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Pan YN, Gu MY, Mao QL, Wang HY, Liang YC, Zhang L, Tang GY. The Clinical Value of Apparent Diffusion Coefficient of Readout Segmentation of Long Variable Echo Trains and Correlation With Ki-67 Expression in Distal Rectal Cancer. J Comput Assist Tomogr 2024; 48:361-369. [PMID: 38110307 DOI: 10.1097/rct.0000000000001573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
OBJECTIVE The aim of the study is to explore the clinical value of the apparent diffusion coefficient (ADC) derived from the readout segmentation of long variable echo trains (RESOLVE) technique for identifying clinicopathologic features of distal rectal cancer and correlations between ADC and Ki-67 expression. METHODS The data of 112 patients with a proven pathology of distal rectal cancer who underwent preoperative magnetic resonance imaging were retrospectively analyzed. The mean ADC value was measured using the "full-layer and center" method. Differences in ADC values and Ki-67 expression in different clinical stages, pathological types, and tumor differentiation were compared using analysis of variance. Correlations between ADC value and clinicopathologic features were assessed using Spearman correlation analysis. RESULTS Interobserver agreement of confidence levels from 2 radiologists was excellent for ADC measurement ( k = 0.85). Patients with a lower clinical stage, well-differentiated adenocarcinomas, and a higher possibility of mucinous adenocarcinoma exhibited a positive correlation with higher ADC values, but these factors were negatively correlated with Ki-67 expression (all P < 0.05). We found that ADC value was negatively correlated with Ki-67 expression ( r = -0.62, P < 0.001). CONCLUSIONS The ADC value generated by RESOLVE sequences was significantly associated with clinicopathologic features and Ki-67 expression in patients with distal rectal cancer in this study. Thus, the ADC value could be considered a new noninvasive imaging biomarker that could be helpful in predicting the biological properties of distal rectal cancer.
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Affiliation(s)
| | - Meng-Yin Gu
- Department of Medical College, Ningbo University, Ningbo, China
| | - Quan-Liang Mao
- Department of Radiology, The First Affiliated Hospital of Ningbo University
| | - Hui-Ying Wang
- Department of Medical College, Ningbo University, Ningbo, China
| | - Yi-Chuan Liang
- Department of Medical College, Ningbo University, Ningbo, China
| | - Lin Zhang
- From the Department of Radiology, Shanghai Tenth People's Hospital of Tongji University, Shanghai
| | - Guang-Yu Tang
- From the Department of Radiology, Shanghai Tenth People's Hospital of Tongji University, Shanghai
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Qin Q, Gan X, Lin P, Pang J, Gao R, Wen R, Liu D, Tang Q, Liu C, He Y, Yang H, Wu Y. Development and validation of a multi-modal ultrasomics model to predict response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. BMC Med Imaging 2024; 24:65. [PMID: 38500022 PMCID: PMC10946192 DOI: 10.1186/s12880-024-01237-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 03/02/2024] [Indexed: 03/20/2024] Open
Abstract
OBJECTIVES To assess the performance of multi-modal ultrasomics model to predict efficacy to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) and compare with the clinical model. MATERIALS AND METHODS This study retrospectively included 106 patients with LARC who underwent total mesorectal excision after nCRT between April 2018 and April 2023 at our hospital, randomly divided into a training set of 74 and a validation set of 32 in a 7: 3 ratios. Ultrasomics features were extracted from the tumors' region of interest of B-mode ultrasound (BUS) and contrast-enhanced ultrasound (CEUS) images based on PyRadiomics. Mann-Whitney U test, spearman, and least absolute shrinkage and selection operator algorithms were utilized to reduce features dimension. Five models were built with ultrasomics and clinical analysis using multilayer perceptron neural network classifier based on python. Including BUS, CEUS, Combined_1, Combined_2 and Clinical models. The diagnostic performance of models was assessed with the area under the curve (AUC) of the receiver operating characteristic. The DeLong testing algorithm was utilized to compare the models' overall performance. RESULTS The AUC (95% confidence interval [CI]) of the five models in the validation cohort were as follows: BUS 0.675 (95%CI: 0.481-0.868), CEUS 0.821 (95%CI: 0.660-0.983), Combined_1 0.829 (95%CI: 0.673-0.985), Combined_2 0.893 (95%CI: 0.780-1.000), and Clinical 0.690 (95%CI: 0.509-0.872). The Combined_2 model was the best in the overall prediction performance, showed significantly better compared to the Clinical model after DeLong testing (P < 0.01). Both univariate and multivariate logistic regression analyses showed that age (P < 0.01) and clinical stage (P < 0.01) could be an independent predictor of efficacy after nCRT in patients with LARC. CONCLUSION The ultrasomics model had better diagnostic performance to predict efficacy to nCRT in patients with LARC than the Clinical model.
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Affiliation(s)
- Qiong Qin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Xiangyu Gan
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Peng Lin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Jingshu Pang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Ruizhi Gao
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Rong Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Dun Liu
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Quanquan Tang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Changwen Liu
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Yun He
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China.
| | - Yuquan Wu
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, China.
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Bai G, Wang C, Sun Y, Li J, Shi X, Zhang W, Yang Y, Yang R. Quantitative analysis of contrast-enhanced ultrasound in neoadjuvant treatment of locally advanced rectal cancer: a retrospective study. Front Oncol 2024; 13:1340060. [PMID: 38322290 PMCID: PMC10844946 DOI: 10.3389/fonc.2023.1340060] [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] [Received: 12/05/2023] [Accepted: 12/26/2023] [Indexed: 02/08/2024] Open
Abstract
Purpose To explore the clinical value of contrast-enhanced ultrasound (CEUS) quantitative analysis in the evaluation and prognosis of neoadjuvant chemoradiotherapy for locally advanced rectal cancer (LARC). Methods Eighty-three consecutive patients undergoing neoadjuvant chemoradiotherapy and total mesorectal excision for LARC were retrospectively included. According to pathological results, patients were categorized into complete or incomplete response groups. Differences in ultrasonic parameters, pathological results, and clinical data between groups were evaluated. The cutoff point for a complete response as determined by quantitative analysis of CEUS was assessed using a receiver operating characteristic curve; additionally, overall survival (OS) and progression-free survival (PFS) were analyzed. Results Of the 83 patients, 12 (14.5%) achieved a complete response and 71 (85.5%) did not. There were significant between-group differences in carcinoembryonic antigen (CEA) levels, differentiation degree, proportion of tumor occupying the lumen, anterior-posterior and superior-inferior diameters of the lesion, and intensity of enhancement (P<0.05). CEUS quantitative analysis showed significant between-group differences in peak intensity (PI) and area under the curve (AUC) values (P<0.05). The OS and PFS of patients with high PI, high AUC value, and poorly differentiated cancer were significantly worse than those with low PI, low AUC values, and moderately to highly differentiated cancer (P<0.05). High CEA levels (hazard ratio: 1.02, 95% confidence interval: 1.01-1.04; P=0.002) and low differentiation (2.72, 1.12-6.62; P=0.028) were independent risk factors for PFS and OS. Conclusions CEUS can predict the response to neoadjuvant treatment in patients with LARC. CEUS quantitative analysis is helpful for clinical prognosis.
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Affiliation(s)
- Gouyang Bai
- Department of Ultrasound, Tang Du Hospital, Xi’an, China
| | - Congying Wang
- Department of Clinical Laboratory, Tang Du Hospital, Xi’an, China
| | - Yi Sun
- Department of Ultrasound, Tang Du Hospital, Xi’an, China
| | - Jinghua Li
- Department of Ultrasound, Tang Du Hospital, Xi’an, China
| | - Xiangzhou Shi
- Department of Ultrasound, Tang Du Hospital, Xi’an, China
| | - Wei Zhang
- Department of Pathology, Tang Du Hospital, Xi’an, China
| | - Yilin Yang
- Department of Ultrasound, Tang Du Hospital, Xi’an, China
| | - Ruijing Yang
- Department of Ultrasound, Tang Du Hospital, Xi’an, China
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Han Q, Lu Y, Wang D, Li X, Ruan Z, Mei N, Ji X, Geng D, Yin B. Glioblastomas with and without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity present morphological and microstructural differences on conventional MR images. Eur Radiol 2023; 33:9139-9151. [PMID: 37495706 DOI: 10.1007/s00330-023-09924-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 05/04/2023] [Accepted: 05/14/2023] [Indexed: 07/28/2023]
Abstract
OBJECTIVES Glioblastoma (GB) without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity is atypical and its characteristics are barely known. The aim of this study was to explore the differences in pathological and MRI-based intrinsic features (including morphologic and first-order features) between GBs with peritumoral FLAIR hyperintensity (PFH-bearing GBs) and GBs without peritumoral FLAIR hyperintensity (PFH-free GBs). METHODS In total, 155 patients with pathologically diagnosed GBs were retrospectively collected, which included 110 PFH-bearing GBs and 45 PFH-free GBs. The pathological and imaging data were collected. The Visually AcceSAble Rembrandt Images (VASARI) features were carefully evaluated. The first-order radiomics features from the tumor region were extracted from FLAIR, apparent diffusion coefficient (ADC), and T1CE (T1-contrast enhanced) images. All parameters were compared between the two groups of GBs. RESULTS The pathological data showed more alpha thalassemia/mental retardation syndrome X-linked (ATRX)-loss in PFH-free GBs compared to PFH-bearing ones (p < 0.001). Based on VASARI evaluation, PFH-free GBs had larger intra-tumoral enhancing proportion and smaller necrotic proportion (both, p < 0.001), more common non-enhancing tumor (p < 0.001), mild/minimal enhancement (p = 0.003), expansive T1/FLAIR ratio (p < 0.001) and solid enhancement (p = 0.009), and less pial invasion (p = 0.010). Moreover, multiple ADC- and T1CE-based first-order radiomics features demonstrated differences, especially the lower intensity heterogeneity in PFH-free GBs (for all, adjusted p < 0.05). CONCLUSIONS Compared to PFH-bearing GBs, PFH-free ones demonstrated less immature neovascularization and lower intra-tumoral heterogeneity, which would be helpful in clinical treatment stratification. CLINICAL RELEVANCE STATEMENT Glioblastomas without peritumoral FLAIR hyperintensity show less immature neovascularization and lower heterogeneity leading to potential higher treatment benefits due to less drug resistance and treatment failure. KEY POINTS • The study explored the differences between glioblastomas with and without peritumoral FLAIR hyperintensity. • Glioblastomas without peritumoral FLAIR hyperintensity showed less necrosis and contrast enhancement and lower intensity heterogeneity. • Glioblastomas without peritumoral FLAIR hyperintensity had less immature neovascularization and lower tumor heterogeneity.
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Affiliation(s)
- Qiuyue Han
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Yiping Lu
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Dongdong Wang
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Xuanxuan Li
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Zhuoying Ruan
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Nan Mei
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Xiong Ji
- Department of Pathology, Huashan Hospital, Fudan University, Shanghai, China
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China.
- Center for Shanghai Intelligent Imaging for Critical Brain Diseases Engineering and Technology Research, Shanghai, China.
| | - Bo Yin
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China.
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Huang H, Zhou M, Gong T, Wang Y. Feasibility of high-resolution readout-segmented echo-planar imaging with simultaneous multislice imaging in assessing rectal cancer. Abdom Radiol (NY) 2023; 48:2258-2269. [PMID: 37142823 DOI: 10.1007/s00261-023-03937-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 05/06/2023]
Abstract
PURPOSE To investigate the feasibility of high-resolution readout-segmented echo-planar imaging (rs-EPI) with simultaneous multislice (SMS) imaging to predict well-differentiated rectal cancer.Kindly check and confirm whether the Author Name 'Hongyun Huang ' is correctly identified.confirm METHODS: A total of eighty-three patients with nonmucinous rectal adenocarcinoma received both prototype SMS high-spatial-resolution and conventional rs-EPI sequences. Image quality was subjectively assessed by two experienced radiologists using a 4-point Likert scale (1 = poor, 4 = excellent). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and apparent diffusion coefficient (ADC) of the lesion were measured by two experienced radiologists in the objective assessment. Paired t tests or Mann‒Whitney U tests were used to compare the two groups. The areas under the receiver operating characteristic (ROC) curves (AUCs) were used to determine the predictive value of the ADCs in discriminating well-differentiated rectal cancer in the two groups. A two-sided p value < 0.05 represented statistical significance.Please check and confirm if the authors and affiliation details have been correctly identified. Amend if necessary.confirm RESULTS: In the subjective assessment, high-resolution rs-EPI had better image quality than conventional rs-EPI (p < 0.001). High-resolution rs-EPI also had a significantly higher SNR and CNR (p < 0.001). The T stage of rectal cancer was inversely correlated with the ADCs measured on high-resolution rs-EPI (r = -0.622, p < 0.001) and rs-EPI (r = -0.567, p < 0.001). The AUC of high-resolution rs-EPI in predicting well-differentiated rectal cancer was 0.768. CONCLUSION High-resolution rs-EPI with SMS imaging provided significantly higher image quality, SNRs, and CNRs and more stable ADC measurements than conventional rs-EPI. Additionally, the pretreatment ADC on high-resolution rs-EPI could discriminate well-differentiated rectal cancer.
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Affiliation(s)
- Hongyun Huang
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No. 32, West Second Section of First Ring Road, Qingyang District, Chengdu, 610072, People's Republic of China
| | - Mi Zhou
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No. 32, West Second Section of First Ring Road, Qingyang District, Chengdu, 610072, People's Republic of China
| | - Tong Gong
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No. 32, West Second Section of First Ring Road, Qingyang District, Chengdu, 610072, People's Republic of China
| | - Yuting Wang
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No. 32, West Second Section of First Ring Road, Qingyang District, Chengdu, 610072, People's Republic of China.
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Di Costanzo G, Ascione R, Ponsiglione A, Tucci AG, Dell’Aversana S, Iasiello F, Cavaglià E. Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:406-421. [PMID: 37455833 PMCID: PMC10344900 DOI: 10.37349/etat.2023.00142] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 03/01/2023] [Indexed: 07/18/2023] Open
Abstract
Rectal cancer (RC) is one of the most common tumours worldwide in both males and females, with significant morbidity and mortality rates, and it accounts for approximately one-third of colorectal cancers (CRCs). Magnetic resonance imaging (MRI) has been demonstrated to be accurate in evaluating the tumour location and stage, mucin content, invasion depth, lymph node (LN) metastasis, extramural vascular invasion (EMVI), and involvement of the mesorectal fascia (MRF). However, these features alone remain insufficient to precisely guide treatment decisions. Therefore, new imaging biomarkers are necessary to define tumour characteristics for staging and restaging patients with RC. During the last decades, RC evaluation via MRI-based radiomics and artificial intelligence (AI) tools has been a research hotspot. The aim of this review was to summarise the achievement of MRI-based radiomics and AI for the evaluation of staging, response to therapy, genotyping, prediction of high-risk factors, and prognosis in the field of RC. Moreover, future challenges and limitations of these tools that need to be solved to favour the transition from academic research to the clinical setting will be discussed.
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Affiliation(s)
- Giuseppe Di Costanzo
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Raffaele Ascione
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy
| | - Anna Giacoma Tucci
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Serena Dell’Aversana
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Francesca Iasiello
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Enrico Cavaglià
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
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Effectiveness of simultaneous multislice accelerated readout-segmented echo planar imaging for the assessment of rectal cancer. Eur J Radiol 2023; 159:110649. [PMID: 36563564 DOI: 10.1016/j.ejrad.2022.110649] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 11/17/2022] [Accepted: 12/10/2022] [Indexed: 12/15/2022]
Abstract
PURPOSE To investigate the effectiveness of simultaneous multislice (SMS) accelerated readout-segmented echo planar imaging (RESOLVE) DWI for assessing rectal cancer in the clinic. METHOD Sixty consecutive histologically proven rectal cancer patients were enrolled. They all received MRI examinations, including both SMS-RESOLVE and RESOLVE sequences. Two readers visually assessed the overall image quality, distinction of anatomical structures, lesion conspicuity, and artifacts of two sequences by using a qualitative 4-point Likert scale. The quantitative ADC value, lesion contrast, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and temporal SNR (tSNR) were independently calculated in rectal cancer on the largest slice of the tumor. RESULTS The scan time was shortened from 3 min and 50 s to 1 min and 47 s. The interobserver agreement of visual and quantitative assessments between the two readers was good overall. There were no differences in overall image quality, lesion conspicuity or artifact scores between the two sequences in both readers (all p > 0.05). The lesion contrast (p = 0.013) was significantly higher in SMS-RESOLVE, and the CNR was similar in the two DWIs. The scores of distinctions of anatomical structures in SMS-RESOLVE were lower (all p < 0.05) in both readers. The SNR of SMS-RESOLVE was lower than that of RESOLVE (p = 0.004), and the tSNR of SMS-RESOLVE was significantly higher (p < 0.001). The ADC value of the tumor was lower in SMS-RESOLVE (p = 0.001), but the ADC values of the normal rectal wall showed no difference between the two DWIs. CONCLUSION SMS-RESOLVE allowed a substantial reduction in acquisition time while maintaining overall image quality and lesion conspicuity in rectal cancer. It also had a higher contrast of the lesion and a higher temporal SNR.
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Tabari A, Chan SM, Omar OMF, Iqbal SI, Gee MS, Daye D. Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers. Cancers (Basel) 2022; 15:cancers15010063. [PMID: 36612061 PMCID: PMC9817513 DOI: 10.3390/cancers15010063] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.
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Affiliation(s)
- Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Correspondence:
| | - Shin Mei Chan
- Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06510, USA
| | - Omar Mustafa Fathy Omar
- Center for Vascular Biology, University of Connecticut Health Center, Farmington, CT 06030, USA
| | - Shams I. Iqbal
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Michael S. Gee
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
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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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Recent Advances in Functional MRI to Predict Treatment Response for Locally Advanced Rectal Cancer. CURRENT COLORECTAL CANCER REPORTS 2021. [DOI: 10.1007/s11888-021-00470-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Li M, Xu X, Qian P, Jiang H, Jiang J, Sun J, Lu Z. Texture Analysis in the Assessment of Rectal Cancer: Comparison of T2WI and Diffusion-Weighted Imaging. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:9976440. [PMID: 34567237 PMCID: PMC8457990 DOI: 10.1155/2021/9976440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 07/05/2021] [Accepted: 08/27/2021] [Indexed: 11/17/2022]
Abstract
Texture analysis (TA) techniques derived from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) maps of rectal cancer can both achieve good diagnosis performance. This study was to compare TA from T2WI and ADC maps between different pathological T and N stages to confirm which TA analysis is better in diagnosis performance. 146 patients were enrolled in this study. Tumor TA was performed on every patient's T2WI and ADC maps, respectively; then, skewness, kurtosis, uniformity, entropy, energy, inertia, and correlation were calculated. Our results demonstrated that those significant different parameters derived from T2WI had better diagnostic performance than those from ADC maps in differentiating pT3b-4 and pN1-2 stage tumors. In particular, the energy derived from T2WI was an optimal parameter for diagnostic efficiency. High-resolution T2WI plays a key point in the local stage of rectal cancer; thus, TA derived from T2WI may be a more useful tool to aid radiologists and surgeons in selecting treatment.
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Affiliation(s)
- Ming Li
- Department of General Surgery, Changshu No. 1 People's Hospital, Changshu, 215500 Jiangsu Province, China
| | - Xiaodan Xu
- Department of Gastroenterology, Changshu No. 1 People's Hospital, Changshu, 215500 Jiangsu Province, China
| | - Pengjiang Qian
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122 Jiangsu Province, China
| | - Heng Jiang
- Department of Radiology, Changshu No. 1 People's Hospital, Changshu, 215500 Jiangsu Province, China
| | - Jianlong Jiang
- Department of General Surgery, Changshu No. 1 People's Hospital, Changshu, 215500 Jiangsu Province, China
| | - Jinbing Sun
- Department of General Surgery, Changshu No. 1 People's Hospital, Changshu, 215500 Jiangsu Province, China
| | - Zhihua Lu
- Department of Radiology, Changshu No. 1 People's Hospital, Changshu, 215500 Jiangsu Province, China
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12
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Stanzione A, Verde F, Romeo V, Boccadifuoco F, Mainenti PP, Maurea S. Radiomics and machine learning applications in rectal cancer: Current update and future perspectives. World J Gastroenterol 2021; 27:5306-5321. [PMID: 34539134 PMCID: PMC8409167 DOI: 10.3748/wjg.v27.i32.5306] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/13/2021] [Accepted: 07/22/2021] [Indexed: 02/06/2023] Open
Abstract
The high incidence of rectal cancer in both sexes makes it one of the most common tumors, with significant morbidity and mortality rates. To define the best treatment option and optimize patient outcome, several rectal cancer biological variables must be evaluated. Currently, medical imaging plays a crucial role in the characterization of this disease, and it often requires a multimodal approach. Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors. Computed tomography is widely adopted for the detection of distant metastases. However, conventional imaging has recognized limitations, and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation. There is a growing interest in artificial intelligence applications in medicine, and imaging is by no means an exception. The introduction of radiomics, which allows the extraction of quantitative features that reflect tumor heterogeneity, allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers. To manage such a huge amount of data, the use of machine learning algorithms has been proposed. Indeed, without prior explicit programming, they can be employed to build prediction models to support clinical decision making. In this review, current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented, with an imaging modality-based approach and a keen eye on unsolved issues. The results are promising, but the road ahead for translation in clinical practice is rather long.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Francesco Verde
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Francesca Boccadifuoco
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging, National Council of Research, Napoli 80131, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
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Xu Q, Xu Y, Sun H, Jiang T, Xie S, Ooi BY, Ding Y. MRI Evaluation of Complete Response of Locally Advanced Rectal Cancer After Neoadjuvant Therapy: Current Status and Future Trends. Cancer Manag Res 2021; 13:4317-4328. [PMID: 34103987 PMCID: PMC8179813 DOI: 10.2147/cmar.s309252] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 05/08/2021] [Indexed: 12/29/2022] Open
Abstract
Complete tumor response can be achieved in a certain proportion of patients with locally advanced rectal cancer, who achieve maximal response to neoadjuvant therapy (NAT). For these patients, a watch-and-wait (WW) or nonsurgical strategy has been proposed and is becoming widely practiced in order to avoid unnecessary surgical complications. Therefore, a non-invasive, reliable diagnostic tool for accurately evaluating complete tumor response is needed. Magnetic resonance imaging (MRI) plays a crucial role in both primary staging and restaging tumor response to NAT in rectal cancer without relying on resected specimen. In recent years, numerous efforts have been made to research the value of MRI in predicting and evaluating complete response in rectal cancer. Current MRI evaluation is mainly based on morphological and functional images. Morphologic MRI yields high soft tissue resolution, multiplanar images, and provides detailed depictions of rectal cancer and its surrounding structures. Functional MRI may help to distinguish residual tumor from fibrosis, therefore improving the diagnostic performance of morphologic MRI in identifying complete tumor response. Both morphologic and functional MRI have several promising parameters that may help accurately evaluate and/or predict complete response of rectal cancer. However, these parameters still have limitations and the results remain inconsistent. Recent development of new techniques, such as textural analysis, radiomics analysis and deep learning, demonstrate great potential based on MRI-derived parameters. This article aimed to review and help better understand the strengths, limitations, and future trends of these MRI-derived methods in evaluating complete response in rectal cancer.
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Affiliation(s)
- Qiaoyu Xu
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Yanyan Xu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, People’s Republic of China
| | - Hongliang Sun
- Department of Radiology, China-Japan Friendship Hospital, Beijing, People’s Republic of China
| | - Tao Jiang
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Sheng Xie
- Department of Radiology, China-Japan Friendship Hospital, Beijing, People’s Republic of China
| | - Bee Yen Ooi
- Department of Radiology, Hospital Seberang Jaya, Penang, Malaysia
| | - Yi Ding
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China
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Feng M, Yin Q, Ren J, Wu F, Lan M, Wang H, Wang M, Li L, Chen X, Lang J. Dynamic Three-Dimensional ADC Changes of Parotid Glands During Radiotherapy Predict the Salivary Secretary Function in Patients With Head and Neck Squamous Carcinoma. Front Oncol 2021; 11:651537. [PMID: 33928037 PMCID: PMC8076545 DOI: 10.3389/fonc.2021.651537] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 03/24/2021] [Indexed: 11/13/2022] Open
Abstract
Objective To investigate the changes of three-dimensional apparent diffusion coefficient (3D-ADC) of bilateral parotid glands during radiotherapy for head and neck squamous cell carcinoma (HNSCC) patients and explore the correlations with the radiation dose, volume reduction of parotid gland and the salivary secretary function. Materials and Methods 60 HNSCC were retrospectively collected in Sichuan cancer hospital. The patients were all received diffusion-weighted imaging (DWI) scan at pre-radiation, the 15th radiation, the 25th radiation and completion of radiation. Dynamic 3D-ADC were measured in different lobes of parotid glands (P1: deep lobe of ipsilateral; P2: superficial lobe of ipsilateral; P3: deep lobe of contralateral; P4: superficial lobe of contralateral), and the 3D-ADC of spinal cord were also recorded. Chewing stimulates test, radionuclide scan and RTOG criteria were recorded to evaluate the salivary secretary function. Pearson analysis was used to assess the correlation between 3D-ADC value, radiation dose, volume change, and salivary secretary function. Results The mean 3D-ADC of parotid glands increased. It began to change at the 15th radiation and the mostly increased in P1. However, there was no change for the maximum and minimum 3D-ADC. The 3D-ADC values of spinal cord changes were almost invisible (ratio ≤ 0.03 ± 0.01). The mean 3D-ADC was negatively correlated with the salivary secretary function (r=-0.72) and volume reduction of different lobes of parotid glands (r1=-0.64; r2=-0.61; r3=-0.57; r4=-0.49), but it was positively correlated with the delivered dose (r1 = 0.73; r2 = 0.69; r3 = 0.65; r4 = 0.78). Conclusion Dynamic 3D-ADC changes might be a new and early indicator to predict and evaluate the secretary function of parotid glands during radiotherapy.
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Affiliation(s)
- Mei Feng
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.,Department of Medical Oncology, Sichuan The Third People's Hospital, Chengdu, China
| | - Qingping Yin
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.,Department of Radiation Oncology, School of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Fei Wu
- Department of Oncology, People's Hospital of Deyang City, Deyang, China
| | - Mei Lan
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - He Wang
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Wang
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Lu Li
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaojian Chen
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Jinyi Lang
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment. Eur J Nucl Med Mol Imaging 2020; 48:1785-1794. [PMID: 33326049 PMCID: PMC8113210 DOI: 10.1007/s00259-020-05142-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 11/29/2020] [Indexed: 02/08/2023]
Abstract
Purpose Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients with gastrointestinal tumors. Methods A systematic review was conducted, according to PRISMA guidelines. The protocol was prospectively registered (PROSPERO: CRD42019128408). PubMed, Embase, and Cochrane databases were searched. Original studies reporting on the value of radiomics in predicting response to treatment in patients with a gastrointestinal tumor were included. A narrative synthesis of results was conducted. Results were stratified by tumor type. Quality assessment of included studies was performed, according to the radiomics quality score. Results The comprehensive literature search identified 1360 unique studies, of which 60 articles were included for analysis. In 37 studies, radiomics models and individual radiomic features showed good predictive performance for response to treatment (area under the curve or accuracy > 0.75). Various strategies to construct predictive models were used. Internal validation of predictive models was often performed, while the majority of studies lacked external validation. None of the studies reported predictive models implemented in clinical practice. Conclusion Radiomics is increasingly used to predict response to treatment in patients suffering from gastrointestinal cancer. This review demonstrates its great potential to help predict response to treatment and improve patient selection and early adjustment of treatment strategy in a non-invasive manner. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-020-05142-w.
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Petresc B, Lebovici A, Caraiani C, Feier DS, Graur F, Buruian MM. Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study. Cancers (Basel) 2020; 12:cancers12071894. [PMID: 32674345 PMCID: PMC7409205 DOI: 10.3390/cancers12071894] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 06/30/2020] [Accepted: 07/09/2020] [Indexed: 12/24/2022] Open
Abstract
Locally advanced rectal cancer (LARC) response to neoadjuvant chemoradiotherapy (nCRT) is very heterogeneous and up to 30% of patients are considered non-responders, presenting no tumor regression after nCRT. This study aimed to determine the ability of pre-treatment T2-weighted based radiomics features to predict LARC non-responders. A total of 67 LARC patients who underwent a pre-treatment MRI followed by nCRT and total mesorectal excision were assigned into training (n = 44) and validation (n = 23) groups. In both datasets, the patients were categorized according to the Ryan tumor regression grade (TRG) system into non-responders (TRG = 3) and responders (TRG 1 and 2). We extracted 960 radiomic features/patient from pre-treatment T2-weighted images. After a three-step feature selection process, including LASSO regression analysis, we built a radiomics score with seven radiomics features. This score was significantly higher among non-responders in both training and validation sets (p < 0.001 and p = 0.03) and it showed good predictive performance for LARC non-response, achieving an area under the curve (AUC) = 0.94 (95% CI: 0.82–0.99) in the training set and AUC = 0.80 (95% CI: 0.58–0.94) in the validation group. The multivariate analysis identified the radiomics score as an independent predictor for the tumor non-response (OR = 6.52, 95% CI: 1.87–22.72). Our results indicate that MRI radiomics features could be considered as potential imaging biomarkers for early prediction of LARC non-response to neoadjuvant treatment.
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Affiliation(s)
- Bianca Petresc
- Department of Radiology, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania; (B.P.); (M.M.B.)
- Department of Radiology, Emergency Clinical County Hospital Cluj-Napoca, 400006 Cluj-Napoca, Romania;
| | - Andrei Lebovici
- Department of Radiology, Emergency Clinical County Hospital Cluj-Napoca, 400006 Cluj-Napoca, Romania;
- Department of Radiology, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Correspondence: (A.L.); (C.C.)
| | - Cosmin Caraiani
- Department of Medical Imaging, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Department of Radiology, Regional Institute of Gastroenterology and Hepatology “Prof. Dr. Octavian Fodor”, 400158 Cluj-Napoca, Romania
- Correspondence: (A.L.); (C.C.)
| | - Diana Sorina Feier
- Department of Radiology, Emergency Clinical County Hospital Cluj-Napoca, 400006 Cluj-Napoca, Romania;
- Department of Radiology, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Florin Graur
- Department of Surgery, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania;
- Department of Surgery, Regional Institute of Gastroenterology and Hepatology “Prof. Dr. Octavian Fodor”, 400158 Cluj-Napoca, Romania
| | - Mircea Marian Buruian
- Department of Radiology, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania; (B.P.); (M.M.B.)
- Department of Radiology, Emergency Clinical County Hospital Târgu Mureș, 540136 Târgu Mureș, Romania
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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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18
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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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