1
|
Chen W, Geng D, Xu XQ, Hu WT, Dai YM, Wu FY, Zhu LN. Characterization of parotid gland tumors using diffusion-relaxation correlation spectrum imaging: a preliminary study. Clin Radiol 2024; 79:e878-e884. [PMID: 38582630 DOI: 10.1016/j.crad.2024.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 01/19/2024] [Accepted: 02/20/2024] [Indexed: 04/08/2024]
Abstract
AIM To assess the performance of diffusion-relaxation correlation spectrum imaging (DR-CSI) in the characterization of parotid gland tumors. MATERIALS AND METHODS Twenty-five pleomorphic adenomas (PA) patients, 9 Warthin's tumors (WT) patients and 7 malignant tumors (MT) patients were prospectively recruited. DR-CSI (7 b-values combined with 5 TEs, totally 35 diffusion-weighted images) was scanned for pre-treatment assessment. Diffusion (D)-T2 signal spectrum summating all voxels were built for each patient, characterized by D-axis with range 0∼5 × 10-3 mm2/s, and T2-axis with range 0∼300ms. With boundaries of 0.5 and 2.5 × 10-3 mm2/s for D, all spectra were divided into three compartments labeled A (low D), B (mediate D) and C (high D). Volume fractions acquired from each compartment (VA, VB, VC) were compared among PA, WT and MT. Diagnostic performance was assessed using receiver operating characteristic analysis and area under the curve (AUC). RESULTS Each subtype of parotid tumors had their specific D-T2 spectrum. PA showed significantly lower VA (8.85 ± 4.77% vs 20.68 ± 10.85%), higher VB (63.40 ± 8.18% vs 43.05 ± 7.16%), and lower VC (27.75 ± 8.51% vs 36.27 ± 11.09) than WT (all p<0.05). VB showed optimal diagnostic performance (AUC 0.969, sensitivity 92.00%, specificity 100.00%). MT showed significantly higher VA (21.23 ± 12.36%), lower VB (37.09 ± 6.43%), and higher VC (41.68 ± 13.72%) than PA (all p<0.05). Similarly, VB showed optimal diagnostic performance (AUC 0.994, sensitivity 96.00%, specificity 100.00%). No significant difference of VA, VB and VC was found between WT and MT. CONCLUSIONS DR-CSI might be a promising and non-invasive way for characterizing parotid gland tumors.
Collapse
Affiliation(s)
- W Chen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - D Geng
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - X-Q Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
| | - W-T Hu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Y-M Dai
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - F-Y Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - L-N Zhu
- Department of Stomatology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
| |
Collapse
|
2
|
Preoperative assessment of microvascular invasion of hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging with a fractional order calculus model: A pilot study. Magn Reson Imaging 2023; 95:110-117. [PMID: 34506910 DOI: 10.1016/j.mri.2021.09.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 09/05/2021] [Accepted: 09/05/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE To assess the clinical potential of a set of new diffusion parameters (D, β, and μ) derived from fractional order calculus (FROC) diffusion model in predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC). MATERIALS AND METHODS Between January 2019 to November 2020, a total of 63 patients with HCC were enrolled in this study. Diffusion-weighted images were acquired by using ten b-values (0-2000 s/mm2). The FROC model parameters including diffusion coefficient (D), fractional order parameter (β), a microstructural quantity (μ) together with a conventional apparent diffusion coefficient (ADC) were calculated. Intraclass coefficients were calculated for assessing the agreement of parameters quantified by two radiologists. The differences of these values between the MVI-positive and MVI-negative HCC groups were compared by using independent sample t-test or the Mann-Whitney U test. Then the parameters showing significant differences between subgroups, including the β and D, were integrated to develop a comprehensive predictive model via binary logistic regression. The diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis. RESULTS Among all the studied diffusion parameters, significant differences were found in D, β, and ADC between the MVI-positive and MVI-negative groups. MVI-positive HCCs showed significantly higher β values (0.65 ± 0.17 vs. 0.51 ± 0.13, P = 0.001), along with lower D values (0.84 ± 0.11 μm2/ms vs. 1.03 ± 0.13 μm2/ms, P < 0.001) and lower ADC values (1.38 ± 0.46 μm2/ms vs. 2.09 ± 0.70 μm2/ms, P < 0.001) than those of MVI-negative HCCs. According to the ROC analysis, the combination of D and β demonstrated the largest area under the ROC curve (0.920) compared with individual parameters (D: 0.912; β: 0.733; and ADC: 0.831) for differentiating MVI-positive from MVI-negative HCCs. CONCLUSIONS The FROC parameters can be used as noninvasive quantitative imaging markers for preoperatively predicting the MVI status of HCCs.
Collapse
|
3
|
Shetty SP, Mettu BSAR, Das SK, Hiremath R. Unusual case of skull base adenoid cystic carcinoma presenting as skull base osteomyelitis: case report. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [DOI: 10.1186/s43055-022-00769-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Adenoid cystic carcinoma is a rare malignancy. Tumours of palatal region with minor salivary gland origin do not generally present at an early stage as the tumour is submucosal with symptoms prevalent only when there is evidence of perineural spread of the tumour. We report a case of adenoid cystic carcinoma of the palate with rare presentation of left ear discharge and diplopia on left lateral gaze. We discuss the case with emphasis on imaging evaluation mimicking a case of infective etiology with adjacent skull base osteomyelitis on initial presentation. However, on follow-up and further evaluation the patient was diagnosed as adenoid cystic carcinoma of hard palate on left side.
Case presentation
A 25-year-old male patient has presented to Jagadguru Sri Shivarathreeswara Hospital in August 2019 with complaints of left ear discharge and diplopia on left lateral gaze since 1 week. The clinical and imaging findings was suggestive of infective etiology and the patient was treated for the same with IV antibiotics. Repeat magnetic resonance imaging was then done which revealed definitive reduction in the severity of inflammation suggestive of response to therapy. Patient was then discharged and was followed up. Three months later, the patient came with complaints of mass in left nasal cavity. Patient was then referred for contrast enhanced computed tomography neck strongly suggestive of neoplastic etiology. The patient was then operated and histopathological examination of the biopsy revealed adenoid cystic carcinoma.
Conclusions
Tumours of palatal region with minor salivary gland origin do not generally present at an early stage as the tumour is submucosal with symptoms prevalent only when there is evidence of perineural spread of the tumour. In our case patient presented with lateral rectus palsy, involvement of meckel’s cave, trigeminal nerve involvement and cavernous sinus involvement which are strong indicators of the perineural and locoregional spread of the tumour. Hence, it is important for the radiologist and clinician to strongly suspect and evaluate for a primary lesion of the head and neck when such a radiological presentation has been demonstrated.
Collapse
|
4
|
Zhang A, Hu Q, Song J, Dai Y, Wu D, Chen T. Value of non-Gaussian diffusion imaging with a fractional order calculus model combined with conventional MRI for differentiating histological types of cervical cancer. Magn Reson Imaging 2022; 93:181-188. [PMID: 35988835 DOI: 10.1016/j.mri.2022.08.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 08/15/2022] [Accepted: 08/16/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVE This study aimed to evaluate the value of a fractional order calculus (FROC) model combined with conventional magnetic resonance imaging (MRI) for differentiating cervical adenocarcinoma (CAC) from squamous cell carcinoma (SCC). METHODS Diffusion-weighted imaging (DWI) with 9 b values (0-2000s/mm2) was carried out in 57 cervical cancer patients. Diffusion coefficient (D), fractional order parameter (β), and microstructural quantity (μ) together with apparent diffusion coefficient (ADC) were calculated and compared between the CAC and SCC groups. Conventional MRI features included T2WI signal intensity (SI), unenhanced-T1WI SI, enhanced-T1WI SI, and ∆T1WI SI, which were also compared between the two groups. Receiver operating characteristic (ROC) analysis was employed to assess the performance of FROC parameters, ADC, and conventional MRI features in differentiating CAC from SCC. RESULTS β was significantly lower in the CAC group than in the SCC group (0.682 ± 0.054 vs. 0.723 ± 0.084, P = 0.035), while D and μ were not significantly different between the two groups (D, P = 0.171; μ, P = 0.127). There was no significant difference in the ADC value between the two groups (P = 0.053). In conventional MRI features, enhanced-T1WI SI was significantly higher in the SCC group than in the CAC group (985.78 ± 130.83 vs. 853.92 ± 149.65, P = 0.002). The area under the curve (AUC) of β, ADC, and enhanced-T1WI SI was 0.700, 0.683, and 0.799, respectively. The combination of β, ADC, and enhanced-T1WI SI revealed optimal diagnostic performance in differentiating CAC from SCC (AUC = 0.930), followed by β + enhanced-T1WI SI (AUC = 0.869), ADC+ enhanced-T1WI SI (AUC = 0.817), and β + ADC (AUC = 0.761). CONCLUSION The FROC model can serve as a noninvasive and quantitative imaging technique for differentiating CAC from SCC. β combined with ADC and enhanced-T1WI SI had the highest diagnostic efficiency.
Collapse
Affiliation(s)
- Aining Zhang
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Qiming Hu
- Department of Obstetrics & Gynecology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jiacheng Song
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yongming Dai
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronics Science, East China Normal University, Shanghai, China.
| | - Ting Chen
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
| |
Collapse
|
5
|
Luo Y, Jiang H, Meng N, Huang Z, Li Z, Feng P, Fang T, Fu F, Yuan J, Wang Z, Yang Y, Wang M. A comparison study of monoexponential and fractional order calculus diffusion models and 18F-FDG PET in differentiating benign and malignant solitary pulmonary lesions and their pathological types. Front Oncol 2022; 12:907860. [PMID: 35936757 PMCID: PMC9351313 DOI: 10.3389/fonc.2022.907860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 06/28/2022] [Indexed: 11/24/2022] Open
Abstract
Objective To evaluate the application value of monoexponential, fractional order calculus (FROC) diffusion models and PET imaging to distinguish between benign and malignant solitary pulmonary lesions (SPLs) and malignant SPLs with different pathological types and explore the correlation between each parameter and Ki67 expression. Methods A total of 112 patients were enrolled in this study. Prior to treatment, all patients underwent a dedicated thoracic 18F-FDG PET/MR examination. Five parameters [including apparent diffusion coefficient (ADC) derived from the monoexponential model; diffusion coefficient (D), a microstructural quantity (μ), and fractional order parameter (β) derived from the FROC model and maximum standardized uptake value (SUVmax) derived from PET] were compared between benign and malignant SPLs and different pathological types of malignant SPLs. Independent sample t test, Mann-Whitney U test, DeLong test and receiver operating characteristic (ROC) curve analysis were used for statistical evaluation. Pearson correlation analysis was used to calculate the correlations between Ki-67 and ADC, D, μ, β, and SUVmax. Results The ADC and D values were significantly higher and the μ and SUVmax values were significantly lower in the benign group [1.57 (1.37, 2.05) μm2/ms, 1.59 (1.52, 1.72) μm2/ms, 5.06 (3.76, 5.66) μm, 5.15 ± 2.60] than in the malignant group [1.32 (1.03, 1.51) μm2/ms, 1.43 (1.29, 1.52) μm2/ms, 7.06 (5.87, 9.45) μm, 9.85 ± 4.95]. The ADC, D and β values were significantly lower and the μ and SUVmax values were significantly higher in the squamous cell carcinoma (SCC) group [1.29 (0.66, 1.42) μm2/ms, 1.32 (1.02, 1.42) μm2/ms, 0.63 ± 0.10, 9.40 (7.76, 15.38) μm, 11.70 ± 5.98] than in the adenocarcinoma (AC) group [1.40 (1.28, 1.67) μm2/ms, 1.52 (1.44, 1.64) μm2/ms, 0.70 ± 0.10, 5.99 (4.54, 6.87) μm, 8.76 ± 4.18]. ROC curve analysis showed that for a single parameter, μ exhibited the best AUC value in discriminating between benign and malignant SPLs groups and AC and SCC groups (AUC = 0.824 and 0.911, respectively). Importantly, the combination of monoexponential, FROC models and PET imaging can further improve diagnostic performance (AUC = 0.872 and 0.922, respectively). The Pearson correlation analysis showed that Ki67 was positively correlated with μ value and negatively correlated with ADC and D values (r = 0.402, -0.346, -0.450, respectively). Conclusion The parameters D and μ derived from the FROC model were superior to ADC and SUVmax in distinguishing benign from malignant SPLs and adenocarcinoma from squamous cell carcinoma, in addition, the combination of multiple parameters can further improve diagnostic performance. The non-Gaussian FROC diffusion model is expected to become a noninvasive quantitative imaging technique for identifying SPLs.
Collapse
Affiliation(s)
- Yu Luo
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Han Jiang
- Department of Medical Imaging, Xinxiang Medical University & Henan Provincial People’s Hospital, Xinxiang, Henan, China
| | - Nan Meng
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Zhun Huang
- Department of Medical Imaging, Henan University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
| | - Ziqiang Li
- Department of Medical Imaging, Xinxiang Medical University & Henan Provincial People’s Hospital, Xinxiang, Henan, China
| | - Pengyang Feng
- Department of Medical Imaging, Henan University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
| | - Ting Fang
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Fangfang Fu
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
| | - Jianmin Yuan
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Zhe Wang
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Yang Yang
- Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Meiyun Wang
- Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
- *Correspondence: Meiyun Wang,
| |
Collapse
|
6
|
Sheng R, Zhang Y, Sun W, Ji Y, Zeng M, Yao X, Dai Y. Staging Chronic Hepatitis B Related Liver Fibrosis with a Fractional Order Calculus Diffusion Model. Acad Radiol 2022; 29:951-963. [PMID: 34429260 DOI: 10.1016/j.acra.2021.07.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/06/2021] [Accepted: 07/07/2021] [Indexed: 12/17/2022]
Abstract
RATIONALE AND OBJECTIVES Accurately staging liver fibrosis is of great clinical significance. We aimed to evaluate the clinical potential of the non-Gaussian fractional order calculus (FROC) diffusion model in staging liver fibrosis. MATERIALS AND METHODS A total of 82 patients with chronic hepatitis B (CHB) were included in this prospective study. Diffusion weighted imaging (DWI)-derived parameters including the diffusion coefficient (D), fractional order parameter (β) and microstructural quantity (μ) sourced from FROC-DWI, and apparent diffusion coefficient (ADC) derived from mono-exponential DWI, as well as the aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 (FIB-4) were calculated. Their correlations with fibrosis stages and the diagnostic efficacy in predicting liver fibrosis were assessed and compared. RESULTS D (r = -0.667), β (r = -0.671), μ (r = -0.481), and ADC (r = -0.665) displayed significant correlations with fibrosis stages (p < 0.001). D, β and ADC (p < 0.01) were independently associated with fibrosis; and compared to inflammatory activity, fibrosis was the independent factor significantly correlated with D, β and ADC (p < 0.001). There were no significant differences between the area under curves of D, β, μ or their combinations and ADC for predicting different fibrosis stages (p > 0.05). The diagnostic performance of the combined index with four diffusion metrics was better than D, β, μ or ADC used alone (p < 0.05) as well as APRI or FIB-4 (p < 0.01) in fibrosis staging. CONCLUSION FROC-DWI was valuable in staging liver fibrosis in patients with CHB, but there were no significant differences between the FROC-DWI parameters and the classical ADC. However, the combined DWI-derived index including D, β, μ and ADC offered the best diagnostic efficacy and may serve as a reliable tool for fibrosis evaluation, superior to APRI and FIB-4.
Collapse
Affiliation(s)
- Ruofan Sheng
- Department of Radiology, Zhongshan Hospital, Fudan University; Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, China
| | - Yunfei Zhang
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Wei Sun
- Department of Radiology, Zhongshan Hospital, Fudan University; Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, China
| | - Yuan Ji
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University; Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, China.
| | - Xiuzhong Yao
- Department of Radiology, Zhongshan Hospital, Fudan University; Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, China
| | - Yongming Dai
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| |
Collapse
|
7
|
Li W. Non-Gaussian Diffusion MRI for Evaluating Hepatic Fibrosis. Acad Radiol 2022; 29:964-966. [PMID: 35597754 DOI: 10.1016/j.acra.2022.04.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 04/16/2022] [Accepted: 04/21/2022] [Indexed: 11/01/2022]
|
8
|
Shi B, Xue K, Yin Y, Xu Q, Shi B, Wu D, Ye J. Grading of clear cell renal cell carcinoma using diffusion MRI with a fractional order calculus model. Acta Radiol 2022; 64:421-430. [PMID: 35040361 DOI: 10.1177/02841851211072482] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
BACKGROUND The fractional order calculus (FROC) model has been developed to describe restrained motion of water molecules as well as microstructural heterogeneity, providing a novel tool for non-invasive tumor grading. PURPOSE To evaluate the role of the FROC model in characterizing clear cell renal cell carcinoma (ccRCC) grades. MATERIAL AND METHODS A total of 59 patients diagnosed with ccRCC were included in this prospective study. The diffusion metrics derived from the mono-exponential model (apparent diffusion coefficient [ADC]), intra-voxel incoherent motion [IVIM] model [D, D*, f], and FROC model [Dfroc, β, μ]) were calculated and compared between low- and high-grade ccRCCs. Binary logistic regression analysis was performed to establish the diagnostic models. Receiver operating characteristic (ROC) analysis and DeLong test were performed to evaluate and compare the diagnostic performance of metrics in grading ccRCC. RESULTS All the metrics except D* and f exhibited statistical differences between low- and high-grade ccRCCs. ROC analysis showed individual FROC parameters, μ, Dfroc, and β, outperformed ADC and IVIM parameters in grading ccRCC. For single parameter, μ demonstrated the highest AUC value, sensitivity, and diagnostic accuracy in discriminating the two ccRCC groups while β exhibited the optimal specificity. Importantly, the combination of Dfroc, μ, and β could further improve the diagnostic performance. CONCLUSION The FROC parameters were superior to ADC and IVIM parameters in grading ccRCC, indicating the great potential of the FROC model in distinguishing low- and high-grade ccRCCs.
Collapse
Affiliation(s)
- Bowen Shi
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
| | - Ke Xue
- Central Research Institute, United Imaging Healthcare, Shanghai, PR China
| | - Yili Yin
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
| | - Qing Xu
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
| | - Binbin Shi
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronics Science, East China Normal University, Shanghai, PR China
| | - Jing Ye
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
| |
Collapse
|
9
|
Predicting the aggressiveness of peripheral zone prostate cancer using a fractional order calculus diffusion model. Eur J Radiol 2021; 143:109913. [PMID: 34464907 DOI: 10.1016/j.ejrad.2021.109913] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/01/2021] [Accepted: 08/12/2021] [Indexed: 11/21/2022]
Abstract
PURPOSE To evaluate the performance of parameters D, β, μ from the Fractional Order Calculus (FROC) model at differentiating peripheral zone (PZ) prostate cancer (PCa) MATERIAL AND METHODS: 75 patients who underwent targeted MRI-guided TRUS prostate biopsy within 6 months of MRI were reviewed retrospectively. Regions of interest (ROI) were placed on suspicious lesions on MRI scans. ROIs were then correlated to pathological results based on core biopsy location. The final tumor count is a total: 23 of GS 6 (3 + 3), 36 of GS 7 (3 + 4), 18 of GS 7 (4 + 3), and 19 of GS ≥ 8. Diffusion-weighted imaging (DWI) scans were fitted into the FROC and monoexponential model to calculate ADC and FROC parameters: anomalous diffusion coefficient D, intravoxel diffusion heterogeneity β, and spatial parameter μ. The performance of FROC parameters and ADC at differentiating PCa grade was evaluated with receiver operating characteristic (ROC) analysis. RESULTS In differentiating low (GS 6) vs. intermediate (GS 7) risk PZ PCa, combination of (D, β) provides the best performance with AUC of 0.829 with significance of p = 0.018 when compared to ADC (AUC of 0.655). In differentiating clinically significant (GS 6) vs. clinically significant (GS ≥ 7) PCa, combination of (D, β, μ) provides highest AUC of 0.802 when compared to ADC (AUC of 0.671) with significance of p = 0.038. Stratification of intermediate (GS 7) and high (GS ≥ 8) risk PCa with FROC did not reach a significant difference when compared to ADC. CONCLUSION Combination of FROC parameters shows greater performance than ADC at differentiating low vs. intermediate risk and clinically insignificant vs. significant prostate cancers in peripheral zone lesions. The FROC diffusion model holds promise as a quantitative imaging technique for non-invasive evaluation of PZ PCa.
Collapse
|