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Zhang C, Ma L, Zhao Y, Zhang Z, Zhang Q, Li X, Qin J, Ren Y, Hu Z, Zhao Q, Shen W, Cheng Y. Estimating pathological prognostic factors in epithelial ovarian cancers using apparent diffusion coefficients of functional tumor volume. Eur J Radiol 2024; 176:111514. [PMID: 38776804 DOI: 10.1016/j.ejrad.2024.111514] [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: 01/23/2024] [Revised: 04/26/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE To assess the utility of apparent diffusion coefficients (ADCs) of whole tumor volume (WTV) and functional tumor volume (FTV) in determining the pathologicalprognostic factors in epithelial ovarian cancers (EOCs). METHODS A total of 155 consecutive patients who were diagnosed with EOC between January 2017 and August 2022 and underwent both conventional magnetic resonance imaging and diffusion-weighted imaging were assessed in this study. The maximum, minimum, and mean ADC values of the whole tumor (ADCwmax, ADCwmin, and ADCwmean, respectively) and functional tumor (ADCfmax, ADCfmin, and ADCfmean, respectively) as well as the WTV and FTV were derived from the ADC maps. The univariate and multivariate logistic regression analyses and receiver operating characteristic curve (ROC) analysis were used to assess the correlation between these ADC values and the pathological prognostic factors, namely subtypes, lymph node metastasis (LNM), Ki-67 index, and p53 expression. RESULTS The ADCfmean value was significantly lower in type II EOC, LNM-positive, and high-Ki-67 index groups compared to the type I EOC, LNM-negative, and low-Ki-67 index groups (p ≤ 0.001). Similarly, the ADCwmean and ADCfmean values were lower in the mutant-p53 group compared to the wild-type-p53 group (p ≤ 0.001). Additionally, the ADCfmean showed the highest area under the ROC curve (AUC) for evaluating type II EOC (0.725), LNM-positive (0.782), and high-Ki-67 index (0.688) samples among the given ROC curves, while both ADCwmean and ADCfmean showed high AUCs for assessing p53 expression (0.694 and 0.678, respectively). CONCLUSION The FTV-derived ADC values, especially ADCfmean, can be used to assess preoperative prognostic factors in EOCs.
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Affiliation(s)
- Cheng Zhang
- The First Central Clinical School, Tianjin Medical University, Tianjin, China.
| | - Luyang Ma
- The First Central Clinical School, Tianjin Medical University, Tianjin, China.
| | - Yujiao Zhao
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
| | - Zhijing Zhang
- School of Medicine, Nankai University, Tianjin, China.
| | - Qi Zhang
- The First Central Clinical School, Tianjin Medical University, Tianjin, China.
| | - Xiaotian Li
- School of Medicine, Nankai University, Tianjin, China.
| | - Jiaming Qin
- School of Medicine, Nankai University, Tianjin, China.
| | - Yan Ren
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
| | - Zhandong Hu
- Department of Pathology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
| | - Qian Zhao
- Department of Gynecology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
| | - Wen Shen
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
| | - Yue Cheng
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
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Liu L, Zhao L, Jing Y, Li D, Linghu H, Wang H, Zhou L, Fang Y, Li Y. Exploring a multiparameter MRI-based radiomics approach to predict tumor proliferation status of serous ovarian carcinoma. Insights Imaging 2024; 15:74. [PMID: 38499907 PMCID: PMC10948697 DOI: 10.1186/s13244-024-01634-7] [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: 06/23/2023] [Accepted: 01/27/2024] [Indexed: 03/20/2024] Open
Abstract
OBJECTIVES To develop a multiparameter magnetic resonance imaging (MRI)-based radiomics approach that can accurately predict the tumor cell proliferation status of serous ovarian carcinoma (SOC). MATERIALS AND METHODS A total of 134 patients with SOC who met the inclusion and exclusion criteria were retrospectively screened from institution A, spanning from January 2016 to March 2022. Additionally, an external validation set comprising 42 SOC patients from institution B was also included. The region of interest was determined by drawing each ovarian mass boundaries manually slice-by-slice on T2-weighted imaging fat-suppressed fast spin-echo (T2FSE) and T1 with contrast enhancement (T1CE) images using ITK-SNAP software. The handcrafted radiomic features were extracted, and then were selected using variance threshold algorithm, SelectKBest algorithm, and least absolute shrinkage and selection operator. The optimal radiomic scores and the clinical/radiological independent predictors were integrated as a combined model. RESULTS Compared with the area under the curve (AUC) values of each radiomic signature of T2FSE and T1CE, respectively, the AUC value of the radiomic signature (T1CE-T2FSE) was the highest in the training set (0.999 vs. 0.965 and 0.860). The homogeneous solid component of the ovarian mass was considered the only independent predictor of tumor cell proliferation status among the clinical/radiological variables. The AUC of the radiomic-radiological model was 0.999. CONCLUSIONS The radiomic-radiological model combining radiomic scores and the homogeneous solid component of the ovarian mass can accurately predict tumor cell proliferation status of SOC which has high repeatability and may enable more targeted and effective treatment strategies. CRITICAL RELEVANCE STATEMENT The proposed radiomic-radiological model combining radiomic scores and the homogeneous solid component of the ovarian mass can predict tumor cell proliferation status of SOC which has high repeatability and may guide individualized treatment programs. KEY POINTS • The radiomic-radiological nomogram may guide individualized treatment programs of SOC. • This radiomic-radiological nomogram showed a favorable prediction ability. • Homogeneous slightly higher signal intensity on T2FSE is vital for Ki-67.
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Affiliation(s)
- Li Liu
- Department of Radiology, The People's Hospital of Yubei District of Chongqing City, No. 23 ZhongyangGongyuanBei Road, Yubei District, Chongqing, 401120, China
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, Yuanjiagang, China
| | - Ling Zhao
- Department of Radiology, The People's Hospital of Yubei District of Chongqing City, No. 23 ZhongyangGongyuanBei Road, Yubei District, Chongqing, 401120, China
| | - Yang Jing
- Huiying Medical Technology Co., Ltd, Dongsheng Science and Technology Park, Room A206, B2Haidian District, Beijing, 100192, China
| | - Dan Li
- Department of Pathology, Chongqing Medical University, No.1 Medical College Road, Yuzhong District, Chongqing, 400016, China
| | - Hua Linghu
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road Yuzhong District, Chongqing, 400016, Yuanjiagang, China
| | - Haiyan Wang
- Department of Radiology, The People's Hospital of Yubei District of Chongqing City, No. 23 ZhongyangGongyuanBei Road, Yubei District, Chongqing, 401120, China
| | - Linyi Zhou
- Department of Radiology, Army Medical Center, Daping Hospital, Army Medical University, 10# Changjiangzhilu, Chongqing, 40024, China
| | - Yuan Fang
- Department of Radiology, The People's Hospital of Yubei District of Chongqing City, No. 23 ZhongyangGongyuanBei Road, Yubei District, Chongqing, 401120, China.
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, Yuanjiagang, China.
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Grabowska-Derlatka L, Derlatka P, Hałaburda-Rola M. Characterization of Primary Mucinous Ovarian Cancer by Diffusion-Weighted and Dynamic Contrast Enhancement MRI in Comparison with Serous Ovarian Cancer. Cancers (Basel) 2023; 15:cancers15051453. [PMID: 36900244 PMCID: PMC10000545 DOI: 10.3390/cancers15051453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/17/2023] [Accepted: 02/23/2023] [Indexed: 03/02/2023] Open
Abstract
(1) Background. The purpose of this study is to evaluate the diagnostic accuracy of a quantitative analysis of diffusion-weighted imaging (DWI) and dynamic contrast enhanced (DCE) MRI of mucinous ovarian cancer (MOC). It also aims to differentiate between low grade serous carcinoma (LGSC), high-grade serous carcinoma (HGSC) and MOC in primary tumors. (2) Materials and Methods. Sixty-six patients with histologically confirmed primary epithelial ovarian cancer (EOC) were included in the study. Patients were divided into three groups: MOC, LGSC and HGSC. In the preoperative DWI and DCE MRI, selected parameters were measured: apparent diffusion coefficients (ADC), time to peak (TTP), and perfusion maximum enhancement (Perf. Max. En.). ROI comprised a small circle placed in the solid part of the primary tumor. The Shapiro-Wilk test was used to test whether the variable had a normal distribution. The Kruskal-Wallis ANOVA test was used to determine the p-value needed to compare the median values of interval variables. (3) Results. The highest median ADC values were found in MOC, followed by LGSC, and the lowest in HGSC. All differences were statistically significant (p < 0.000001). This was also confirmed by the ROC curve analysis for MOC and HGSC, showing that ADC had excellent diagnostic accuracy in differentiating between MOC and HGSC (p < 0.001). In the type I EOCs, i.e., MOC and LGSC, ADC has less differential value (p = 0.032), and TTP can be considered the most valuable parameter for diagnostic accuracy (p < 0.001). (4) Conclusions. DWI and DCE appear to be very good diagnostic tools in differentiating between serous carcinomas (LGSC, HGSC) and MOC. Significant differences in median ADC values between MOC and LGSC compared with those between MOC and HGSC indicate the usefulness of DWI in differentiating between less and more aggressive types of EOC, not only among the most common serous carcinomas. ROC curve analysis showed that ADC had excellent diagnostic accuracy in differentiating between MOC and HGSC. In contrast, TTP showed the greatest value for differentiating between LGSC and MOC.
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Affiliation(s)
- Laretta Grabowska-Derlatka
- Second Department of Clinical Radiology, Medical University of Warsaw, Banacha 1a St., 02-097 Warsaw, Poland
| | - Pawel Derlatka
- Second Department Obstetrics and Gynecology, Medical University of Warsaw, Karowa 2 St., 00-315 Warsaw, Poland
- Correspondence: ; Tel.: +48-22-5966-512
| | - Marta Hałaburda-Rola
- Second Department of Clinical Radiology, Medical University of Warsaw, Banacha 1a St., 02-097 Warsaw, Poland
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Lu J, Zhao S, Ma F, Li H, Li Y, Qiang J. Whole-tumor ADC histogram analysis for differentiating endometriosis-related tumors: seromucinous borderline tumor, clear cell carcinoma and endometrioid carcinoma. Abdom Radiol (NY) 2023; 48:724-732. [PMID: 36401131 DOI: 10.1007/s00261-022-03742-8] [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: 09/26/2022] [Revised: 11/03/2022] [Accepted: 11/04/2022] [Indexed: 11/21/2022]
Abstract
PURPOSE To investigate the feasibility of whole-tumor apparent diffusion coefficient (ADC) histogram analysis for improving the differentiation of endometriosis-related tumors: seromucinous borderline tumor (SMBT), clear cell carcinoma (CCC) and endometrioid carcinoma (EC). METHODS Clinical features, solid component ADC (ADCSC) and whole-tumor ADC histogram-derived parameters (volume, the ADCmean, 10th, 50th and 90th percentile ADCs, inhomogeneity, skewness, kurtosis and entropy) were compared among 22 SMBTs, 42 CCCs and 21 ECs. Statistical analyses were performed using chi-square test, one-way ANOVA or Kruskal-Wallis test, and receiver operating characteristic curves. RESULTS A significantly higher ADCSC and smaller volume were associated with SMBT than with CCC/EC. The ADCmean was significantly higher in CCC than in EC. The 10th percentile ADC was significantly lower in EC than in SMBT/CCC. The 50th and 90th percentile ADCs were significantly higher in CCC than in SMBT/EC. For differentiating SMBT from CCC, AUCs of the ADCSC, volume, and 50th and 90th percentile ADCs were 0.97, 0.86, 0.72 and 0.81, respectively. For differentiating SMBT from EC, AUCs of the ADCSC, volume and 10th percentile ADC were 0.97, 0.71 and 0.72, respectively. For differentiating CCC from EC, AUCs of the ADCmean and 10th, 50th and 90th percentile ADCs were 0.79, 0.72, 0.81 and 0.85, respectively. CONCLUSION Whole-tumor ADC histogram analysis was valuable for differentiating endometriosis-related tumors, and the 90th percentile ADC was optimal in differentiating CCC from EC.
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Affiliation(s)
- Jing Lu
- Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, People's Republic of China
| | - Shuhui Zhao
- Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, People's Republic of China.,Department of Radiology, Xinhua Hospital, Medical College of Shanghai Jiao Tong University, Shanghai, 200092, People's Republic of China
| | - Fenghua Ma
- Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, People's Republic of China.,Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, 419 Fangxie Road, Shanghai, 200011, People's Republic of China
| | - Haiming Li
- Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, People's Republic of China.,Department of Radiology, Shanghai Cancer Center, Fudan University, 270 Dongan Road, Shanghai, 200032, People's Republic of China
| | - Yong'ai Li
- Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, People's Republic of China.
| | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, People's Republic of China.
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Xu Y, Luo HJ, Ren J, Guo LM, Niu J, Song X. Diffusion-weighted imaging-based radiomics in epithelial ovarian tumors: Assessment of histologic subtype. Front Oncol 2022; 12:978123. [PMID: 36544703 PMCID: PMC9762272 DOI: 10.3389/fonc.2022.978123] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 11/16/2022] [Indexed: 12/12/2022] Open
Abstract
Background Epithelial ovarian tumors (EOTs) are a group of heterogeneous neoplasms. It is importance to preoperatively differentiate the histologic subtypes of EOTs. Our study aims to investigate the potential of radiomics signatures based on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps for categorizing EOTs. Methods This retrospectively enrolled 146 EOTs patients [34 with borderline EOT(BEOT), 30 with type I and 82 with type II epithelial ovarian cancer (EOC)]. A total of 390 radiomics features were extracted from DWI and ADC maps. Subsequently, the LASSO algorithm was used to reduce the feature dimensions. A radiomics signature was established using multivariable logistic regression method with 3-fold cross-validation and repeated 50 times. Patients with bilateral lesions were included in the validation cohort and a heuristic selection method was established to select the tumor with maximum probability for final consideration. A nomogram incorporating the radiomics signature and clinical characteristics was also developed. Receiver operator characteristic, decision curve analysis (DCA), and net reclassification index (NRI) were applied to compare the diagnostic performance and clinical net benefit of predictive model. Results For distinguishing BEOT from EOC, the radiomics signature and nomogram showed more favorable discrimination than the clinical model (0.915 vs. 0.852 and 0.954 vs. 0.852, respectively) in the training cohort. In classifying early-stage type I and type II EOC, the radiomics signature exhibited superior diagnostic performance over the clinical model (AUC 0.905 vs. 0.735). The diagnostic efficacy of the nomogram was the same as that of the radiomics model with NRI value of -0.1591 (P = 0.7268). DCA also showed that the radiomics model and combined model had higher net benefits than the clinical model. Conclusion Radiomics analysis based on DWI, and ADC maps serve as an effective quantitative approach to categorize EOTs.
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Affiliation(s)
- Yi Xu
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Hong-Jian Luo
- Department of Radiology, The Third Affiliated Hospital of Zunyi Medical University (The First People’s Hospital of Zunyi), Zunyi, Guizhou, China
| | | | - Li-mei Guo
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Jinliang Niu
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Xiaoli Song
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China,*Correspondence: Xiaoli Song,
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Liu R, Li R, Fang J, Deng K, Chen C, Li J, Wu Z, Zeng X. Apparent diffusion coefficient histogram analysis for differentiating solid ovarian tumors. Front Oncol 2022; 12:904323. [PMID: 35978817 PMCID: PMC9376384 DOI: 10.3389/fonc.2022.904323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 07/06/2022] [Indexed: 12/21/2022] Open
Abstract
Objective To evaluate the utility of apparent diffusion coefficient (ADC) histogram analysis to differentiate between three types of solid ovarian tumors: granulosa cell tumors (GCTs) of the ovary, ovarian fibromas, and high-grade serous ovarian carcinomas (HGSOCs). Methods The medical records of 11 patients with GCTs of the ovary (regions of interest [ROI-cs], 137), 61 patients with ovarian fibromas (ROI-cs, 161), and 14 patients with HGSOCs (ROI-cs, 113) confirmed at surgery and histology who underwent diffusion-weighted imaging were retrospectively reviewed. Histogram parameters of ADC maps (ADCmean, ADCmax, ADCmin) were estimated and compared using the Kruskal-WallisH test and Mann-Whitney U test. The area under the curve of receiver operating characteristic curves was used to assess the diagnostic performance of ADC parameters for solid ovarian tumors. Results There were significant differences in ADCmean, ADCmax and ADCmin values between GCTs of the ovary, ovarian fibromas, and HGSOCs. The cutoff ADCmean value for differentiating a GCT of the ovary from an ovarian fibroma was 0.95×10-3 mm2/s, for differentiating a GCT of the ovary from an HGSOC was 0.69×10-3 mm2/s, and for differentiating an ovarian fibroma from an HGSOC was 1.24×10-3 mm2/s. Conclusion ADCmean derived from ADC histogram analysis provided quantitative information that allowed accurate differentiation of GCTs of the ovary, ovarian fibromas, and HGSOCs before surgery.
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Affiliation(s)
- Renwei Liu
- Department of Radiology, Affiliated Longhua People’s Hospital Southern Medical University (Longhua People’s Hospital), Shenzhen, China
| | - Ruifeng Li
- Department of Radiology, Affiliated Longhua People’s Hospital Southern Medical University (Longhua People’s Hospital), Shenzhen, China
| | - Jinzhi Fang
- Department of Radiology, Affiliated Longhua People’s Hospital Southern Medical University (Longhua People’s Hospital), Shenzhen, China
| | - Kan Deng
- C&TS Clinical Science, Philips Healthcare, Guangzhou, China
| | - Cuimei Chen
- Department of Radiology, Affiliated Longhua People’s Hospital Southern Medical University (Longhua People’s Hospital), Shenzhen, China
| | - Jianhua Li
- Department of Radiology, Affiliated Longhua People’s Hospital Southern Medical University (Longhua People’s Hospital), Shenzhen, China
| | - Zhiqing Wu
- Department of Radiology, Affiliated Longhua People’s Hospital Southern Medical University (Longhua People’s Hospital), Shenzhen, China
| | - Xiaoxu Zeng
- Department of Radiology, Affiliated Longhua People’s Hospital Southern Medical University (Longhua People’s Hospital), Shenzhen, China
- *Correspondence: Xiaoxu Zeng,
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Jiménez de los Santos ME, Reyes-Pérez JA, Domínguez Osorio V, Villaseñor-Navarro Y, Moreno-Astudillo L, Vela-Sarmiento I, Sollozo-Dupont I. Whole lesion histogram analysis of apparent diffusion coefficient predicts therapy response in locally advanced rectal cancer. World J Gastroenterol 2022; 28:2609-2624. [PMID: 35949349 PMCID: PMC9254137 DOI: 10.3748/wjg.v28.i23.2609] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/25/2021] [Accepted: 04/25/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Whole-tumor apparent diffusion coefficient (ADC) histogram analysis is relevant to predicting the neoadjuvant chemoradiation therapy (nCRT) response in patients with locally advanced rectal cancer (LARC).
AIM To evaluate the performance of ADC histogram-derived parameters for predicting the outcomes of patients with LARC.
METHODS This is a single-center, retrospective study, which included 48 patients with LARC. All patients underwent a pre-treatment magnetic resonance imaging (MRI) scan for primary tumor staging and a second restaging MRI for response evaluation. The sample was distributed as follows: 18 responder patients (R) and 30 non-responders (non-R). Eight parameters derived from the whole-lesion histogram analysis (ADCmean, skewness, kurtosis, and ADC10th, 25th, 50th, 75th, 90th percentiles), as well as the ADCmean from the hot spot region of interest (ROI), were calculated for each patient before and after treatment. Then all data were compared between R and non-R using the Mann-Whitney U test. Two measures of diagnostic accuracy were applied: the receiver operating characteristic curve and the diagnostic odds ratio (DOR). We also reported intra- and interobserver variability by calculating the intraclass correlation coefficient (ICC).
RESULTS Post-nCRT kurtosis, as well as post-nCRT skewness, were significantly lower in R than in non-R (both P < 0.001, respectively). We also found that, after treatment, R had a larger loss of both kurtosis and skewness than non-R (∆%kurtosis and ∆skewness, P < 0.001). Other parameters that demonstrated changes between groups were post-nCRT ADC10th, ∆%ADC10th, ∆%ADCmean, and ROI ∆%ADCmean. However, the best diagnostic performance was achieved by ∆%kurtosis at a threshold of 11.85% (Area under the receiver operating characteristic curve [AUC] = 0.991, DOR = 376), followed by post-nCRT kurtosis = 0.78 × 10-3 mm2/s (AUC = 0.985, DOR = 375.3), ∆skewness = 0.16 (AUC = 0.885, DOR = 192.2) and post-nCRT skewness = 1.59 × 10-3 mm2/s (AUC = 0.815, DOR = 168.6). Finally, intraclass correlation coefficient analysis showed excellent intraobserver and interobserver agreement, ensuring the implementation of histogram analysis into routine clinical practice.
CONCLUSION Whole-tumor ADC histogram parameters, particularly kurtosis and skewness, are relevant biomarkers for predicting the nCRT response in LARC. Both parameters appear to be more reliable than ADCmean from one-slice ROI.
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Affiliation(s)
| | | | | | | | | | - Itzel Vela-Sarmiento
- Department of Gastrointestinal Surgery, National Cancer Institute, Mexico 14080, Mexico
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Li C, Wang H, Chen Y, Zhu C, Gao Y, Wang X, Dong J, Wu X. Nomograms of Combining MRI Multisequences Radiomics and Clinical Factors for Differentiating High-Grade From Low-Grade Serous Ovarian Carcinoma. Front Oncol 2022; 12:816982. [PMID: 35747838 PMCID: PMC9211758 DOI: 10.3389/fonc.2022.816982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 05/05/2022] [Indexed: 11/13/2022] Open
Abstract
Objective To compare the performance of clinical factors, FS-T2WI, DWI, T1WI+C based radiomics and a combined clinic-radiomics model in predicting the type of serous ovarian carcinomas (SOCs). Methods In this retrospective analysis, 138 SOC patients were confirmed by histology. Significant clinical factors (P < 0.05, and with the area under the curve (AUC) > 0.7) was retained to establish a clinical model. The radiomics model included FS-T2WI, DWI, and T1WI+C, and also, a multisequence model was established. A total of 1,316 radiomics features of each sequence were extracted; the univariate and multivariate logistic regressions, cross-validations were performed to reduce valueless features and then radiomics signatures were developed. Nomogram models using clinical factors, combined with radiomics features, were developed in the training cohort. The predictive performance was validated by receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA). A stratified analysis was conducted to compare the differences between the combined radiomics model and the clinical model in identifying low- and high-grade SOC. Results The AUC of the clinical model and multisequence radiomics model in the training and validation cohorts was 0.90 and 0.89, 0.91 and 0.86, respectively. By incorporating clinical factors and multi-radiomics signature, the AUC of the radiomic-clinical nomogram in the training and validation cohorts was 0.98 and 0.95. The model comparison results show that the AUC of the combined model is higher than that of the uncombined models (P= 0.05, 0.002). Conclusion The nomogram models of clinical factors combined with MRI multisequence radiomics signatures can help identifying low- and high-grade SOCs and a provide a more comprehensive, effective method to evaluate preoperative risk stratification for SOCs.
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Affiliation(s)
- Cuiping Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Radiology, The First Affiliated Hospital of the University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
| | - Hongfei Wang
- Department of Radiotherapy, The First Affiliated Hospital, University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
| | - Yulan Chen
- Department of Radiology, The First Affiliated Hospital of the University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
| | - Chao Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yankun Gao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xia Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jiangning Dong
- Department of Radiology, The First Affiliated Hospital of the University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
- *Correspondence: Jiangning Dong, ; Xingwang Wu,
| | - Xingwang Wu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- *Correspondence: Jiangning Dong, ; Xingwang Wu,
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Derlatka P, Grabowska-Derlatka L, Halaburda-Rola M, Szeszkowski W, Czajkowski K. The Value of Magnetic Resonance Diffusion-Weighted Imaging and Dynamic Contrast Enhancement in the Diagnosis and Prognosis of Treatment Response in Patients with Epithelial Serous Ovarian Cancer. Cancers (Basel) 2022; 14:cancers14102464. [PMID: 35626067 PMCID: PMC9139226 DOI: 10.3390/cancers14102464] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 05/09/2022] [Accepted: 05/13/2022] [Indexed: 12/19/2022] Open
Abstract
Background. The aim of our study was to describe the selected parameters of diffusion-weighted imaging (DWI) and perfusion dynamic contrast enhancement (DCE) MRI in primary tumors in patients with serous epithelial ovarian cancer (EOC), as well as in disease course prognosis and treatment response, including bevacizumab maintenance therapy. Materials and Methods. In total, 55 patients with primary serous EOC were enrolled in the study. All patients underwent MR imaging using a 1.5 T clinical whole-body MR system in preoperative DWI and DCE MRI selected parameters: apparent diffusion coefficients (ADC), time to peek (TTP) and perfusion maximum enhancement (Perf. Max. En.) were measured. The data were compared with histopathological and immunochemistry results (with Ki67 and VEGF expression) and clinical outcomes. Results. Higher mean ADC values were found in low-grade EOC compared to high-grade EOC: 1151.27 vs. 894,918 (p < 0.0001). A negative correlation was found between ADC and Ki67 expression (p = 0.027), and between ADC and VEGF expression (p = 0.042). There was a negative correlation between TTP and PFS (p = 0.0019) and Perf. Max. En. and PSF (p = 0.003). In the Kaplan−Meier analysis (log rank), a longer PFS was found in patients with ADC values greater than the median; p = 0.046. The Kaplan−Meier analysis showed a longer PFS (p = 0.0126) in a group with TTP below the mean value for this parameter in patients who received maintenance treatment with bevacizumab. Conclusions. The described relationships between PFS and DCE and DWI allow us to hope to include these parameters in the group of EOC prognostic factors. This aspect seems to be of particular interest in the case of the association of PFS with DCE values in the group of patients treated with bevacizumab.
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Affiliation(s)
- Pawel Derlatka
- Second Department Obstetrics and Gynecology, Medical University of Warsaw, Karowa 2 St., 00-315 Warsaw, Poland;
- Correspondence: (P.D.); (L.G.-D.); Tel.: +48-22-5966-512 (P.D.); +48-22-5992-300 (L.G.-D.)
| | - Laretta Grabowska-Derlatka
- Second Department of Clinical Radiology, Medical University of Warsaw, Banacha 1a St., 02-097 Warsaw, Poland; (M.H.-R.); (W.S.)
- Correspondence: (P.D.); (L.G.-D.); Tel.: +48-22-5966-512 (P.D.); +48-22-5992-300 (L.G.-D.)
| | - Marta Halaburda-Rola
- Second Department of Clinical Radiology, Medical University of Warsaw, Banacha 1a St., 02-097 Warsaw, Poland; (M.H.-R.); (W.S.)
| | - Wojciech Szeszkowski
- Second Department of Clinical Radiology, Medical University of Warsaw, Banacha 1a St., 02-097 Warsaw, Poland; (M.H.-R.); (W.S.)
| | - Krzysztof Czajkowski
- Second Department Obstetrics and Gynecology, Medical University of Warsaw, Karowa 2 St., 00-315 Warsaw, Poland;
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A Nomogram Combining MRI Multisequence Radiomics and Clinical Factors for Predicting Recurrence of High-Grade Serous Ovarian Carcinoma. JOURNAL OF ONCOLOGY 2022; 2022:1716268. [PMID: 35571486 PMCID: PMC9095390 DOI: 10.1155/2022/1716268] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 02/24/2022] [Accepted: 04/11/2022] [Indexed: 11/26/2022]
Abstract
Objective To develop a combined nomogram based on preoperative multimodal magnetic resonance imaging (mMRI) and clinical information for predicting recurrence in patients with high-grade serous ovarian carcinoma (HGSOC). Methods This retrospective study enrolled 141 patients with clinicopathologically confirmed HGSOC, including 65 patients with recurrence and 76 without recurrence. Radiomics features were extracted from the mMRI images (FS-T2WI, DWI, and T1WI+C). L1 regularization-based least absolute shrinkage and selection operator (LASSO) regression was performed to select radiomics features. A multivariate logistic regression analysis was used to build the classification models. A nomogram was established by incorporating clinical risk factors and radiomics Radscores. The area under the curve (AUC) of receiver operating characteristics, accuracy, and calibration curves were assessed to evaluate the performance of classification models and nomograms in discriminating recurrence. Kaplan-Meier survival analysis was used to evaluate the associations between the Radscore or clinical factors and disease-free survival (DFS). Results One clinical factor and seven radiomics signatures were ultimately selected to establish the predictive model for this study. The AUCs for identifying recurrence in the training and validation cohorts were 0.76 (0.68, 0.84) and 0.67 (0.53, 0.81) with the clinical model, 0.78 (0.71, 0.86) and 0.74 (0.61, 0.86) with the multiradiomics model, and 0.83 (0.77, 0.90) and 0.78 (0.65, 0.90) with the combined nomogram, respectively. The DFS was significantly shorter in the high-risk group than in the low-risk group. Conclusion By incorporating radiomics Radscores and clinical factors, we created a radiomics nomogram to preoperatively identify patients with HGSOC who have a high risk of recurrence, which may serve as a potential tool to guide personalized treatment.
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Gagliardi T, Adejolu M, deSouza NM. Diffusion-Weighted Magnetic Resonance Imaging in Ovarian Cancer: Exploiting Strengths and Understanding Limitations. J Clin Med 2022; 11:1524. [PMID: 35329850 PMCID: PMC8949455 DOI: 10.3390/jcm11061524] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 03/05/2022] [Accepted: 03/08/2022] [Indexed: 02/06/2023] Open
Abstract
Detection, characterization, staging, and response assessment are key steps in the imaging pathway of ovarian cancer. The most common type, high grade serous ovarian cancer, often presents late, so that accurate disease staging and response assessment are required through imaging in order to improve patient management. Currently, computerized tomography (CT) is the most common method for these tasks, but due to its poor soft-tissue contrast, it is unable to quantify early response within lesions before shrinkage is observed by size criteria. Therefore, quantifiable techniques, such as diffusion-weighted magnetic resonance imaging (DW-MRI), which generates high contrast between tumor and healthy tissue, are increasingly being explored. This article discusses the basis of diffusion-weighted contrast and the technical issues that must be addressed in order to achieve optimal implementation and robust quantifiable diffusion-weighted metrics in the abdomen and pelvis. The role of DW-MRI in characterizing adnexal masses in order to distinguish benign from malignant disease, and to differentiate borderline from frankly invasive malignancy is discussed, emphasizing the importance of morphological imaging over diffusion-weighted metrics in this regard. Its key role in disease staging and predicting resectability in comparison to CT is addressed, including its valuable use as a biomarker for following response within individual lesions, where early changes in the apparent diffusion coefficient in peritoneal metastases may be detected. Finally, the task of implementing DW-MRI into clinical trials in order to validate this biomarker for clinical use are discussed, along with the trials that include it within their protocols.
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Affiliation(s)
- Tanja Gagliardi
- Department of Imaging, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK; (T.G.); (M.A.)
| | - Margaret Adejolu
- Department of Imaging, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK; (T.G.); (M.A.)
| | - Nandita M. deSouza
- Department of Imaging, The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK; (T.G.); (M.A.)
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SW7 3RP, UK
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12
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Liang P, Li S, Xu C, Li J, Tan F, Hu D, Kamel I, Li Z. Assessment of renal function using magnetic resonance quantitative histogram analysis based on spatial labeling with multiple inversion pulses. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1614. [PMID: 34926658 PMCID: PMC8640904 DOI: 10.21037/atm-21-2299] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 08/15/2021] [Indexed: 12/17/2022]
Abstract
Background The incidence of chronic kidney disease (CKD) is high, and is easy to develop into end-stage renal disease (ESRD), which requires kidney dialysis or kidney transplantation. Therefore, we want to explore the clinical value of magnetic resonance quantitative histogram analysis based on spatial labeling with multiple inversion pulses (SLEEK) in assessing renal function in the early stage. Methods One hundred and twenty-nine patients underwent abdominal MRI examination, including a coronal SLEEK sequence. The patients were divided into the control group [CG, 47 cases, estimated glomerular filtration rate (eGFR) >90], the mild renal function impairment (mRI) group (48 cases, eGFR =60–90), and the moderate to severe renal function impairment (m-sRI) group (34 cases, eGFR <60). Two experienced radiologists delineated cortex and medulla regions of interest (ROIs) on SLEEK images to obtain cortex and medulla quantitative histogram parameters [Mean, Median, Percentiles (5th, 10th, 25th, 75th, and 90th), Skewness, Kurtosis, and Entropy] using FireVoxel. These histogram parameters were compared by proper statistical methods such as one-way analysis of variance, the χ2 test, and receiver operating characteristic (ROC) curve analysis. Results Four histogram parameters (Inhomogeneitycortex, Skewnesscortex, Kurtosismedulla, and Entropymedulla) differed significantly between the CG and the mRI group. One medulla (Entropymedulla) and nine cortex (Meancortex, Mediancortex, Kurtosiscortex, Entropycortex, and 5th, 10th, 25th, 75th, and 90th Percentilecortex) histogram parameters were significantly different between the m-RI and m-sRI groups. The most relevant parameter to eGFR was Inhomogenitycortex (r=−0.450, P<0.001). Inhomogeneitycortex had the largest area under the curve (AUC) for differentiating the mRI group from the CG (AUC =0.718; 95% CI: 0.616–0.806), while 25th Percentilecortex generated the largest AUC (AUC =0.786; 95% CI: 0.681–0.869) for differentiating the mRI and m-sRI groups. Conclusions Quantitative histogram parameters based on a SLEEK sequence can be used to supplement renal dysfunction assessment. Cortex histogram parameters are more valuable for evaluating renal function than medulla histogram parameters.
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Affiliation(s)
- Ping Liang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shichao Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chuou Xu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiali Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fangqin Tan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Daoyu Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ihab Kamel
- Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Baltimore, MD, USA
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Ozturk M, Polat AV, Selcuk MB. Whole-lesion ADC histogram analysis versus single-slice ADC measurement for the differentiation of benign and malignant soft tissue tumors. Eur J Radiol 2021; 143:109934. [PMID: 34500411 DOI: 10.1016/j.ejrad.2021.109934] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 07/25/2021] [Accepted: 08/23/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE To evaluate and compare the diagnostic performances of whole-lesion apparent diffusion coefficient (ADC) histogram analysis and single-slice ADC measurement in the differentiation of benign and malignant soft tissue tumors. METHODS Fifty-three patients (mean age: 48.5 ± 21.4) with soft tissue tumors (27 benign and 26 malignant) were evaluated with diffusion-weighted MRI. Whole-lesion ADC histogram parameters (mean, median, 10th percentile, 90th percentile, minimum, maximum, range, mean absolute deviation, interquartile range, kurtosis, skewness, root mean squared, variance and inhomogeneity) of the lesions were measured using the whole solid tumor volume region of interest (ROI). In other sessions, five ROIs were manually drawn on the tumor slices, and mean ADC and minimum ADC of the measurements were calculated. Diagnostic accuracies of the two methods were assessed and compared. RESULTS Mean, median, minimum, 10th percentile, 90th percentile, range, root mean squared and inhomogeneity of ADC histogram analysis, and mean ADC and minimum ADC of single-slice ADC measurement were significantly different between malignant and benign lesions (p < 0.001 - p = 0.002). Among the ADC histogram parameters, 10th percentile had the highest diagnostic performance (AUC = 0.825) followed by mean (AUC = 0.792) and median (AUC = 0.789). For the single-slice ADC measurement, the AUC of mean ADC and minimum ADC were 0.842 and 0.786, respectively. Mean ADC of single-slice measurement had a similar diagnostic performance with the 10th percentile, mean, and median of ADC histogram analysis (p = 0.070-1.000). CONCLUSIONS Both whole-lesion ADC histogram analysis and single-slice ADC measurement can differentiate benign and malignant soft tissue tumors with similar diagnostic performances.
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Affiliation(s)
- Mesut Ozturk
- Radiology Clinic, Samsun Gazi State Hospital, Samsun, Turkey; Department of Radiology, Ondokuz Mayis University, Faculty of Medicine, Samsun, Turkey.
| | - Ahmet Veysel Polat
- Department of Radiology, Ondokuz Mayis University, Faculty of Medicine, Samsun, Turkey
| | - Mustafa Bekir Selcuk
- Department of Radiology, Ondokuz Mayis University, Faculty of Medicine, Samsun, Turkey
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Zhao J, Gao S, Sun W, Grimm R, Fu C, Han J, Sheng R, Zeng M. Magnetic resonance imaging and diffusion-weighted imaging-based histogram analyses in predicting glypican 3-positive hepatocellular carcinoma. Eur J Radiol 2021; 139:109732. [PMID: 33905978 DOI: 10.1016/j.ejrad.2021.109732] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 03/23/2021] [Accepted: 04/15/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE We aimed to investigate the potential MR imaging findings in predicting glypican-3 (GPC3)-positive hepatocellular carcinomas (HCCs), with special emphasis on diffusion-weighted imaging (DWI)-based histogram analyses. METHODS Forty-three patients with pathologically-confirmed GPC3-negative HCCs and 100 patients with GPC3-positive HCCs were retrospectively evaluated using contrast-enhanced MRI and DWI. Clinical characteristics and MRI features including DWI-based histogram features were assessed and compared between the two groups. Univariate and multivariate analyses were used to identify the significant clinico-radiologic variables associated with GPC3 expressions that were then incorporated into a predictive nomogram. Nomogram performance was evaluated based on calibration, discrimination, and decision curve analyses. RESULTS Features significantly related to GPC3-positive HCCs at univariate analyses were serum alpha-fetoprotein (AFP) levels >20 ng/mL (P < 0.0001), absence of enhancing capsule (P = 0.040), peritumoral enhancement appearance on the arterial phase (P = 0.049), as well as lower mean (P = 0.0278), median (P = 0.0372) and 75th percentile (P = 0.0085) apparent diffusion coefficient (ADC) values. At multivariate analysis, the AFP levels (odds ratio, 11.236; P < 0.0001) and 75th percentile ADC values (odds ratio, 1.009; P = 0.033) were independent risk factors associated with GPC3-positive HCCs. When both criteria were combined, both sensitivity (79.0 %) and specificity (79.1 %) greater than 75 % were achieved, and satisfactory predictive nomogram performance was obtained with a C-index of 0.804 (95 % confidence interval, 0.729-0.866). Decision curve analysis further confirmed the clinical usefulness of the nomogram. CONCLUSIONS Elevated serum AFP levels and lower 75th percentile ADC values were helpful in differentiating GPC3-positive and GPC3-negative HCCs. The combined nomogram achieved satisfactory preoperative risk prediction of GPC3 expression in HCC patients.
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Affiliation(s)
- Jiangtao Zhao
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, 200032, China.
| | - Shanshan Gao
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, 200032, China.
| | - Wei Sun
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, 200032, China.
| | - Robert Grimm
- MR Application Predevelopment, Siemens Healthcare GmbH, 91052, Erlangen, Germany.
| | - Caixia Fu
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, 518057, China.
| | - Jing Han
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 20032, China.
| | - Ruofan Sheng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, 200032, China.
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, 200032, China.
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Xu M, Tang Q, Li M, Liu Y, Li F. An analysis of Ki-67 expression in stage 1 invasive ductal breast carcinoma using apparent diffusion coefficient histograms. Quant Imaging Med Surg 2021; 11:1518-1531. [PMID: 33816188 DOI: 10.21037/qims-20-615] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Background To investigate the value of apparent diffusion coefficient (ADC) histograms in differentiating Ki-67 expression in T1 stage invasive ductal breast carcinoma (IDC). Methods The records of 111 patients with pathologically confirmed T1 stage IDC who underwent magnetic resonance imaging prior to surgery were retrospectively reviewed. The expression of Ki-67 in tumor tissue samples from the patients was assessed using immunohistochemical (IHC) staining, with a cut-off value of 25% for high Ki-67 labeling index (LI). ADC images of the maximum lay of tumors were selected, and the region of interest (ROI) of each lay was delineated using the MaZda software and analyzed by histogram. The correlations between the histogram characteristic parameters and the Ki-67 LI were investigated. Additionally, the histogram characteristic parameters of the high Ki-67 group (n=54) and the low Ki-67 group (n=57) were statistically analyzed to determine the characteristic parameters with significant difference. Receiver operator characteristic (ROC) analyses were further performed for the significant parameters. Results The mean value, and the 1st, 10th, 50th, 90th, and 99th percentiles were found to be negatively correlated with the expression of Ki-67 (all P values <0.001), with a correlation coefficient of -0.624, -0.749, -0.717, -0.621, -0.500, and -0.410, respectively. In the high Ki-67 group, the mean value, and the 1st, 10th, 50th, 90th, and 99th percentiles extracted by the histogram were significantly lower (all P values <0.05) than that of the low Ki-67 group, with areas under the ROC curves ranging from 0.717-0.856. However, the variance, skewness, and kurtosis did not differ between the two groups (all P values >0.05). Conclusions Histogram-derived parameters for ADC images can serve as a reliable tool in the prediction of Ki-67 proliferation status in patients with T1 stage IDC. Among the significant ADC histogram values, the 1st and 10th percentiles showed the best predictive values.
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Affiliation(s)
- Maolin Xu
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Tang
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Manxiu Li
- Department of Breast Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yulin Liu
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fang Li
- Department of Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Prediction of Platinum-based Chemotherapy Response in Advanced High-grade Serous Ovarian Cancer: ADC Histogram Analysis of Primary Tumors. Acad Radiol 2021; 28:e77-e85. [PMID: 32061467 DOI: 10.1016/j.acra.2020.01.024] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 01/11/2020] [Accepted: 01/13/2020] [Indexed: 02/06/2023]
Abstract
RATIONALE AND OBJECTIVES To investigate the feasibility of apparent diffusion coefficient (ADC) histogram analysis of primary advanced high-grade serous ovarian cancer (HGSOC) to predict patient response to platinum-based chemotherapy. MATERIALS AND METHODS A total of 70 patients with 102 advanced stage HGSOCs (International Federation of Gynecology and Obstetrics (FIGO) stages III-IV) who received standard treatment of primary debulking surgery followed by the first line of platinum-based chemotherapy were retrospectively enrolled. Patients were grouped as platinum-resistant and platinum-sensitive according to whether relapse occurred within 6 months. Clinical characteristics, including age, pretherapy CA125 level, International Federation of Gynecology and Obstetrics stage, residual tumor, and histogram parameters derived from whole tumor and solid component such as ADCmean; 10th, 20th, 25th, 30th, 40th, 50th, 60th, 70th, 75th, 80th, 90th percentiles; skewness and kurtosis, were compared between platinum-resistant and platinum-sensitive groups. RESULTS No significantly different clinical characteristics were observed between platinum-sensitive and platinum-resistant patients. There were no significant differences in any whole-tumor histogram-derived parameters between the two groups. Significantly higher ADCmean and percentiles and significantly lower skewness and kurtosis from the solid-component histogram parameters were observed in the platinum-sensitive group when compared with the platinum-resistant group. ADCmean, skewness and kurtosis showed moderate prediction performances, with areas under the curve of 0.667, 0.733 and 0.616, respectively. Skewness was an independent risk factor for platinum resistance. CONCLUSION Pretreatment ADC histogram analysis of primary tumors has the potential to allow prediction of response to platinum-based chemotherapy in patients with advanced HGSOC.
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Therapeutic Approach to Low-Grade Serous Ovarian Carcinoma: State of Art and Perspectives of Clinical Research. Cancers (Basel) 2020; 12:cancers12051336. [PMID: 32456205 PMCID: PMC7281204 DOI: 10.3390/cancers12051336] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 05/14/2020] [Accepted: 05/21/2020] [Indexed: 12/14/2022] Open
Abstract
Low-grade serous ovarian carcinoma (LGSOC) is a distinct pathologic and clinical entity, characterized by less aggressive biological behavior, lower sensitivity to chemotherapy and longer survival compared with high-grade serous ovarian carcinoma. LGSOC often harbors activating mutations of genes involved in mitogen activated protein kinase (MAPK) pathway. Patients with disease confined to the gonad(s) should undergo bilateral salpingo-oophorectomy, total hysterectomy and comprehensive surgical staging, although fertility-sparing surgery can be considered in selected cases. Women with stage IA-IB disease should undergo observation alone after surgery, whereas observation, chemotherapy or endocrine therapy are all possible options for those with stage IC-IIA disease. Patients with advanced disease should undergo primary debulking surgery with the aim of removing all macroscopically detectable disease, whereas neoadjuvant chemotherapy followed by interval debuking surgery. After surgery, the patients can receive either carboplatin plus paclitaxel followed by endocrine therapy or endocrine therapy alone. Molecularly targeted agents, and especially MEK inhibitors and Cyclin-dependent kinase (CDK) inhibitors, are currently under evaluation. Additional research on the genomics of LGSOC and clinical trials on the combination of MEK inhibitors with hormonal agents, other molecularly targeted agents or metformin, are strongly warranted to improve the prognosis of patients with this malignancy.
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