MSI status, and preoperative contrast-enhanced CT images were included in this study. Clinical and pathological information was obtained from the institutional database. The radiomics features were extracted from portal venous-phase CT images of segmented volumes of each entire primary tumor by using Matrix Laboratory (MATLAB), and radiomics signatures were generated using the least absolute shrinkage and selection operator logistic regression model. The minority group was balanced via synthetic minority over-sampling technique method. The association between the clinicopathologic characteristics and MSI status was assessed using Student's t test, Chi-square, or Fisher's exact test. The predictive efficacy of MSI status using radiomics features, clinical factors (including age, gender, CT-reported tumor location, differentiation degree of tumor, smoking history, hypertension history, family history of cancer, diabetes history, level of the Ki-67 expression, and laboratory analysis) and the combined models were evaluated, respectively. Predictive performance was evaluated by the area under receiver operating characteristic curve, accuracy, sensitivity, and specificity. RESULTS MSI status was significantly associated with tumor location (p = 0.043); differentiation degree of tumor (p < 0.0001), hypertension history (p = 0.012), and the level of the Ki-67 expression (p = 0.015). Six radiomics features and 11 clinical characteristics were selected for predicting MSI status. The model that used the combination of clinical factors and radiomics features achieved the overall best performance than using either of the two features alone, yielding the area under the curve, sensitivity, and specificity of 0.752, 0.663, 0.841 for the combined model, 0.598, 0.371, 0.825 for clinical factors alone, and 0.688, 0.517, 0.858 for radiomics features alone, respectively. CONCLUSION CT-based radiomic features of stage Ⅱ CRC are associated with MSI status. Combining analysis of clinical features and CT features could improve predictive efficacy and could potentially select the patients for individualized therapy noninvasively.
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Affiliation(s)
- Shuxuan Fan
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhuxi Road, Hexi District, Tianjin 300060, China
| | - Xubin Li
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhuxi Road, Hexi District, Tianjin 300060, China
| | - Xiaonan Cui
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhuxi Road, Hexi District, Tianjin 300060, China
| | - Lei Zheng
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhuxi Road, Hexi District, Tianjin 300060, China
| | - Xiaoyi Ren
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhuxi Road, Hexi District, Tianjin 300060, China
| | - Wenjuan Ma
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhuxi Road, Hexi District, Tianjin 300060, China.
| | - Zhaoxiang Ye
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhuxi Road, Hexi District, Tianjin 300060, China.
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Xu Y, Xu Q, Ma Y, Duan J, Zhang H, Liu T, Li L, Sun H, Shi K, Xie S, Wang W. Characterizing MRI features of rectal cancers with different KRAS status. BMC Cancer 2019; 19:1111. [PMID: 31727020 PMCID: PMC6857233 DOI: 10.1186/s12885-019-6341-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 11/06/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND To investigate whether MRI findings, including texture analysis, can differentiate KRAS mutation status in rectal cancer. METHODS Totally, 158 patients with pathologically proved rectal cancers and preoperative pelvic MRI examinations were enrolled. Patients were stratified into two groups: KRAS wild-type group (KRASwt group) and KRAS mutation group (KRASmt group) according to genomic DNA extraction analysis. MRI findings of rectal cancers (including texture features) and relevant clinical characteristics were statistically evaluated to identify the differences between the two groups. The independent samples t test or Mann-Whitney U test were used for continuous variables. The differences of the remaining categorical polytomous variables were analyzed using the Chi-square test or Fisher exact test. A receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminatory power of MRI features. The area under the ROC curve (AUC) and the optimal cut-off values were calculated using histopathology diagnosis as a reference; meanwhile, sensitivity and specificity were determined. RESULTS Mean values of six texture parameters (Mean, Variance, Skewness, Entropy, gray-level nonuniformity, run-length nonuniformity) were significantly higher in KRASmt group compared to KRASwt group (p < 0.0001, respectively). The AUC values of texture features ranged from 0.703~0.813. In addition, higher T stage and lower ADC values were observed in the KRASmt group compared to KRASwt group (t = 7.086, p = 0.029; t = - 2.708, p = 0.008). CONCLUSION The MRI findings of rectal cancer, especially texture features, showed an encouraging value for identifying KRAS status.
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Affiliation(s)
- Yanyan Xu
- Department of Radiology, China-Japan Friendship Hospital, No.2 Yinghua East Street, Chaoyang District, Beijing, 100029, People's Republic of China
| | - Qiaoyu Xu
- Department of Radiology, China-Japan Friendship Hospital, No.2 Yinghua East Street, Chaoyang District, Beijing, 100029, People's Republic of China
| | - Yanhui Ma
- Department of Radiology, China-Japan Friendship Hospital, No.2 Yinghua East Street, Chaoyang District, Beijing, 100029, People's Republic of China
| | - Jianghui Duan
- Department of Radiology, China-Japan Friendship Hospital, No.2 Yinghua East Street, Chaoyang District, Beijing, 100029, People's Republic of China
| | - Haibo Zhang
- Department of Radiology, China-Japan Friendship Hospital, No.2 Yinghua East Street, Chaoyang District, Beijing, 100029, People's Republic of China
| | - Tongxi Liu
- Department of Radiology, China-Japan Friendship Hospital, No.2 Yinghua East Street, Chaoyang District, Beijing, 100029, People's Republic of China
| | - Lu Li
- Department of Radiology, China-Japan Friendship Hospital, No.2 Yinghua East Street, Chaoyang District, Beijing, 100029, People's Republic of China
| | - Hongliang Sun
- Department of Radiology, China-Japan Friendship Hospital, No.2 Yinghua East Street, Chaoyang District, Beijing, 100029, People's Republic of China.
| | - Kaining Shi
- Philips Healthcare, Beijing, 100001, People's Republic of China
| | - Sheng Xie
- Department of Radiology, China-Japan Friendship Hospital, No.2 Yinghua East Street, Chaoyang District, Beijing, 100029, People's Republic of China
| | - Wu Wang
- Department of Radiology, China-Japan Friendship Hospital, No.2 Yinghua East Street, Chaoyang District, Beijing, 100029, People's Republic of China
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Fu Y, Liu X, Yang Q, Sun J, Xie Y, Zhang Y, Zhang H. Radiomic features based on MRI for prediction of lymphovascular invasion in rectal cancer. ACTA ACUST UNITED AC 2019. [DOI: 10.1007/s42058-019-00016-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Horvat N, Bates DDB, Petkovska I. Novel imaging techniques of rectal cancer: what do radiomics and radiogenomics have to offer? A literature review. Abdom Radiol (NY) 2019; 44:3764-3774. [PMID: 31055615 DOI: 10.1007/s00261-019-02042-y] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
INTRODUCTION As computational capabilities have advanced, radiologists and their collaborators have looked for novel ways to analyze diagnostic images. This has resulted in the development of radiomics and radiogenomics as new fields in medical imaging. Radiomics and radiogenomics may change the practice of medicine, particularly for patients with colorectal cancer. Radiomics corresponds to the extraction and analysis of numerous quantitative imaging features from conventional imaging modalities in correlation with several endpoints, including the prediction of pathology, genomics, therapeutic response, and clinical outcome. In radiogenomics, qualitative and/or quantitative imaging features are extracted and correlated with genetic profiles of the imaged tissue. Thus far, several studies have evaluated the use of radiomics and radiogenomics in patients with colorectal cancer; however, there are challenges to be overcome before its routine implementation including challenges related to sample size, model design and interpretability, and the lack of robust multicenter validation set. MATERIAL AND METHODS In this article, we will review the concepts of radiomics and radiogenomics and their potential applications in rectal cancer. CONCLUSION Radiologists should be aware of the basic concepts, benefits, pitfalls, and limitations of new radiomic and radiogenomics techniques to achieve a balanced interpretation of the results.
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Affiliation(s)
- Natally Horvat
- Department of Radiology, Hospital Sirio-Libanes, Sao Paulo, SP, Brazil
| | - David D B Bates
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Iva Petkovska
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA.
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Zhang Y, Chen C, Tian Z, Feng R, Cheng Y, Xu J. The Diagnostic Value of MRI-Based Texture Analysis in Discrimination of Tumors Located in Posterior Fossa: A Preliminary Study. Front Neurosci 2019; 13:1113. [PMID: 31708724 PMCID: PMC6819318 DOI: 10.3389/fnins.2019.01113] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 10/02/2019] [Indexed: 02/05/2023] Open
Abstract
Objectives To investigate the diagnostic value of MRI-based texture analysis in discriminating common posterior fossa tumors, including medulloblastoma, brain metastatic tumor, and hemangioblastoma. Methods A total number of 185 patients were enrolled in the current study: 63 of them were diagnosed with medulloblastoma, 56 were diagnosed with brain metastatic tumor, and 66 were diagnosed with hemangioblastoma. Texture features were extracted from contrast-enhanced T1-weighted (T1C) images and fluid-attenuation inversion recovery (FLAIR) images within two matrixes. Mann–Whitney U test was conducted to identify whether texture features were significantly different among subtypes of tumors. Logistic regression analysis was performed to assess if they could be taken as independent predictors and to establish the integrated models. Receiver operating characteristic analysis was conducted to evaluate their performances in discrimination. Results There were texture features from both T1C images and FLAIR images found to be significantly different among the three types of tumors. The integrated model represented that the promising diagnostic performance of texture analysis depended on a series of features rather than a single feature. Moreover, the predictive model that combined texture features and clinical feature implied feasible performance in prediction with an accuracy of 0.80. Conclusion MRI-based texture analysis could potentially be served as a radiological method in discrimination of common tumors located in posterior fossa.
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Affiliation(s)
- Yang Zhang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.,West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Chaoyue Chen
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.,West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Zerong Tian
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.,West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Ridong Feng
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.,West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Yangfan Cheng
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Jianguo Xu
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.,West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
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Prezzi D, Owczarczyk K, Bassett P, Siddique M, Breen DJ, Cook GJR, Goh V. Adaptive statistical iterative reconstruction (ASIR) affects CT radiomics quantification in primary colorectal cancer. Eur Radiol 2019; 29:5227-5235. [PMID: 30887205 PMCID: PMC6717179 DOI: 10.1007/s00330-019-06073-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 01/21/2019] [Accepted: 02/05/2019] [Indexed: 12/22/2022]
Abstract
OBJECTIVES To investigate whether adaptive statistical iterative reconstruction (ASIR), a hybrid iterative CT image reconstruction algorithm, affects radiomics feature quantification in primary colorectal cancer compared to filtered back projection. Additionally, to establish whether radiomics from single-slice analysis undergo greater change than those from multi-slice analysis. METHODS Following review board approval, contrast-enhanced CT studies from 32 prospective primary colorectal cancer patients were reconstructed with 20% ASIR level increments, from 0 to 100%. Radiomics analysis was applied to single-slice and multi-slice regions of interest outlining the tumour: 70 features, including statistical (first-, second- and high-order) and fractal radiomics, were generated per dataset. The effect of ASIR was calculated by means of multilevel linear regression. RESULTS Twenty-eight CT datasets were suitable for analysis. Incremental ASIR levels determined a significant change (p < 0.001) in most statistical radiomics features, best described by a simple linear relationship. First-order statistical features, including mean, standard deviation, skewness, kurtosis, energy and entropy, underwent a relatively small change in both single-slice and multi-slice analysis (median standardised effect size B = 0.08). Second-order statistical features, including grey-level co-occurrence and difference matrices, underwent a greater change in single-slice analysis (median B = 0.36) than in multi-slice analysis (median B = 0.13). Fractal features underwent a significant change only in single-slice analysis (median B = 0.49). CONCLUSIONS Incremental levels of ASIR affect significantly CT radiomics quantification in primary colorectal cancer. Second-order statistical and fractal features derived from single-slice analysis undergo greater change than those from multi-slice analysis. KEY POINTS • Incremental levels of ASIR determine a significant change in most statistical (first-, second- and high-order) CT radiomics features measured in primary colorectal cancer, best described by a linear relationship. • First-order statistical features undergo a small change, both from single-slice and multi-slice radiomics analyses. • Most second-order statistical features undergo a greater change in single-slice analysis than in multi-slice analysis. Fractal features are only affected in single-slice analysis.
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Affiliation(s)
- Davide Prezzi
- School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, Lambeth Wing, St Thomas' Hospital, London, SE1 7EH, UK.
- Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, Lambeth Wing, St Thomas' Hospital, London, SE1 7EH, UK.
| | - Katarzyna Owczarczyk
- School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, Lambeth Wing, St Thomas' Hospital, London, SE1 7EH, UK
- Department of Clinical Oncology, Guy's and St Thomas' NHS Foundation Trust, Lambeth Wing, St Thomas' Hospital, London, SE1 7EH, UK
| | - Paul Bassett
- Statsconsultancy Ltd., 40 Longwood Lane, Amersham, Bucks, HP7 9EN, UK
| | - Muhammad Siddique
- School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, Lambeth Wing, St Thomas' Hospital, London, SE1 7EH, UK
| | - David J Breen
- University Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton, SO16 6YD, UK
| | - Gary J R Cook
- School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, Lambeth Wing, St Thomas' Hospital, London, SE1 7EH, UK
- King's College London & Guy's and St Thomas' PET Centre, Lambeth Wing, St Thomas' Hospital, London, SE1 7EH, UK
| | - Vicky Goh
- School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, Lambeth Wing, St Thomas' Hospital, London, SE1 7EH, UK
- Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, Lambeth Wing, St Thomas' Hospital, London, SE1 7EH, UK
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The Impact of Extramural Venous Invasion in Colorectal Cancer: A Detailed Analysis Based on Tumor Location and Evaluation Methods. Int J Surg Pathol 2019; 28:120-127. [DOI: 10.1177/1066896919874493] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
This study aimed to elucidate the prognostic implications of extramural venous invasion (EMVI) in colorectal cancer (CRC) through a meta-analysis. Eighteen eligible studies were included in this meta-analysis. Data on the prevalence of EMVI and the correlation between EMVI and survival were collected from these studies. In addition, a subgroup analysis was conducted based on tumor location and evaluation methods. The estimated prevalence of EMVI was 28.3% (95% confidence interval [CI] = 23.1% to 34.0%) in patients with CRC. The estimated prevalence of EMVI in patients with colon cancer and rectal cancer was 23.0% (95% CI = 17.6% to 29.6%) and 35.7% (95% CI = 22.3% to 51.6%), respectively. Based on the evaluation method, the estimated prevalence of EMVI were 28.3% (95% CI = 23.2% to 34.1%) and 27.3% (95% CI = 8.4% to 60.6%) in pathologic and radiologic examinations, respectively. The correlation of EMVI with worse overall and disease-free survival rates was significant (hazard ratio = 1.773, 95% CI = 1.483-2.120, and hazard ratio = 2.059, 95% CI = 1.683-2.520, respectively). However, in the subgroup analysis with radiologic examination, there was no significant difference in survival rates between patients with and without EMVI. Our study showed that EMVI was frequently detected in 28.3% of patients with CRC and was correlated to worse survival. The detection of EMVI can be useful for predicting the prognosis of patients with CRC.
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Rectal cancer: can T2WI histogram of the primary tumor help predict the existence of lymph node metastasis? Eur Radiol 2019; 29:6469-6476. [PMID: 31278581 DOI: 10.1007/s00330-019-06328-z] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 06/03/2019] [Accepted: 06/13/2019] [Indexed: 02/06/2023]
Abstract
OBJECTIVES To explore if there is a correlation between T2WI histogram features of the primary tumor and the existence of regional lymph node (LN) metastasis in rectal cancer. METHODS Eighty-eight patients with pathologically proven rectal adenocarcinoma, who received direct surgical resection and underwent preoperative rectal MRIs, were enrolled retrospectively. Based on pathological analysis of surgical specimen, patients were classified into negative LN (LN-) and positive LN (LN+) groups. The degree of differentiation and pathological T stage were recorded. Clinical T stage, tumor location, and maximum diameter of tumor were evaluated of each patient. Whole-tumor texture analysis was independently performed by two radiologists on axial T2WI, including skewness, kurtosis, energy, and entropy. RESULTS The interobserver agreement was overall good for texture analysis between two radiologists, with intraclass correlation coefficients (ICCs) ranging from 0.626 to 0.826. The LN- group had a significantly higher skewness (p < 0.001), kurtosis (p < 0.001), and energy (p = 0.004) than the LN+ group, and a lower entropy (p = 0.028). These four parameters showed moderate to good diagnostic power in predicting LN metastasis with respective AUC of 0.750, 0.733, 0.669, and 0.648. In addition, they were both correlated with LN metastasis (rs = - 0.413, - 0.385, - 0.28, and 0.245, respectively). The multivariate analysis showed that lower skewness was an independent risk factor of LN metastasis (odds ratio, OR = 9.832; 95%CI, 1.171-56.295; p = 0.01). CONCLUSIONS Signal intensity histogram parameters of primary tumor on T2WI were associated with regional LN status in rectal cancer, which may help improve the prediction of nodal stage. KEY POINTS • Histogram parameters of tumor on T2WI may help to reduce uncertainty when assessing LN status in rectal cancer. • Histogram parameters of tumor on T2WI showed a significant difference between different regional LN status groups in rectal cancer. • Skewness was an independent risk factor of regional LN metastasis in rectal cancer.
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Choi IY, Yeom SK, Cha J, Cha SH, Lee SH, Chung HH, Lee CM, Choi J. Feasibility of using computed tomography texture analysis parameters as imaging biomarkers for predicting risk grade of gastrointestinal stromal tumors: comparison with visual inspection. Abdom Radiol (NY) 2019; 44:2346-2356. [PMID: 30923842 DOI: 10.1007/s00261-019-01995-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
PURPOSE To evaluate the feasibility of using computed tomography texture analysis (CTTA) parameters for predicting malignant risk grade and mitosis index of gastrointestinal stromal tumors (GISTs), compared with visual inspection. METHOD AND MATERIALS CTTA was performed on portal phase CT images of 145 surgically confirmed GISTs (mean size: 42.9 ± 37.5 mm), using TexRAD software. Mean, standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis of CTTA parameters, on spatial scaling factor (SSF), 2-6 were compared by risk grade, mitosis rate, and the presence or absence of necrosis on visual inspection. CTTA parameters were correlated with risk grade. Diagnostic performance was evaluated with receiver operating characteristic curve analysis. Enhancement pattern, necrosis, heterogeneity, calcification, growth pattern, and mucosal ulceration were subjectively evaluated by two observers. RESULTS Three to four parameters at different scales were significantly different according to the risk grade, mitosis rate, and the presence or absence of necrosis (p < 0.041). MPP at fine or medium scale (r = - 0.547 to - 393) and kurtosis at coarse scale (r = 0.424-0.454) correlated significantly with risk grade (p < 0.001). HG-GIST was best differentiated from LG-GIST by MPP at SSF 2 (AUC, 0.782), and kurtosis at SSF 4 (AUC, 0.779) (all p < 0.001). CT features predictive of HG-GIST were density lower than or equal to that of the erector spinae muscles on enhanced images (OR 2.1; p = 0.037; AUC, 0.59), necrosis (OR, 6.1; p < 0.001; AUC, 0.70), heterogeneity (OR, 4.3; p < 0.001; AUC, 0.67), and mucosal ulceration (OR, 3.3; p = 0.002; AUC, 0.62). CONCLUSION Using TexRAD, MPP and kurtosis are feasible in predicting risk grade and mitosis index of GISTs. CTTA demonstrated meaningful accuracy in preoperative risk stratification of GISTs.
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Affiliation(s)
- In Young Choi
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan, 15355, Republic of Korea
| | - Suk Keu Yeom
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan, 15355, Republic of Korea.
| | - Jaehyung Cha
- Department of Biostatistics, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan, 15355, Republic of Korea
| | - Sang Hoon Cha
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan, 15355, Republic of Korea
| | - Seung Hwa Lee
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan, 15355, Republic of Korea
| | - Hwan Hoon Chung
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan, 15355, Republic of Korea
| | - Chang Min Lee
- Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan, 15355, Republic of Korea
| | - Jungwoo Choi
- Department of Pathology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan, 15355, Republic of Korea
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Jeon SH, Song C, Chie EK, Kim B, Kim YH, Chang W, Lee YJ, Chung JH, Chung JB, Lee KW, Kang SB, Kim JS. Delta-radiomics signature predicts treatment outcomes after preoperative chemoradiotherapy and surgery in rectal cancer. Radiat Oncol 2019; 14:43. [PMID: 30866965 PMCID: PMC6417065 DOI: 10.1186/s13014-019-1246-8] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Accepted: 02/28/2019] [Indexed: 12/31/2022] Open
Abstract
Background To develop and compare delta-radiomics signatures from 2- (2D) and 3-dimensional (3D) features that predict treatment outcomes following preoperative chemoradiotherapy (CCRT) and surgery for locally advanced rectal cancer. Methods In total, 101 patients (training cohort, n = 67; validation cohort, n = 34) with locally advanced rectal adenocarcinoma between 2008 and 2015 were included. We extracted 55 features from T2-weighted magnetic resonance imaging (MRI) scans. Delta-radiomics feature was defined as the difference in radiomics feature before and after CCRT. Signatures were developed to predict local recurrence (LR), distant metastasis (DM), and disease-free survival (DFS) from 2D and 3D features. The least absolute shrinkage and selection operator regression was used to select features and build signatures. The delta-radiomics signatures and clinical factors were integrated into Cox regression analysis to determine if the signatures were independent prognostic factors. Results The radiomics signatures for LR, DM, and DFS were developed and validated using both 2D and 3D features. Outcomes were significantly different in the low- and high-risk patients dichotomized by optimal cutoff in both the training and validation cohorts. In multivariate analysis, the signatures were independent prognostic factors even when considering the clinical parameters. There were no significant differences in C-index from 2D vs. 3D signatures. Conclusions This is the first study to develop delta-radiomics signatures for rectal cancer. The signatures successfully predicted the outcomes and were independent prognostic factors. External validation is warranted to ensure their performance. Electronic supplementary material The online version of this article (10.1186/s13014-019-1246-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Seung Hyuck Jeon
- Department of Radiation Oncology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Changhoon Song
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 82 Gumi-ro 173beon-gil, Bundang-gu, Seongnam, 13620, Republic of Korea
| | - Eui Kyu Chie
- Department of Radiation Oncology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Bohyoung Kim
- Division of Biomedical Engineering, Hankuk University of Foreign Studies, 81 Oedae-ro, Mohyeon-eup, Cheoin-gu, Yongin, 17035, Republic of Korea
| | - Young Hoon Kim
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 82 Gumi-ro 173beon-gil, Bundang-gu, Seongnam, 13620, Republic of Korea
| | - Won Chang
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 82 Gumi-ro 173beon-gil, Bundang-gu, Seongnam, 13620, Republic of Korea
| | - Yoon Jin Lee
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 82 Gumi-ro 173beon-gil, Bundang-gu, Seongnam, 13620, Republic of Korea
| | - Joo-Hyun Chung
- Department of Radiation Oncology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Jin Beom Chung
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 82 Gumi-ro 173beon-gil, Bundang-gu, Seongnam, 13620, Republic of Korea
| | - Keun-Wook Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 82 Gumi-ro 173beon-gil, Bundang-gu, Seongnam, 13620, Republic of Korea
| | - Sung-Bum Kang
- Department of Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 82 Gumi-ro 173beon-gil, Bundang-gu, Seongnam, 13620, Republic of Korea
| | - Jae-Sung Kim
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 82 Gumi-ro 173beon-gil, Bundang-gu, Seongnam, 13620, Republic of Korea.
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Aker M, Ganeshan B, Afaq A, Wan S, Groves AM, Arulampalam T. Magnetic Resonance Texture Analysis in Identifying Complete Pathological Response to Neoadjuvant Treatment in Locally Advanced Rectal Cancer. Dis Colon Rectum 2019; 62:163-170. [PMID: 30451764 DOI: 10.1097/dcr.0000000000001224] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND A certain proportion of patients with locally advanced rectal cancer experience complete response after undergoing neoadjuvant chemoradiotherapy. These patients might be suitable for a conservative "watch and wait" approach, avoiding high-morbidity surgery. Texture analysis is a new modality that can assess heterogeneity in medical images by statistically analyzing gray-level intensities on a pixel-by-pixel basis. This study hypothesizes that texture analysis of magnetic resonance images can identify patients with a complete response. OBJECTIVE This study aims to determine whether texture analysis of magnetic resonance images as a quantitative imaging biomarker can accurately identify patients with complete response. DESIGN This is a retrospective diagnostic accuracy study. SETTINGS This study was conducted at Colchester General Hospital, January 2003 to 2014. PATIENTS All patients diagnosed with locally advanced rectal cancer who underwent long-course chemoradiotherapy had a posttreatment magnetic resonance scan and underwent surgery are included. INTERVENTION Texture analysis was extracted from T2-weighted magnetic resonance images of the rectal cancer. MAIN OUTCOME MEASURES Textural features that are able to identify complete responders were identified by a Mann-Whitney U test. Their diagnostic accuracy in identifying complete responders was determined by the area under the receiver operator characteristics curve. Cutoff values were determined by the Youden index. Pathology was the standard of reference. RESULTS One hundred fourteen patients with first posttreatment MRI scans (6.2 weeks after completion of neoadjuvant treatment) were included. Sixty-eight patients had a second posttreatment scan (10.4 weeks). With no filtration, mean (p = 0.033), SD (p = 0.048), entropy (p = 0.007), and skewness (p = 0.000) from first posttreatment scans, and SD (p = 0.042), entropy (p = 0.014), mean of positive pixels (p = 0.032), and skewness (p = 0.000) from second posttreatment scans were all able to identify complete response. Area under the curve ranged from 0.750 to 0.88. LIMITATIONS Texture analysis of MRI is a new modality; therefore, further studies are necessary to standardize the methodology of extraction of texture features, timing of scans, and acquisition parameters. CONCLUSIONS Texture analysis of MRI is a potentially significant imaging biomarker that can accurately identify patients who have experienced complete response and might be suitable for a nonsurgical approach. (Cinicaltrials.gov:NCT02439086). See Video Abstract at http://links.lww.com/DCR/A760.
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Affiliation(s)
- Medhat Aker
- Department of General Surgery, Colchester General Hospital, Colchester, United Kingdom
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, University College London Hospitals, London, United Kingdom
| | - Asim Afaq
- Institute of Nuclear Medicine, University College London Hospitals, London, United Kingdom
| | - Simon Wan
- Institute of Nuclear Medicine, University College London Hospitals, London, United Kingdom
| | - Ashley M Groves
- Institute of Nuclear Medicine, University College London Hospitals, London, United Kingdom
| | - Tan Arulampalam
- Department of General Surgery, Colchester General Hospital, Colchester, United Kingdom
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Giannini V, Mazzetti S, Bertotto I, Chiarenza C, Cauda S, Delmastro E, Bracco C, Di Dia A, Leone F, Medico E, Pisacane A, Ribero D, Stasi M, Regge D. Predicting locally advanced rectal cancer response to neoadjuvant therapy with 18F-FDG PET and MRI radiomics features. Eur J Nucl Med Mol Imaging 2019; 46:878-888. [PMID: 30637502 DOI: 10.1007/s00259-018-4250-6] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 12/26/2018] [Indexed: 12/12/2022]
Abstract
PURPOSE Pathological complete response (pCR) following neoadjuvant chemoradiotherapy or radiotherapy in locally advanced rectal cancer (LARC) is reached in approximately 15-30% of cases, therefore it would be useful to assess if pretreatment of 18F-FDG PET/CT and/or MRI texture features can reliably predict response to neoadjuvant therapy in LARC. METHODS Fifty-two patients were dichotomized as responder (pR+) or non-responder (pR-) according to their pathological tumor regression grade (TRG) as follows: 22 as pR+ (nine with TRG = 1, 13 with TRG = 2) and 30 as pR- (16 with TRG = 3, 13 with TRG = 4 and 1 with TRG = 5). First-order parameters and 21 second-order texture parameters derived from the Gray-Level Co-Occurrence matrix were extracted from semi-automatically segmented tumors on T2w MRI, ADC maps, and PET/CT acquisitions. The role of each texture feature in predicting pR+ was assessed with monoparametric and multiparametric models. RESULTS In the mono-parametric approach, PET homogeneity reached the maximum AUC (0.77; sensitivity = 72.7% and specificity = 76.7%), while PET glycolytic volume and ADC dissimilarity reached the highest sensitivity (both 90.9%). In the multiparametric analysis, a logistic regression model containing six second-order texture features (five from PET and one from T2w MRI) yields the highest predictivity in distinguish between pR+ and pR- patients (AUC = 0.86; sensitivity = 86%, and specificity = 83% at the Youden index). CONCLUSIONS If preliminary results of this study are confirmed, pretreatment PET and MRI could be useful to personalize patient treatment, e.g., avoiding toxicity of neoadjuvant therapy in patients predicted pR-.
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Affiliation(s)
- V Giannini
- Imaging Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, TO, Italy. .,Department of Surgical Sciences, University of Turin, 10124, Turin, Italy.
| | - S Mazzetti
- Imaging Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, TO, Italy.,Department of Surgical Sciences, University of Turin, 10124, Turin, Italy
| | - I Bertotto
- Imaging Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, TO, Italy
| | - C Chiarenza
- Imaging Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, TO, Italy
| | - S Cauda
- Nuclear Medicine Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - E Delmastro
- Radiation Therapy Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - C Bracco
- Medical Physics Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - A Di Dia
- Medical Physics Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - F Leone
- Medical Oncology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - E Medico
- Laboratory of Oncogenomics, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - A Pisacane
- Pathology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - D Ribero
- Hepatobilio-Pancreatic and Colorectal Surgery Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - M Stasi
- Medical Physics Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - D Regge
- Imaging Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, TO, Italy.,Department of Surgical Sciences, University of Turin, 10124, Turin, Italy
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Choi MH, Lee YJ, Yoon SB, Choi JI, Jung SE, Rha SE. MRI of pancreatic ductal adenocarcinoma: texture analysis of T2-weighted images for predicting long-term outcome. Abdom Radiol (NY) 2019; 44:122-130. [PMID: 29980829 DOI: 10.1007/s00261-018-1681-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE To assess the association between T2-weighted imaging (T2WI) texture-analysis parameters and the pathological aggressiveness or long-term outcomes in pancreatic ductal adenocarcinoma (PDAC) patients. METHODS A total of 66 patients (mean age 65.3 ± 9.0 years) who underwent preoperative MRI followed by pancreatectomy for PDAC between 2013 and 2015 were included in this study. A radiologist performed a texture analysis twice on one axial image using commercial software. Differences in the tex parameters, according to pathological factors, were analyzed using a Student's t test or an ANOVA with Tukey's test. Univariate and multivariate Cox proportional hazards regression analyses were used to evaluate the association between tex parameters and recurrence-free survival (RFS) or overall survival (OS). RESULTS The mean follow-up time was 18.5 months, and there were 58 recurrences and 39 deaths. The mean of the positive pixel (MPP)-related factors was significantly lower in poorly differentiated tumors than in well-differentiated tumors as well as in cases with perineural invasion. The univariate Cox proportional hazards analysis showed a significant association between the tex parameters and RFS or OS. However, only tumor size was statistically significant after the multivariate analysis. Only tumor size and entropy with medium texture were significantly associated with OS after the multivariate analysis. CONCLUSIONS Tumor size was a significant predictive factor for RFS and OS in PDAC patients. Although entropy with medium texture analysis was significantly associated with OS, there were also limitations in the texture analysis; thus, further study is necessary.
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Abstract
Treatment of locally advanced rectal cancer is evolving through surgical innovation and paradigm shifts in neoadjuvant treatment. Whereas local recurrence was a significant concern before the systematic implementation of neoadjuvant chemoradiation therapy and surgery according to total mesorectal excision principles, distant relapse remains a major drawback. Hence, efforts in recent years have focused on delivering preoperative chemotherapy regimens to overcome compliance issues with adjuvant administration. In parallel, new surgical techniques, including transanal video-assisted total mesorectal excision and robot-assisted surgery, emerged to face the challenge to navigate in the deep and narrow spaces of the pelvis. Furthermore, patients experiencing a complete response after neoadjuvant treatment might even escape surgery within a close surveillance strategy. This novel "watch and wait" concept has gained interest to improve quality of life in highly selected patients. This review summarizes recent evidence and controversies and provides an overview on timely and innovative aspects in the treatment of locally advanced rectal cancer.
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Affiliation(s)
- Fabian Grass
- Department of Surgery, Division of Colon and Rectal Surgery, Mayo Clinic, Rochester, MN, USA
| | - Kellie Mathis
- Department of Surgery, Division of Colon and Rectal Surgery, Mayo Clinic, Rochester, MN, USA
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Pellino G, Gallo G, Pallante P, Capasso R, De Stefano A, Maretto I, Malapelle U, Qiu S, Nikolaou S, Barina A, Clerico G, Reginelli A, Giuliani A, Sciaudone G, Kontovounisios C, Brunese L, Trompetto M, Selvaggi F. Noninvasive Biomarkers of Colorectal Cancer: Role in Diagnosis and Personalised Treatment Perspectives. Gastroenterol Res Pract 2018; 2018:2397863. [PMID: 30008744 PMCID: PMC6020538 DOI: 10.1155/2018/2397863] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 04/03/2018] [Accepted: 04/15/2018] [Indexed: 02/08/2023] Open
Abstract
Colorectal cancer (CRC) is the third leading cause of cancer-related deaths worldwide. It has been estimated that more than one-third of patients are diagnosed when CRC has already spread to the lymph nodes. One out of five patients is diagnosed with metastatic CRC. The stage of diagnosis influences treatment outcome and survival. Notwithstanding the recent advances in multidisciplinary management and treatment of CRC, patients are still reluctant to undergo screening tests because of the associated invasiveness and discomfort (e.g., colonoscopy with biopsies). Moreover, the serological markers currently used for diagnosis are not reliable and, even if they were useful to detect disease recurrence after treatment, they are not always detected in patients with CRC (e.g., CEA). Recently, translational research in CRC has produced a wide spectrum of potential biomarkers that could be useful for diagnosis, treatment, and follow-up of these patients. The aim of this review is to provide an overview of the newer noninvasive or minimally invasive biomarkers of CRC. Here, we discuss imaging and biomolecular diagnostics ranging from their potential usefulness to obtain early and less-invasive diagnosis to their potential implementation in the development of a bespoke treatment of CRC.
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Affiliation(s)
- Gianluca Pellino
- Unit of General Surgery, Department of Medical, Surgical, Neurological, Metabolic and Ageing Sciences, Università degli Studi della Campania “Luigi Vanvitelli”, Piazza Miraglia 2, 80138 Naples, Italy
- Colorectal Surgery Unit, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Gaetano Gallo
- Department of Medical and Surgical Sciences, OU of General Surgery, University of Catanzaro, Catanzaro, Italy
- Department of Colorectal Surgery, Clinic S. Rita, Vercelli, Italy
| | - Pierlorenzo Pallante
- Institute of Experimental Endocrinology and Oncology (IEOS), National Research Council (CNR), Via S. Pansini 5, Naples, Italy
| | - Raffaella Capasso
- Department of Medicine and Health Sciences, University of Molise, Via Francesco de Sanctis 1, 86100 Campobasso, Italy
| | - Alfonso De Stefano
- Department of Abdominal Oncology, Division of Abdominal Medical Oncology, Istituto Nazionale per lo Studio e la Cura dei Tumori, “Fondazione G. Pascale, ” IRCCS, Naples, Italy
| | - Isacco Maretto
- 1st Surgical Clinic, Department of Surgical, Oncological, and Gastroenterological Sciences, University of Padua, Padua, Italy
| | - Umberto Malapelle
- Dipartimento di Sanità Pubblica, Università degli Studi di Napoli Federico II, Naples, Italy
| | - Shengyang Qiu
- Department of Colorectal Surgery, Royal Marsden Hospital, London, UK
| | - Stella Nikolaou
- Department of Colorectal Surgery, Royal Marsden Hospital, London, UK
| | - Andrea Barina
- 1st Surgical Clinic, Department of Surgical, Oncological, and Gastroenterological Sciences, University of Padua, Padua, Italy
| | - Giuseppe Clerico
- Department of Colorectal Surgery, Clinic S. Rita, Vercelli, Italy
| | - Alfonso Reginelli
- Department of Internal and Experimental Medicine, Magrassi-Lanzara, Institute of Radiology, Università degli Studi della Campania “Luigi Vanvitelli”, Piazza Miraglia 2, 80138 Naples, Italy
| | - Antonio Giuliani
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, Campobasso, Italy
| | - Guido Sciaudone
- Unit of General Surgery, Department of Medical, Surgical, Neurological, Metabolic and Ageing Sciences, Università degli Studi della Campania “Luigi Vanvitelli”, Piazza Miraglia 2, 80138 Naples, Italy
| | - Christos Kontovounisios
- Department of Colorectal Surgery, Royal Marsden Hospital, London, UK
- Department of Surgery and Cancer, Chelsea and Westminster Hospital Campus, Imperial College London, London, UK
| | - Luca Brunese
- Department of Medicine and Health Sciences, University of Molise, Via Francesco de Sanctis 1, 86100 Campobasso, Italy
| | - Mario Trompetto
- Department of Colorectal Surgery, Clinic S. Rita, Vercelli, Italy
| | - Francesco Selvaggi
- Unit of General Surgery, Department of Medical, Surgical, Neurological, Metabolic and Ageing Sciences, Università degli Studi della Campania “Luigi Vanvitelli”, Piazza Miraglia 2, 80138 Naples, Italy
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Can MR textural analysis improve the prediction of extracapsular nodal spread in patients with oral cavity cancer? Eur Radiol 2018; 28:5010-5018. [DOI: 10.1007/s00330-018-5524-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 04/23/2018] [Accepted: 05/03/2018] [Indexed: 01/18/2023]
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García-Figueiras R, Baleato-González S, Padhani AR, Luna-Alcalá A, Marhuenda A, Vilanova JC, Osorio-Vázquez I, Martínez-de-Alegría A, Gómez-Caamaño A. Advanced Imaging Techniques in Evaluation of Colorectal Cancer. Radiographics 2018; 38:740-765. [PMID: 29676964 DOI: 10.1148/rg.2018170044] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Imaging techniques are clinical decision-making tools in the evaluation of patients with colorectal cancer (CRC). The aim of this article is to discuss the potential of recent advances in imaging for diagnosis, prognosis, therapy planning, and assessment of response to treatment of CRC. Recent developments and new clinical applications of conventional imaging techniques such as virtual colonoscopy, dual-energy spectral computed tomography, elastography, advanced computing techniques (including volumetric rendering techniques and machine learning), magnetic resonance (MR) imaging-based magnetization transfer, and new liver imaging techniques, which may offer additional clinical information in patients with CRC, are summarized. In addition, the clinical value of functional and molecular imaging techniques such as diffusion-weighted MR imaging, dynamic contrast material-enhanced imaging, blood oxygen level-dependent imaging, lymphography with contrast agents, positron emission tomography with different radiotracers, and MR spectroscopy is reviewed, and the advantages and disadvantages of these modalities are evaluated. Finally, the future role of imaging-based analysis of tumor heterogeneity and multiparametric imaging, the development of radiomics and radiogenomics, and future challenges for imaging of patients with CRC are discussed. Online supplemental material is available for this article. ©RSNA, 2018.
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Affiliation(s)
- Roberto García-Figueiras
- From the Departments of Radiology (R.G.F., S.B.G., I.O.V., A.M.d.A.) and Radiation Oncology (A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Health Time, Jaén, Spain (A.L.A.); Department of Radiology, University Hospitals of Cleveland, Case Western Reserve University, Cleveland, Ohio (A.L.A.); Department of Radiology, IVO (Instituto Valenciano de Oncología), Valencia, Spain (A.M.); and Department of Radiology, Clínica Girona and IDI, Girona, Spain (J.C.V.)
| | - Sandra Baleato-González
- From the Departments of Radiology (R.G.F., S.B.G., I.O.V., A.M.d.A.) and Radiation Oncology (A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Health Time, Jaén, Spain (A.L.A.); Department of Radiology, University Hospitals of Cleveland, Case Western Reserve University, Cleveland, Ohio (A.L.A.); Department of Radiology, IVO (Instituto Valenciano de Oncología), Valencia, Spain (A.M.); and Department of Radiology, Clínica Girona and IDI, Girona, Spain (J.C.V.)
| | - Anwar R Padhani
- From the Departments of Radiology (R.G.F., S.B.G., I.O.V., A.M.d.A.) and Radiation Oncology (A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Health Time, Jaén, Spain (A.L.A.); Department of Radiology, University Hospitals of Cleveland, Case Western Reserve University, Cleveland, Ohio (A.L.A.); Department of Radiology, IVO (Instituto Valenciano de Oncología), Valencia, Spain (A.M.); and Department of Radiology, Clínica Girona and IDI, Girona, Spain (J.C.V.)
| | - Antonio Luna-Alcalá
- From the Departments of Radiology (R.G.F., S.B.G., I.O.V., A.M.d.A.) and Radiation Oncology (A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Health Time, Jaén, Spain (A.L.A.); Department of Radiology, University Hospitals of Cleveland, Case Western Reserve University, Cleveland, Ohio (A.L.A.); Department of Radiology, IVO (Instituto Valenciano de Oncología), Valencia, Spain (A.M.); and Department of Radiology, Clínica Girona and IDI, Girona, Spain (J.C.V.)
| | - Ana Marhuenda
- From the Departments of Radiology (R.G.F., S.B.G., I.O.V., A.M.d.A.) and Radiation Oncology (A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Health Time, Jaén, Spain (A.L.A.); Department of Radiology, University Hospitals of Cleveland, Case Western Reserve University, Cleveland, Ohio (A.L.A.); Department of Radiology, IVO (Instituto Valenciano de Oncología), Valencia, Spain (A.M.); and Department of Radiology, Clínica Girona and IDI, Girona, Spain (J.C.V.)
| | - Joan C Vilanova
- From the Departments of Radiology (R.G.F., S.B.G., I.O.V., A.M.d.A.) and Radiation Oncology (A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Health Time, Jaén, Spain (A.L.A.); Department of Radiology, University Hospitals of Cleveland, Case Western Reserve University, Cleveland, Ohio (A.L.A.); Department of Radiology, IVO (Instituto Valenciano de Oncología), Valencia, Spain (A.M.); and Department of Radiology, Clínica Girona and IDI, Girona, Spain (J.C.V.)
| | - Iria Osorio-Vázquez
- From the Departments of Radiology (R.G.F., S.B.G., I.O.V., A.M.d.A.) and Radiation Oncology (A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Health Time, Jaén, Spain (A.L.A.); Department of Radiology, University Hospitals of Cleveland, Case Western Reserve University, Cleveland, Ohio (A.L.A.); Department of Radiology, IVO (Instituto Valenciano de Oncología), Valencia, Spain (A.M.); and Department of Radiology, Clínica Girona and IDI, Girona, Spain (J.C.V.)
| | - Anxo Martínez-de-Alegría
- From the Departments of Radiology (R.G.F., S.B.G., I.O.V., A.M.d.A.) and Radiation Oncology (A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Health Time, Jaén, Spain (A.L.A.); Department of Radiology, University Hospitals of Cleveland, Case Western Reserve University, Cleveland, Ohio (A.L.A.); Department of Radiology, IVO (Instituto Valenciano de Oncología), Valencia, Spain (A.M.); and Department of Radiology, Clínica Girona and IDI, Girona, Spain (J.C.V.)
| | - Antonio Gómez-Caamaño
- From the Departments of Radiology (R.G.F., S.B.G., I.O.V., A.M.d.A.) and Radiation Oncology (A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Health Time, Jaén, Spain (A.L.A.); Department of Radiology, University Hospitals of Cleveland, Case Western Reserve University, Cleveland, Ohio (A.L.A.); Department of Radiology, IVO (Instituto Valenciano de Oncología), Valencia, Spain (A.M.); and Department of Radiology, Clínica Girona and IDI, Girona, Spain (J.C.V.)
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Caruso D, Zerunian M, Ciolina M, de Santis D, Rengo M, Soomro MH, Giunta G, Conforto S, Schmid M, Neri E, Laghi A. Haralick's texture features for the prediction of response to therapy in colorectal cancer: a preliminary study. Radiol Med 2017; 123:161-167. [PMID: 29119525 DOI: 10.1007/s11547-017-0833-8] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 11/02/2017] [Indexed: 12/28/2022]
Abstract
PURPOSE Haralick features Texture analysis is a recent oncologic imaging biomarker used to assess quantitatively the heterogeneity within a tumor. The aim of this study is to evaluate which Haralick's features are the most feasible in predicting tumor response to neoadjuvant chemoradiotherapy (CRT) in colorectal cancer. MATERIALS AND METHODS After MRI and histological assessment, eight patients were enrolled and divided into two groups based on response to neoadjuvant CRT in complete responders (CR) and non-responders (NR). Oblique Axial T2-weighted MRI sequences before CRT were analyzed by two radiologists in consensus drawing a ROI around the tumor. 14 over 192 Haralick's features were extrapolated from normalized gray-level co-occurrence matrix in four different directions. A dedicated statistical analysis was performed to evaluate distribution of the extracted Haralick's features computing mean and standard deviation. RESULTS Pretreatment MRI examination showed significant value (p < 0.05) of 5 over 14 computed Haralick texture. In particular, the significant features are the following: concerning energy, contrast, correlation, entropy and inverse difference moment. CONCLUSIONS Five Haralick's features showed significant relevance in the prediction of response to therapy in colorectal cancer and might be used as additional imaging biomarker in the oncologic management of colorectal patients.
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Affiliation(s)
- Damiano Caruso
- Department of Radiological Sciences, Oncology and Pathology, "Sapienza" - University of Rome, I.C.O.T. Hospital, Via Franco Faggiana 1668, 04100, Latina, Italy
| | - Marta Zerunian
- Department of Radiological Sciences, Oncology and Pathology, "Sapienza" - University of Rome, I.C.O.T. Hospital, Via Franco Faggiana 1668, 04100, Latina, Italy
| | - Maria Ciolina
- Department of Radiological Sciences, Oncology and Pathology, "Sapienza" - University of Rome, I.C.O.T. Hospital, Via Franco Faggiana 1668, 04100, Latina, Italy
| | - Domenico de Santis
- Department of Radiological Sciences, Oncology and Pathology, "Sapienza" - University of Rome, I.C.O.T. Hospital, Via Franco Faggiana 1668, 04100, Latina, Italy
| | - Marco Rengo
- Department of Radiological Sciences, Oncology and Pathology, "Sapienza" - University of Rome, I.C.O.T. Hospital, Via Franco Faggiana 1668, 04100, Latina, Italy
| | - Mumtaz H Soomro
- Department of Engineering, University of Roma Tre, Via Vito Volterra 62, 00146, Rome, Italy
| | - Gaetano Giunta
- Department of Engineering, University of Roma Tre, Via Vito Volterra 62, 00146, Rome, Italy
| | - Silvia Conforto
- Department of Engineering, University of Roma Tre, Via Vito Volterra 62, 00146, Rome, Italy
| | - Maurizio Schmid
- Department of Engineering, University of Roma Tre, Via Vito Volterra 62, 00146, Rome, Italy
| | - Emanuele Neri
- Department of Radiological Sciences, AOUP, Via Savi 10, 56126, Pisa, Italy
| | - Andrea Laghi
- Department of Radiological Sciences, Oncology and Pathology, "Sapienza" - University of Rome, I.C.O.T. Hospital, Via Franco Faggiana 1668, 04100, Latina, Italy.
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