Combination of 99mTc-labeled PSMA-SPECT/CT and Diffusion-Weighted MRI in the prediction of early response after carbon ion therapy in prostate cancer: a prospective study

Combination of 99mTc-labeled PSMA-SPECT/CT and Diffusion-Weighted MRI in the prediction of early response after carbon ion therapy in prostate cancer: a prospective study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article Combination of 99m Tc-labeled PSMA-SPECT/CT and Diffusion-Weighted MRI in the prediction of early response after carbon ion therapy in prostate cancer: a prospective study Ping Li, Chang Liu, Shuang Wu, Lin Deng, guangyuan Zhang, Xin Cai, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-16861/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The purpose of this study was to assess the potential of 99mTc-labeled PSMA-SPECT/CT and Diffusion-Weighted Image (DWI) for predicting treatment response after carbon ion radiotherapy (CIRT) in prostate cancer. Methods We prospectively registered 26 patients with localized prostate cancer treated with CIRT. All patients underwent 99m Tc-labeled PSMA-SPECT/CT and multiparametric MRI before and after CIRT. The tumor/background ratio (TBR) and mean apparent diffusion coefficient (ADC mean ) were measured on the tumor and the percentage changes between 2 time points (ΔTBR and ΔADC mean ) were calculated. Patients were divided into two groups: good response and poor response according to clinical follow-up. Results The median follow up time was 38.3months. The TBR was significantly decreased ( p =0.001), while the ADC mean was significantly increased compared with the pretreatment value ( p <0.001). The ΔTBR and ΔADC mean were negatively correlated with each other ( p = 0.002). On ROC curve analysis for predicting treatment response, the area under the ROC curve (AUC) of ΔTBR (0.867) for predicting good response was higher than that of ΔADC mean (0.819). The AUC of combined with ΔTBR and ΔADC mean (0.895) was higher than that of either ΔADC mean or ΔTBR alone. The combined use of ΔTBR and ΔADC mean showed 91.4% sensitivity and 95.2% specificity. Conclusions Our preliminary data indicate that the changes of TBR and ADC mean maybe an early bio-marker for predicting prognosis after CIRT in localized prostate cancer patients. In addition, the ΔTBR was a more powerful prognostic factor than ΔADC mean in prostate cancer treated with CIRT. Oncology Cancer Biology PSMA DWI Prostate cancer Carbon ion radiotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background External beam radiotherapy (EBRT) is commonly used as a curative strategy for man diagnosed with localized prostate cancer. Because there are some critical organs at risk (OARs) surrounding the prostate, it is very difficult to deliver a high dose to prostate while minimizing the radiation dose to the adjacent OARs, such as, rectum and bladder. Carbon ion radiotherapy (CIRT) is considered to be the most advanced and promising radiotherapy technique. The physical and biological advantage of carbon ion that allow for the application of a high dose to the prostate while maintaining a steep gradient to the surrounding normal tissue [ 1 ]. Shanghai Proton and Heavy Ion Center (SPHIC) started CIRT for prostate cancer in 2014. Until November 2019, there are more than 200 prostate cancer patients have been treated at our center. However, CIRT is a novel and to date not thoroughly investigated technique. Until now, there are only about 3000 patients with prostate cancer received CIRT around the world [ 2 , 3 ]. So, the experience for CIRT is very limited for prostate cancer. In addition, Prostate cancer often has a long natural history, it often takes a decade or more to judge the therapeutic efficacy of prostate cancer. An early prediction of treatment response may allow for therapeutic optimization; such as radiation dose modification. Thus, we assessed whether molecular imaging can act as an early predictive tool for these patients’ outcome after CIRT. Prostate-specific membrane antigen (PSMA), a unique membrane-bound type II glycoprotein, is known to be over-expressed in almost all prostate cancer cells, with only 5–10% primary prostate cancer not having PSMA expression [ 4 ]. PSMA - targeted molecular imaging have been approved to be a better diagnostic tool in patients with prostate cancer than conventional imaging [ 5 ]. However, the clinical data focusing on the predictive value of PSMA imaging for primary localized prostate cancer patients treated with radiotherapy (especially CIRT) was limited. In addition, our primary study shown that apparent diffusion coefficient (ADC) vales may be an useful imaging bio-marker for early assessment of therapeutic response of prostate cancer to CIRT [ 6 ]. To our knowledge, there were limited studies addressed the relationship between PSMA-targeted imaging and diffusion-weighted image (DWI) of prostate cancer [ 7 ]. And there was no data comparing the predictive value of these two functional imaging for prostate cancer patients underwent CIRT. Therefore, we designed a prospective clinical trial to evaluate and compare the potential value of 99m Tc-labeled PSMA-single photon emission computed tomography/computed tomography (SPECT/CT) and DWI for predicting outcome after CIRT in prostate cancer. Methods Patients: This study was a phase I study evaluating the CIRT for localized prostate cancer in dose escalation at SPHIC. Prior to screening procedures and treatment, signed informed consent was obtained from all patients. This trial is registered with ClinicalTrials.gov, number NCT02739659. Eligible men were required to be aged 20–85 years and have Karnofsky Performance Score ≥ 70, pathologically confirmed adenocarcinoma of the prostate. And localized patients (T1-4 N0 M0, AJCC 7th ) without pelvic lymph nodes or distant metastasis planned for CIRT were eligible for this study. PSMA-SPECT/CT and magnetic resonance imaging (MRI) examinations were conducted at two time points: before and immediately (1 week after the last irradiation) after CIRT. And the interval between SPECT/CT and MRI examination was less than one week. Men who had received prior chemotherapy or radioisotopes for prostate cancer were excluded. The study protocol was approved by all institutional ethics boards. Radiopharmaceuticals and SPECT/CT imaging protocols This small-molecular inhibitor of PSMA, 6-hydrazinonicotinate-Aminocaproic acid-Lysine-Urea-Glutamate (HYNIC-ALUG) was radiolabeled by 99mTc as described previously [ 8 ]. The prepared radiotracer was injected into patient within 1 h of preparation. Patients underwent 99m Tc-HYNIC-PSMA SPECT/CT using a rotating, large field-of-view gamma camera (Discovery NM/CT 670, General Electric Medical Systems, Waukesha, WI) at 120 min after tracer injection of 750 MBq 99m Tc-HYNIC-PSMA. MRI acquisition: All MRI examinations were performed using a 3-T MR system (Magnetom Skyra Simens) equipped with a phased-array coil at SPHIC. T1- weighted, T2-weighted, and DWI sequences were acquired, but only DWI sequence was used for analysis in this study. Parametric maps of ADC values were automatically measured by the image software with the use of the two b values. Images analysis: SPECT/CT image readout was performed on a work Station and software (Xeleris, General Electric, Waukesha, WI). Two board-certified specialists in nuclear medicine, who blinded to patient related medical data, independently read all datasets and resolved any disagreements by consensus. Areas of abnormal tracer uptake within the prostate gland were determined and recorded. For semi-quantitative analysis, the tumor/background ratio (TBR) was calculated for each visually detected lesion or other tissue within each lobe (right / left) of the prostate using the quotient of maximal counts within a circular region-of-interest and mean counts within the obturator muscle [ 9 ]. Similarly, all acquired MRI was analyzed and interpreted by two radiologists independently using the manufacturer supplied software (Simens Healthcare). For calculating the mean ADC (ADC mean ) value of tumor, the region of interest (ROI) was manually drawn by two radiologists on single axial image where the tumor shows the maximum dimension. If the two readers disagreed about the exact tumor localization on the MR images, consensus was reached using information from SPECT/CT image or pathological results of biopsies. To assess the changes of TBR and ADC values after CIRT, percentage changes in TBR and ADC mean were calculated by the following equation: ΔTBR (%) = [(preTx TBR - postTx TBR) / preTx TBR] × 100; ΔADC (%) =[ (preTx ADC mean - postTx ADC mean ) / preTx ADC mean ] × 100. Carbon ion radiotherapy: The radiation dose was 59.2 Gy (relative biological effectiveness, RBE) / 16Fx with carbon ion only, and the clinical target volume (CTV) consisted of the prostate and seminal vesicle but not the pelvic lymph nodes. Combined androgen blockade (CAB) was concurrently administered to the all patients. Patients with intermediate risk received CAB for about 6 months, and high / very high risk patients received CAB for 2–3 years. Evaluations of patient outcomes: After the treatments, these patients were followed up every 3 months. Physical examinations and prostate specific antigen (PSA) were performed at each visit. Naik’s report [ 10 ] showed that 6 months post-treatment PSA > 0.1 ng/mL in prostate cancer patients treated with EBRT was associated with worse biochemical relapse free survival (bRFS), distant metastasis free survival (DMFS), and prostate cancer specific mortality (PCSM). Therefore, clinical outcomes were divided into two groups: good response (PSA ≤ 0.1 ng/mL at 6 months after therapy) and poor response (PSA > 0.1 ng/mL at 6 months after therapy). Statistical Analysis The data were analyzed using SPSS statistical software (version 22.0; IBM Corp.). All continuous variables were tested for normal distribution using the Kolmogorov-Smirnov test. Clinical data and parametric data from images were compared using the χ 2 test for categorical data, the Student t test for continuous data, and the Mann–Whitney test for nonparametric analysis. We calculated the Spearman rank-order correlation coefficient to characterize correlation strength between imaging features (TBR and ADC mean ) and clinical features (GS, PSA). The correlation between percentage change in TBR (ΔTBR) and ADC mean (ΔADC mean ) were also evaluating using the spearman correlation coefficient. We used receiver - operating - characteristic (ROC) curves and calculated areas under the curves (AUCs) for each parameter. The combinations of parameters that distinguished good responders from poor responders were tested by multi-ROC curve analysis. For all statistical comparisons, a p value of less than 0.05 was considered significant. Results Clinical characteristics and treatment outcomes A total of 30 consecutive patients with biopsy confirmed prostate cancer being considered for CIRT were prospectively recruited at SPHIC between Apr. 2016 and Mar. 2017. Of them, 4 patients were excluded due to not performed PSMA-SPECT/CT before or after CIRT. Finally, 26 patients with localized prostate cancer, who completely received the CIRT and had adequate 99m Tc-labeled PSMA-SPECT/CT and multiparametric MRI image information at our institution were analyzed in this study. The characteristics of the 26 patients are summarized in Table 1 . The median age was 66.5 (Inter Quartile Range,IQR: 58.8–72.8). 8 patients had intermediate risk, 9 patients had high risk and 9 patients had very high risk of prostate cancer. The median PSA level among patients before CIRT was 7.09 ng/mL (IQR: 1.16–9.37 ng/mL), and the median PSA level decreased to 1.65 ng/mL (IQR: 0.17–3.80 ng/mL) after CIRT. Table 1 The clinical characteristics of all the patients No. of patients n = 26 Age (years) Median 66.5 IQR 58.8–72.8 Gleason score 6 9 7 5 8 8 9 4 T staging T1 2 T2 19 T3 3 T4 2 Risk groups * Intermediate 8 High 9 Very high 9 Pre-treatment PSA value(ng/mL) Median 7.09 After treatment PSA value(ng/mL) Median 1.65 * These patients were classified into prognostic risk groups based on the National Comprehensive Cancer Network (NCCN) criteria. All of the patients completed their CIRT without any problem. After a median follow up of 38.3 months (IQR 25.7–31.4 months), 21 of the 26 (80.77%) patients were evaluated as good response, whereas 5 (19.23%) patients were poor response. At the time of analysis, 1 patient evaluated as good response died due to cerebrovascular accident, and 1 patient evaluated as poor response died due to pulmonary infection. 1 patient evaluated as poor response developed into biochemical recurrence (Phoenix consensus). And another patient with poor response developed bone metastases. In addition to the above mentioned patients, other patients remained alive and disease-free. TBR and ADC mean Before CIRT, TBR significantly correlated with baseline PSA (correlation coefficient r = 0.588; p = 0.002). However, there was no correlation between ADC mean and baseline PSA (correlation coefficient r = -0.167; p = 0.415). There was no significant difference of TBR ( p = 0.128) and ADC mean ( p = 0.991) among different Gleason score groups. After CIRT, the mean TBR of the 26 patients significantly decreased from 15.183 ± 14.703 to 5.503 ± 3.096 ( p = 0.001). And the ADC mean value increased from 0.771 × 10 − 3 ± 0.204 × 10 − 3 mm 2 /s to 1.172 × 10 − 3 ± 0.154 × 10 − 3 mm 2 /s ( p ༜ 0.001). In addition, there was an inverse correlation between TBR and ADC mean before CIRT (Spearman correlation coefficient, -0.488; p = 0.011). But there was no correlation between TBR and ADC mean after CIRT (Spearman correlation coefficient, 0.005; p = 0.980). Table 2 presents the results of quantitative parameters of PSMA and DWI before and after CIRT with the two groups. Before CIRT, there were no significant difference in TBR ( p = 0.200) and ADC mean ( p = 0.138) between good responders and poor responders. And after CIRT, there were still no significant difference in ADC mean ( p = 0.374) between good responders and patients with poor responders. But there was significant difference in TBR between the two groups after CIRT ( p = 0.019). There was no significant difference in treatment response between Patient with low GS (6–7) and high GS (8–9) ( p = 0.091). Significant differences were also not observed for risk classification based on the National Comprehensive Cancer Network (NCCN) criteria ( p = 0.413) Table 2 Comparison of MRI, PSMA - SPECT/CT and clinical parameters between good responders and poor responders parameters Good responders (N = 21) Poor responders (N = 5) P value † ROC TBR Before PT 16.277 ± 15.746 10.591 ± 8.874 0.200 NA After PT 5.091 ± 3.176 7.231 ± 2.201 0.019 NA Δ TBR -0.582 ± 0.255 -0.141 ± 0.300 0.010 0.867 ADC mean (× 10 − 3 mm 2 /s ) Before PT 0.741 ± 0.199 0.892 ± 0.201 0.138 NA After PT 1.189 ± 0.136 1.099 ± 0.216 0.374 NA Δ ADC mean 0.719 ± 0.508 0.253 ± 0.223 0.028 0.819 Gleason Score (No. pts) 6–7 13 1 0.091 NA 8–9 8 4 Risk Group (No. pts) Intermediate 7 1 0.413 NA High 8 1 Very high 6 3 † Comparison between good responders and poor responders. The ΔTBR and ΔADC mean In a subgroup of patients with good response, the mean TBR significantly decreased from 17.688 ± 16.484 to 6.122 ± 3.605 ( p = 0.003), and the ADC mean significantly increased from 0.715 ± 0.200 × 10 − 3 mm 2 /s to 1.186 ± 0.169 × 10 − 3 mm 2 /s ( p < 0.001), after CIRT. In another subgroup of patients with poor response, the ADC mean also increased from 0.892 ± 0.201 × 10 − 3 mm 2 /s to 1.099 ± 0.216 × 10 − 3 mm 2 /s ( p = 0.038), However, there was no significant difference between TBR before (10.591 ± 8.875) and after (7.231 ± 2.201) CIRT ( p = 0.325) (Fig. 1 ). The ΔTBR and ΔADC mean were negatively correlated with each other (Spearman correlation coefficient, -0.586; p = 0.002) (Fig. 2 ). On ROC curve analysis for predicting treatment response, the AUC of ΔTBR (0.867, 95% confidence interval [CI], 0.686, 1.000) for predicting good response was higher than ΔADC mean (0.819, 95% confidence interval [CI], 0.631, 1.000). The optimal cutoff for distinguishing good response from poor response in the ROC analysis were ΔTBR 59.9%, respectively. And ΔTBR showed 80.0% sensitivity and 95.2% specificity, and ΔADC mean showed 57.1% sensitivity and 100% specificity for predicting good response using these criteria. The AUC of combined with ΔTBR and ΔADC mean (0.895, 95% confidence interval [CI], 0.747, 1.000) was higher than that of either ΔADC mean or ΔTBR alone. The combined use of ΔTBR and ΔADC mean showed 91.4% sensitivity and 95.2% specificity (Fig. 3 ). Discussion Our study demonstrated that ΔTBR and ΔADC mean after CIRT were negatively correlated. And both of them provide a noninvasive imaging biomarker for the early assessment of treatment response (Figs. 4 and 5 ). In addition, the ΔTBR was a more powerful prognostic factor than ΔADC mean in prostate cancer treated with CIRT. The combined use of ΔTBR and ΔADC mean served to better distinguish the good responders from poor responders. PSMA-based molecular imaging has rapidly emerged as a potential new standard of care for imaging prostate cancer, with images demonstrating relevant protein expression levels [ 11 ]. It is reported that PSMA expression is a relevant factor for tumor aggressiveness [ 12 ]. However, it is still unclear whether a receptor-targeting radiopharmaceutical, instead of a metabolic tracer, would have the same value for treatment response monitoring in prostate cancer [ 13 ]. Hillier’s study laid the theoretical foundation for response evaluation, which found that 123 I-MIP-1072 may allow monitoring of tumor progression in patients before, during, and after chemotherapy [ 14 ]. Seitz’s preliminary results concluded that the concordance rate was high between biochemical response and 68 Ga-PSMA PET/CT response in patients with metastatic prostate cancer undergoing chemotherapy [ 15 ]. Similar results were confirmed in another study using 99m Tc-MIP-1404 SPECT/CT [ 16 ], suggesting a possible role of that imaging tool for monitoring treatment in metastatic prostate cancer. However, these clinical studies were all focused on metastatic prostate cancer. There is no data reported in literature for the prediction of radiotherapy response in primary prostate cancer with PSMA ligands imaging. Although PSMA imaging has been used in prostate cancer radiotherapy, they always research whether this novel nuclear imaging modality can be used to direct a local boost to the lesions or to plan salvage radiotherapy [ 17 , 18 ]. Therefore, we designed a prospective clinical trial to explore the value of PSMA imaging in the early evaluation of CIRT and got very good results. The results showed that TBR significantly correlated with baseline PSA (p = 0.002). The change of TBR significantly decreased in the patients with good response (p = 0.003), but not in the patients with poor response (p = 0.325). Moreover, ΔTBR showed very high sensitivity (80.0%) and specificity (95.2%) in predicting the response of carbon ion radiotherapy. These indicated that the 99m Tc-labeled PSMA-SPECT/CT could serve as an early biomarker for predicting prognosis after CIRT and influence planned clinical management in a high proportion of patients with prostate cancer. Our previous study showed that the mean ADC value of prostate tumor was significantly increased after CIRT [ 6 ]. In a similar study, Wolf et al [ 19 ] also found that particle therapy induced a measurable and continuous increase in the ADC value of prostate cancer during and after therapy. In this study, the change in tumor of the ADC value after CIRT were consistent with these previous studies. The increase of ADC value after CIRT likely indicates the alterations in water diffusivity due to necrosis and apoptotic-induced cell death. Positron Emission Tomography/Computed Tomography (PET/CT) and DWI have complementary roles in the assessment of prostate cancer [ 20 ]. Recently, combined PET/MRI imaging systems have been explored in the clinic, and literature describing the initial experiences with PSMA - PET/MRI imaging in prostate cancer is already available [ 21 ]. However, relatively little early date is available regarding PSMA - PET and MRI for assessment of therapeutic response in prostate cancer. In this study, the TBR was inversely correlated with ADC mean before CIRT, however, there was no correlation between TBR and ADC mean after CIRT. Interestingly, the ΔTBR and ΔADC mean were negatively correlated with each other. Consistent with our data, previous studies in osteosarcoma have shown a significant negative correlation between ΔSUV (standard uptake value) and ΔADC mean after chemotherapy [ 22 ]. This is partly explainable because the different effectiveness of treatment influenced by radiosensitivity or chemosensitivity between these patients. Effective treatment may substantially increase tumor necrosis and apoptosis. Accordingly, greater changes of both ADC and TBR/SUV values after treatment potentially suggest that the tumors are more sensitive to treatment. Prostate cancer often has a long natural history, so it can take many years to determine whether a new treatment strategy for prostate result in improved prostate cancer patients’ survival. Combined use of 99m Tc-labeled PSMA-SPECT/CT and DWI imaging modalities can provide various biological information and thus may overcome the limitations of SPECT/CT and DWI. The higher predictive power achieved by a combination of DWI and 99m Tc-labeled PSMA-SPECT/CT parameters enable early predict the treatment response and then optimize the prescription dose, fraction size or hormone therapy during time. Our study also indicated that PSMA high expressed area is potential biological target volume for radiotherapy dose escalation in the future. In this study, endocrine therapy combined with CIRT should be taken into account. All of those patients in our study were concurrently treated endocrine therapy with CIRT. So the change of these image findings might contribute to endocrine therapy beyond the CIRT. However, in our clinical practice, Dose-escalated radiotherapy RT with endocrine therapy is a standard definitive treatment of localized prostate cancer. So our results still have clinical implication. There were several limitations in our study. First, the number of enrolled patients was relatively small and the follow-up duration was short. The short follow up of 38.3 months is not sufficient to identify the true recurrent patients with Phoenix criteria. Primary study [ 10 ] showed that 6 months post-treatment PSA > 0.1 ng/mL in prostate cancer patients treated with concurrent radiotherapy is associated with worse bRFS, DMFS, and PCSM. Therefore, our study use PSA response at 6 months after therapy as our clinical outcome endpoint instead of biochemical relapse free survival or overall survival rate. Meanwhile, by the date of follow-up, 2 of the 5 patients in the poor response group had progressed, 1 patient had biochemical recurrence and another one had bone metastasis. These results indicated that this surrogate endpoint was credible. A future study with larger population and longer follow-up is necessary for validating our preliminary results. Second, we investigated the utility of tumor ADC mean and TBR out of many quantitative imaging parameters (10th percentile ADC, SUV et, al). Third, there is no standard method for measuring the ADC value of prostate cancer [ 23 ], and we did not compare different methods in current study. Conclusions In conclusion, both 99m Tc-labeled PSMA-SPECT/CT and DWI are useful for predicting therapeutic response after CIRT in prostate cancer. The ΔTBR was a more powerful prognostic factor than ΔADC mean in prostate cancer treated with CIRT. The combined use of ΔTBR and ΔADC mean served to better distinguish the good responders from poor responders. Abbreviations EBRT External beam radiotherapy OARs Organs at risks CIRT Carbon ion radiotherapy SPHIC Shanghai Proton and Heavy Ion Center PSMA Prostate-specific membrane antigen ADC Apparent diffusion coefficient DWI Diffusion-weighted image SPECT/CT Single-photon emission computed tomography/computed tomography MRI Magnetic resonance imaging TBR Tumor/background ratio ROI Region of interest CAB Combined androgen blockade AUCs Areas under the curves CTV Clinical target volume PSA Prostate specific antigen bRFS Biochemical relapse free survival DMFS Distant metastasis free survival PCSM Prostate cancer specific mortality IQR Inter quartile range PET/CT Positron emission tomography/computed tomography SUV Standard uptake value Declarations Acknowledgments We thank all the patients who participated in this trial. Funding This article has drawn on a program of research funded by the National Key Research and Development Program of China (2017YFC0107600), National Natural Science Foundation of China (81773225), Pudong New area science and technology development foundation (PKJ2016-Y43), Shanghai Municipal Health commission (201940121). Author information Ping Li and Chang Liu contributed equally to this article. Affiliations Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, ,Shanghai, China Ping Li, Xin Cai, Qing Zhang, Shen Fu, Xiaomao Guo Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China Chang Liu, Silong Hu, Jingyi Cheng, Xiaoping Xu, Yingjian Zhang Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China Lin Deng, Guangyuan Zhang, Bin Wu Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China Shuang Wu Contribution PL, SF, QZ and YJZ conceived and designed the study; CL, JYC and XPX contributed to PSMA-SPECT/CT imaging analysis; LD, GYZ, BW contributed to MRI imaging analysis; SW Contributed data analysis and reagents; PL, XC and XMG participated in patient treatment and follow up; PL, CL and SW wrote the manuscript. All authors have read and approved the manuscript. Corresponding author Correspondence to Shen Fu, Qing zhang, Yingjian Zhang Ethics declarations Ethics approval and consent to participate This study was approved by the ethical review board of Shanghai Proton and Heavy Ion Center (approval reference number 1510-03-03-1907). All patients provided written informed consent before enrollment. The informed consent form also contained an agreement for the publication of the study results. Content for publication Not applicable Competing interests The authors declare that they have no competing interests. References Schulz-Ertner D, Tsujii H. Particle radiation therapy using proton and heavier ion beams. J Clin Oncol 2007;25:953-64. Nomiya T, Tsuji H, Kawamura H, et al. A multi-institutional analysis of prospective studies of carbon ion radiotherapy for prostate cancer: A report from the Japan Carbon ion Radiation Oncology Study Group (J-CROS). Radiother Oncol 2016;121:288-93. Habl G, Uhl M, Katayama S, et al. 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Diffusion-Weighted MRI Treatment Monitoring of Primary Hypofractionated Proton and Carbon Ion Prostate Cancer Irradiation Using Raster Scan Techniqu J Magn Reson Imaging 2017;46:850-60. Chen M, Zhang Q, Zhang C, et al. Combination of 68 Ga-PSMA PET/CT and multiparameter MRI improves the detection of clinically significant prostate cancer: a lesion by lesion analysis. J Nucl Med 2019;60:944-9. Grubmüller B, Baltzer P, Hartenbach S, et al. PSMA Ligand PET/MRI for Primary Prostate Cancer: Staging Performance and Clinical Impact. Clin Cancer Res 2018;24:6300-7. Byun BH, Kong CB, Lim I, et al. Combination of 18F-FDG PET/CT and diffusion-weighted MR imaging as a predictor of histologic response to neoadjuvant chemotherapy: preliminary results in osteosarcoma. J Nucl Med 2013;54:1053-9. Donati OF, Mazaheri Y, Afaq A, et al. Prostate cancer aggressiveness: assessment with whole-lesion histogram analysis of the apparent diffusion coefficient. Radiology 2014;271:143-52. 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Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Liu","suffix":""},{"id":398917,"identity":"5f525acc-95ed-4fdc-b1d2-44bda527f11d","order_by":3,"name":"Shuang Wu","email":"","orcid":"","institution":"Fudan University Shanghai Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Wu","suffix":""},{"id":398918,"identity":"d75456c0-f680-4a74-bae2-77d7b7a14413","order_by":4,"name":"Lin Deng","email":"","orcid":"","institution":"Shanghai Proton and Heavy ion Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Deng","suffix":""},{"id":398919,"identity":"13df9a8a-f7cb-43a7-9e1f-5dc79c5055ce","order_by":5,"name":"guangyuan Zhang","email":"","orcid":"","institution":"Shanghai Proton and Heavy Ion Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"guangyuan","middleName":"","lastName":"Zhang","suffix":""},{"id":398920,"identity":"5851a4ad-60a2-4879-bc06-ad745ac18c7e","order_by":6,"name":"Xin Cai","email":"","orcid":"","institution":"Shanghai Proton and Heavy Ion Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Cai","suffix":""},{"id":398921,"identity":"fe29a038-c18b-4ab4-b82f-1644e98490cd","order_by":7,"name":"Silong Hu","email":"","orcid":"","institution":"Fudan University Shanghai Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Silong","middleName":"","lastName":"Hu","suffix":""},{"id":398922,"identity":"c623a8cc-4481-44b7-a110-ba9f2e4a1e17","order_by":8,"name":"Jingyi Cheng","email":"","orcid":"","institution":"Fudan University Shanghai Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jingyi","middleName":"","lastName":"Cheng","suffix":""},{"id":398923,"identity":"785a9d10-c486-4977-ba4a-ca76f7253898","order_by":9,"name":"Xiaoping Xu","email":"","orcid":"","institution":"Fudan University Shanghai Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoping","middleName":"","lastName":"Xu","suffix":""},{"id":398924,"identity":"1761a6f9-71e5-44bf-865f-fc2e3443dc92","order_by":10,"name":"Bin Wu","email":"","orcid":"","institution":"Fudan University Shanghai Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Wu","suffix":""},{"id":398925,"identity":"ff62592e-7f9e-44c4-a407-31743f9bab0e","order_by":11,"name":"Xiaomao Guo","email":"","orcid":"","institution":"Fudan University Shanghai Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaomao","middleName":"","lastName":"Guo","suffix":""},{"id":398926,"identity":"ee0b7e10-9d34-4be0-9cad-54c3a893b18c","order_by":12,"name":"Yingjian Zhang","email":"","orcid":"","institution":"Fudan University Shanghai Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yingjian","middleName":"","lastName":"Zhang","suffix":""},{"id":398927,"identity":"aac6462b-97bb-43a7-834c-486d7c318e35","order_by":13,"name":"Qing Zhang","email":"","orcid":"","institution":"Shanghai Proton and Heavy Ion Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Zhang","suffix":""},{"id":398928,"identity":"c42d0978-62d9-49cf-9a34-02850468fdff","order_by":14,"name":"Shen Fu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYBACfvnzDx8k/rORI16L5AweZoMPbGnGxGsxuMHDJjiD7VBiA/G2zO49xszDcyC973gC44ePOURo4Zc5l/aYR+JO7swzD5glZ24jxpaGBHNjHoNnuRtuJLAx8xKjxeBAgpk0T8LhdAPitdzIMZOcceBwAvFaJHuOJRt8bEgznHnmYTNxfuFnbz74ILHBRp7vePLBDx+J0YIAB0iIGpiWBFJ1jIJRMApGwUgBAC/vPY+yiDaZAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai Proton and Heavy ion Center","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shen","middleName":"","lastName":"Fu","suffix":""}],"badges":[],"createdAt":"2020-03-07 11:41:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-16861/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-16861/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":633950,"identity":"ff61802b-6bfb-4f46-940a-f8d49cc440f5","added_by":"auto","created_at":"2020-03-11 21:43:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37081,"visible":true,"origin":"","legend":"TBR(A) and ADC(B) changes after CIRT of good responders and poor responders. * p\u003c 0.05","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-16861/v1/fig1.png"},{"id":633951,"identity":"3e70da6b-cb01-4fb8-aed2-176e9fc668da","added_by":"auto","created_at":"2020-03-11 21:43:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32275,"visible":true,"origin":"","legend":"Scatter-plot showing relationship between Δ TBR and Δ ADCmean after CIRT.","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-16861/v1/fig2.png"},{"id":633952,"identity":"e46142ac-849c-4fc7-86aa-7254a8a9171d","added_by":"auto","created_at":"2020-03-11 21:43:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":40325,"visible":true,"origin":"","legend":"ROC curves used to evaluate good response to CIRT with Δ TBR, Δ ADCmean and combined used of Δ TBR and Δ ADCmean, AUC of Δ TBR (0.867) was higher than that of Δ ADCmean (0.819). AUC increased with combined used of Δ TBR and Δ ADCmean (0.895).","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-16861/v1/fig3.png"},{"id":633953,"identity":"c2f206de-3d0d-40c8-a6b1-ae75fce59f9e","added_by":"auto","created_at":"2020-03-11 21:43:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":721589,"visible":true,"origin":"","legend":"A patient with pathology confirmed prostate cancer of Gleason score 4+4 (T3bN0M0, very high risk) who showed good response after CIRT. PSMA uptake (A) was visualized on the prostate with TBR of 34.9. After CIRT, the SPECT/CT (D) showed PSMA uptake was significantly decreased with TBR of 3.4. Axial T1 weighted MRI showed the tumor before (B) and after (E) CIRT. In baseline ADC map(C), ADCmean was 0.616×10-3 mm2/s. In post CIRT ADC map(F), ADCmean was 1.205 × 10-3 mm2/s.","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-16861/v1/fig4.png"},{"id":633954,"identity":"71aea334-aae5-4e0d-a124-996b7b7549d3","added_by":"auto","created_at":"2020-03-11 21:43:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":733121,"visible":true,"origin":"","legend":"A patient with pathology confirmed prostate cancer of Gleason score 5+4 (T3aN0M0, very high risk) who showed poor response after CIRT. PSMA uptake (A) was visualized on the prostate with TBR of 4.87. After CIRT, the PSMA (D) uptake was still visualized on the prostate (TBR, 3.4). Axial T1 weighted MRI showed the tumor before (B) and after (E) CIRT. In baseline ADC map(C), ADCmean was 0.787× 10-3 mm2/s. In post CIRT ADC map(F), ADCmean was 0.812 × 10-3 mm2/s.","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-16861/v1/fig5.png"},{"id":15666301,"identity":"89d091fe-5a3d-4a64-b371-9e350386dcc6","added_by":"auto","created_at":"2021-11-18 13:36:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2467033,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-16861/v1/67c41496-ac6b-4d1b-9cea-96993317ebd5.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eCombination of \u003csup\u003e99m\u003c/sup\u003eTc-labeled PSMA-SPECT/CT and Diffusion-Weighted MRI in the prediction of early response after carbon ion therapy in prostate cancer: a prospective study\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eExternal beam radiotherapy (EBRT) is commonly used as a curative strategy for man diagnosed with localized prostate cancer. Because there are some critical organs at risk (OARs) surrounding the prostate, it is very difficult to deliver a high dose to prostate while minimizing the radiation dose to the adjacent OARs, such as, rectum and bladder. Carbon ion radiotherapy (CIRT) is considered to be the most advanced and promising radiotherapy technique. The physical and biological advantage of carbon ion that allow for the application of a high dose to the prostate while maintaining a steep gradient to the surrounding normal tissue [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Shanghai Proton and Heavy Ion Center (SPHIC) started CIRT for prostate cancer in 2014. Until November 2019, there are more than 200 prostate cancer patients have been treated at our center. However, CIRT is a novel and to date not thoroughly investigated technique. Until now, there are only about 3000 patients with prostate cancer received CIRT around the world [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. So, the experience for CIRT is very limited for prostate cancer. In addition, Prostate cancer often has a long natural history, it often takes a decade or more to judge the therapeutic efficacy of prostate cancer. An early prediction of treatment response may allow for therapeutic optimization; such as radiation dose modification. Thus, we assessed whether molecular imaging can act as an early predictive tool for these patients\u0026rsquo; outcome after CIRT.\u003c/p\u003e \u003cp\u003eProstate-specific membrane antigen (PSMA), a unique membrane-bound type II glycoprotein, is known to be over-expressed in almost all prostate cancer cells, with only 5\u0026ndash;10% primary prostate cancer not having PSMA expression [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. PSMA - targeted molecular imaging have been approved to be a better diagnostic tool in patients with prostate cancer than conventional imaging [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the clinical data focusing on the predictive value of PSMA imaging for primary localized prostate cancer patients treated with radiotherapy (especially CIRT) was limited. In addition, our primary study shown that apparent diffusion coefficient (ADC) vales may be an useful imaging bio-marker for early assessment of therapeutic response of prostate cancer to CIRT [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo our knowledge, there were limited studies addressed the relationship between PSMA-targeted imaging and diffusion-weighted image (DWI) of prostate cancer [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. And there was no data comparing the predictive value of these two functional imaging for prostate cancer patients underwent CIRT. Therefore, we designed a prospective clinical trial to evaluate and compare the potential value of \u003csup\u003e99m\u003c/sup\u003eTc-labeled\u0026ensp;PSMA-single photon emission computed tomography/computed tomography (SPECT/CT) and DWI for predicting outcome after CIRT in prostate cancer.\u003c/p\u003e "},{"header":"Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients:\u003c/h2\u003e \u003cp\u003eThis study was a phase I study evaluating the CIRT for localized prostate cancer in dose escalation at SPHIC. Prior to screening procedures and treatment, signed informed consent was obtained from all patients. This trial is registered with ClinicalTrials.gov, number NCT02739659.\u003c/p\u003e \u003cp\u003eEligible men were required to be aged 20\u0026ndash;85\u0026nbsp;years and have Karnofsky Performance Score\u0026thinsp;\u0026ge;\u0026thinsp;70, pathologically confirmed adenocarcinoma of the prostate. And localized patients (T1-4 N0 M0, AJCC 7th ) without pelvic lymph nodes or distant metastasis planned for CIRT were eligible for this study. PSMA-SPECT/CT and magnetic resonance imaging (MRI) examinations were conducted at two time points: before and immediately (1 week after the last irradiation) after CIRT. And the interval between SPECT/CT and MRI examination was less than one week. Men who had received prior chemotherapy or radioisotopes for prostate cancer were excluded. The study protocol was approved by all institutional ethics boards.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eRadiopharmaceuticals and SPECT/CT imaging protocols\u003c/h2\u003e \u003cp\u003eThis small-molecular inhibitor of PSMA, 6-hydrazinonicotinate-Aminocaproic acid-Lysine-Urea-Glutamate (HYNIC-ALUG) was radiolabeled by 99mTc as described previously [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The prepared radiotracer was injected into patient within 1\u0026nbsp;h of preparation. Patients underwent \u003csup\u003e99m\u003c/sup\u003eTc-HYNIC-PSMA SPECT/CT using a rotating, large field-of-view gamma camera (Discovery NM/CT 670, General Electric Medical Systems, Waukesha, WI) at 120\u0026nbsp;min after tracer injection of 750\u0026nbsp;MBq \u003csup\u003e99m\u003c/sup\u003eTc-HYNIC-PSMA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMRI acquisition:\u003c/h2\u003e \u003cp\u003eAll MRI examinations were performed using a 3-T MR system (Magnetom Skyra Simens) equipped with a phased-array coil at SPHIC. T1- weighted, T2-weighted, and DWI sequences were acquired, but only DWI sequence was used for analysis in this study. Parametric maps of ADC values were automatically measured by the image software with the use of the two b values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eImages analysis:\u003c/h2\u003e \u003cp\u003eSPECT/CT image readout was performed on a work Station and software (Xeleris, General Electric, Waukesha, WI). Two board-certified specialists in nuclear medicine, who blinded to patient related medical data, independently read all datasets and resolved any disagreements by consensus. Areas of abnormal tracer uptake within the prostate gland were determined and recorded. For semi-quantitative analysis, the tumor/background ratio (TBR) was calculated for each visually detected lesion or other tissue within each lobe (right / left) of the prostate using the quotient of maximal counts within a circular region-of-interest and mean counts within the obturator muscle [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimilarly, all acquired MRI was analyzed and interpreted by two radiologists independently using the manufacturer supplied software (Simens Healthcare). For calculating the mean ADC (ADC\u003csub\u003emean\u003c/sub\u003e) value of tumor, the region of interest (ROI) was manually drawn by two radiologists on single axial image where the tumor shows the maximum dimension. If the two readers disagreed about the exact tumor localization on the MR images, consensus was reached using information from SPECT/CT image or pathological results of biopsies.\u003c/p\u003e \u003cp\u003eTo assess the changes of TBR and ADC values after CIRT, percentage changes in TBR and ADC\u003csub\u003emean\u003c/sub\u003e were calculated by the following equation: ΔTBR (%) = [(preTx TBR - postTx TBR) / preTx TBR]\u0026thinsp;\u0026times;\u0026thinsp;100; ΔADC (%) =[ (preTx ADC\u003csub\u003emean\u003c/sub\u003e - postTx ADC\u003csub\u003emean\u003c/sub\u003e) / preTx ADC\u003csub\u003emean\u003c/sub\u003e ]\u0026thinsp;\u0026times;\u0026thinsp;100.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCarbon ion radiotherapy:\u003c/h2\u003e \u003cp\u003eThe radiation dose was 59.2\u0026nbsp;Gy (relative biological effectiveness, RBE) / 16Fx with carbon ion only, and the clinical target volume (CTV) consisted of the prostate and seminal vesicle but not the pelvic lymph nodes. Combined androgen blockade (CAB) was concurrently administered to the all patients. Patients with intermediate risk received CAB for about 6 months, and high / very high risk patients received CAB for 2\u0026ndash;3\u0026nbsp;years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEvaluations of patient outcomes:\u003c/h2\u003e \u003cp\u003eAfter the treatments, these patients were followed up every 3 months. Physical examinations and prostate specific antigen (PSA) were performed at each visit. Naik\u0026rsquo;s report [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] showed that 6 months post-treatment PSA\u0026thinsp;\u0026gt;\u0026thinsp;0.1\u0026nbsp;ng/mL in prostate cancer patients treated with EBRT was associated with worse biochemical relapse free survival (bRFS), distant metastasis free survival (DMFS), and prostate cancer specific mortality (PCSM). Therefore, clinical outcomes were divided into two groups: good response (PSA\u0026thinsp;\u0026le;\u0026thinsp;0.1\u0026nbsp;ng/mL at 6 months after therapy) and poor response (PSA\u0026thinsp;\u0026gt;\u0026thinsp;0.1\u0026nbsp;ng/mL at 6 months after therapy).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe data were analyzed using SPSS statistical software (version 22.0; IBM Corp.). All continuous variables were tested for normal distribution using the Kolmogorov-Smirnov test. Clinical data and parametric data from images were compared using the χ\u003csup\u003e2\u003c/sup\u003e test for categorical data, the Student \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003et\u003c/span\u003e test for continuous data, and the Mann\u0026ndash;Whitney test for nonparametric analysis. We calculated the Spearman rank-order correlation coefficient to characterize correlation strength between imaging features (TBR and ADC\u003csub\u003emean\u003c/sub\u003e) and clinical features (GS, PSA). The correlation between percentage change in TBR (ΔTBR) and ADC\u003csub\u003emean\u003c/sub\u003e (ΔADC\u003csub\u003emean\u003c/sub\u003e) were also evaluating using the spearman correlation coefficient. We used receiver - operating - characteristic (ROC) curves and calculated areas under the curves (AUCs) for each parameter. The combinations of parameters that distinguished good responders from poor responders were tested by multi-ROC curve analysis. For all statistical comparisons, a \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e value of less than 0.05 was considered significant.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics and treatment outcomes\u003c/h2\u003e \u003cp\u003eA total of 30 consecutive patients with biopsy confirmed prostate cancer being considered for CIRT were prospectively recruited at SPHIC between Apr. 2016 and Mar. 2017. Of them, 4 patients were excluded due to not performed PSMA-SPECT/CT before or after CIRT. Finally, 26 patients with localized prostate cancer, who completely received the CIRT and had adequate \u003csup\u003e99m\u003c/sup\u003eTc-labeled\u0026ensp;PSMA-SPECT/CT and multiparametric MRI image information at our institution were analyzed in this study.\u003c/p\u003e \u003cp\u003eThe characteristics of the 26 patients are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age was 66.5 (Inter Quartile Range,IQR: 58.8\u0026ndash;72.8). 8 patients had intermediate risk, 9 patients had high risk and 9 patients had very high risk of prostate cancer. The median PSA level among patients before CIRT was 7.09\u0026nbsp;ng/mL (IQR: 1.16\u0026ndash;9.37\u0026nbsp;ng/mL), and the median PSA level decreased to 1.65\u0026nbsp;ng/mL (IQR: 0.17\u0026ndash;3.80\u0026nbsp;ng/mL) after CIRT.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eThe clinical characteristics of all the patients\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo. of patients\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003en\u0026thinsp;=\u0026thinsp;26\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAge (years)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMedian\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e66.5\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eIQR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e58.8\u0026ndash;72.8\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eGleason score\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e6\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e9\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e7\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e5\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e8\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e8\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eT staging\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eT1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eT2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e19\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eT3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eT4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eRisk groups \u003csup\u003e*\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eIntermediate\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e8\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHigh\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e9\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eVery high\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e9\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePre-treatment PSA value(ng/mL)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMedian\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e7.09\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAfter treatment PSA value(ng/mL)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMedian\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.65\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e* These patients were classified into prognostic risk groups based on the National Comprehensive Cancer Network (NCCN) criteria.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll of the patients completed their CIRT without any problem. After a median follow up of 38.3 months (IQR 25.7\u0026ndash;31.4 months), 21 of the 26 (80.77%) patients were evaluated as good response, whereas 5 (19.23%) patients were poor response. At the time of analysis, 1 patient evaluated as good response died due to cerebrovascular accident, and 1 patient evaluated as poor response died due to pulmonary infection. 1 patient evaluated as poor response developed into biochemical recurrence (Phoenix consensus). And another patient with poor response developed bone metastases. In addition to the above mentioned patients, other patients remained alive and disease-free.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTBR and ADC\u003csub\u003emean\u003c/sub\u003e\u003c/h2\u003e \u003cp\u003eBefore CIRT, TBR significantly correlated with baseline PSA (correlation coefficient r\u0026thinsp;=\u0026thinsp;0.588; \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u0026thinsp;=\u003c/span\u003e\u0026thinsp;0.002). However, there was no correlation between ADC\u003csub\u003emean\u003c/sub\u003e and baseline PSA (correlation coefficient r = -0.167; \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.415). There was no significant difference of TBR (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.128) and ADC\u003csub\u003emean\u003c/sub\u003e (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.991) among different Gleason score groups.\u003c/p\u003e \u003cp\u003eAfter CIRT, the mean TBR of the 26 patients significantly decreased from 15.183\u0026thinsp;\u0026plusmn;\u0026thinsp;14.703 to 5.503\u0026thinsp;\u0026plusmn;\u0026thinsp;3.096 (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.001). And the ADC\u003csub\u003emean\u003c/sub\u003e value increased from 0.771\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e \u0026plusmn; 0.204\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s to 1.172\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e \u0026plusmn; 0.154\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e ༜ 0.001). In addition, there was an inverse correlation between TBR and ADC\u003csub\u003emean\u003c/sub\u003e before CIRT (Spearman correlation coefficient, -0.488; \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.011). But there was no correlation between TBR and ADC\u003csub\u003emean\u003c/sub\u003e after CIRT (Spearman correlation coefficient, 0.005; p\u0026thinsp;=\u0026thinsp;0.980).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of quantitative parameters of PSMA and DWI before and after CIRT with the two groups. Before CIRT, there were no significant difference in TBR (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.200) and ADC\u003csub\u003emean\u003c/sub\u003e (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.138) between good responders and poor responders. And after CIRT, there were still no significant difference in ADC\u003csub\u003emean\u003c/sub\u003e (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.374) between good responders and patients with poor responders. But there was significant difference in TBR between the two groups after CIRT (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.019). There was no significant difference in treatment response between Patient with low GS (6\u0026ndash;7) and high GS (8\u0026ndash;9) (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.091). Significant differences were also not observed for risk classification based on the National Comprehensive Cancer Network (NCCN) criteria (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.413)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eComparison of MRI, PSMA - SPECT/CT and clinical parameters between good responders and poor responders\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eparameters\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eGood responders (N\u0026thinsp;=\u0026thinsp;21)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ePoor responders (N\u0026thinsp;=\u0026thinsp;5)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eP value \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eROC\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTBR\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eBefore PT\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e16.277\u0026thinsp;\u0026plusmn;\u0026thinsp;15.746\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e10.591\u0026thinsp;\u0026plusmn;\u0026thinsp;8.874\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.200\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eNA\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAfter PT\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.091\u0026thinsp;\u0026plusmn;\u0026thinsp;3.176\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e7.231\u0026thinsp;\u0026plusmn;\u0026thinsp;2.201\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e0.019\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eNA\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eΔ TBR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.582\u0026thinsp;\u0026plusmn;\u0026thinsp;0.255\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.141\u0026thinsp;\u0026plusmn;\u0026thinsp;0.300\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e0.010\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.867\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eADC\u003csub\u003emean\u003c/sub\u003e (\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s )\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eBefore PT\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.741\u0026thinsp;\u0026plusmn;\u0026thinsp;0.199\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.892\u0026thinsp;\u0026plusmn;\u0026thinsp;0.201\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.138\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eNA\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAfter PT\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.189\u0026thinsp;\u0026plusmn;\u0026thinsp;0.136\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.099\u0026thinsp;\u0026plusmn;\u0026thinsp;0.216\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.374\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eNA\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eΔ ADC\u003csub\u003emean\u003c/sub\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.719\u0026thinsp;\u0026plusmn;\u0026thinsp;0.508\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.253\u0026thinsp;\u0026plusmn;\u0026thinsp;0.223\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e0.028\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.819\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eGleason Score (No. pts)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e6\u0026ndash;7\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e13\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.091\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNA\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e8\u0026ndash;9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e8\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eRisk Group (No. pts)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eIntermediate\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e7\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.413\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cdiv class=\"SimplePara\"\u003eNA\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHigh\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e8\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eVery high\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e6\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e Comparison between good responders and poor responders.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe ΔTBR and ΔADC\u003csub\u003emean\u003c/sub\u003e\u003c/h2\u003e \u003cp\u003eIn a subgroup of patients with good response, the mean TBR significantly decreased from 17.688\u0026thinsp;\u0026plusmn;\u0026thinsp;16.484 to 6.122\u0026thinsp;\u0026plusmn;\u0026thinsp;3.605 (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.003), and the ADC\u003csub\u003emean\u003c/sub\u003e significantly increased from 0.715\u0026thinsp;\u0026plusmn;\u0026thinsp;0.200\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s to 1.186\u0026thinsp;\u0026plusmn;\u0026thinsp;0.169\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), after CIRT. In another subgroup of patients with poor response, the ADC\u003csub\u003emean\u003c/sub\u003e also increased from 0.892\u0026thinsp;\u0026plusmn;\u0026thinsp;0.201\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s to 1.099\u0026thinsp;\u0026plusmn;\u0026thinsp;0.216\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.038), However, there was no significant difference between TBR before (10.591\u0026thinsp;\u0026plusmn;\u0026thinsp;8.875) and after (7.231\u0026thinsp;\u0026plusmn;\u0026thinsp;2.201) CIRT (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.325) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The ΔTBR and ΔADC\u003csub\u003emean\u003c/sub\u003e were negatively correlated with each other (Spearman correlation coefficient, -0.586; \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOn ROC curve analysis for predicting treatment response, the AUC of ΔTBR (0.867, 95% confidence interval [CI], 0.686, 1.000) for predicting good response was higher than ΔADC\u003csub\u003emean\u003c/sub\u003e (0.819, 95% confidence interval [CI], 0.631, 1.000). The optimal cutoff for distinguishing good response from poor response in the ROC analysis were ΔTBR \u0026lt; -25.5% and ΔADC\u003csub\u003emean\u003c/sub\u003e \u0026gt; 59.9%, respectively. And ΔTBR showed 80.0% sensitivity and 95.2% specificity, and ΔADC\u003csub\u003emean\u003c/sub\u003e showed 57.1% sensitivity and 100% specificity for predicting good response using these criteria.\u003c/p\u003e \u003cp\u003eThe AUC of combined with ΔTBR and ΔADC\u003csub\u003emean\u003c/sub\u003e (0.895, 95% confidence interval [CI], 0.747, 1.000) was higher than that of either ΔADC\u003csub\u003emean\u003c/sub\u003e or ΔTBR alone. The combined use of ΔTBR and ΔADC\u003csub\u003emean\u003c/sub\u003e showed 91.4% sensitivity and 95.2% specificity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion","content":" \u003cp\u003eOur study demonstrated that ΔTBR and ΔADC\u003csub\u003emean\u003c/sub\u003e after CIRT were negatively correlated. And both of them provide a noninvasive imaging biomarker for the early assessment of treatment response (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In addition, the ΔTBR was a more powerful prognostic factor than ΔADC\u003csub\u003emean\u003c/sub\u003e in prostate cancer treated with CIRT. The combined use of ΔTBR and ΔADC\u003csub\u003emean\u003c/sub\u003e served to better distinguish the good responders from poor responders.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePSMA-based molecular imaging has rapidly emerged as a potential new standard of care for imaging prostate cancer, with images demonstrating relevant protein expression levels [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. It is reported that PSMA expression is a relevant factor for tumor aggressiveness [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, it is still unclear whether a receptor-targeting radiopharmaceutical, instead of a metabolic tracer, would have the same value for treatment response monitoring in prostate cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Hillier\u0026rsquo;s study laid the theoretical foundation for response evaluation, which found that \u003csup\u003e123\u003c/sup\u003eI-MIP-1072 may allow monitoring of tumor progression in patients before, during, and after chemotherapy [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Seitz\u0026rsquo;s preliminary results concluded that the concordance rate was high between biochemical response and \u003csup\u003e68\u003c/sup\u003eGa-PSMA PET/CT response in patients with metastatic prostate cancer undergoing chemotherapy [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Similar results were confirmed in another study using \u003csup\u003e99m\u003c/sup\u003eTc-MIP-1404 SPECT/CT [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], suggesting a possible role of that imaging tool for monitoring treatment in metastatic prostate cancer. However, these clinical studies were all focused on metastatic prostate cancer. There is no data reported in literature for the prediction of radiotherapy response in primary prostate cancer with PSMA ligands imaging. Although PSMA imaging has been used in prostate cancer radiotherapy, they always research whether this novel nuclear imaging modality can be used to direct a local boost to the lesions or to plan salvage radiotherapy [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, we designed a prospective clinical trial to explore the value of PSMA imaging in the early evaluation of CIRT and got very good results. The results showed that TBR significantly correlated with baseline PSA (p\u0026thinsp;=\u0026thinsp;0.002). The change of TBR significantly decreased in the patients with good response (p\u0026thinsp;=\u0026thinsp;0.003), but not in the patients with poor response (p\u0026thinsp;=\u0026thinsp;0.325). Moreover, ΔTBR showed very high sensitivity (80.0%) and specificity (95.2%) in predicting the response of carbon ion radiotherapy. These indicated that the \u003csup\u003e99m\u003c/sup\u003eTc-labeled\u0026ensp;PSMA-SPECT/CT could serve as an early biomarker for predicting prognosis after CIRT and influence planned clinical management in a high proportion of patients with prostate cancer.\u003c/p\u003e \u003cp\u003eOur previous study showed that the mean ADC value of prostate tumor was significantly increased after CIRT [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In a similar study, Wolf et al [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] also found that particle therapy induced a measurable and continuous increase in the ADC value of prostate cancer during and after therapy. In this study, the change in tumor of the ADC value after CIRT were consistent with these previous studies. The increase of ADC value after CIRT likely indicates the alterations in water diffusivity due to necrosis and apoptotic-induced cell death.\u003c/p\u003e \u003cp\u003ePositron Emission Tomography/Computed Tomography (PET/CT) and DWI have complementary roles in the assessment of prostate cancer [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Recently, combined PET/MRI imaging systems have been explored in the clinic, and literature describing the initial experiences with PSMA - PET/MRI imaging in prostate cancer is already available [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, relatively little early date is available regarding PSMA - PET and MRI for assessment of therapeutic response in prostate cancer. In this study, the TBR was inversely correlated with ADC\u003csub\u003emean\u003c/sub\u003e before CIRT, however, there was no correlation between TBR and ADC\u003csub\u003emean\u003c/sub\u003e after CIRT. Interestingly, the ΔTBR and ΔADC\u003csub\u003emean\u003c/sub\u003e were negatively correlated with each other. Consistent with our data, previous studies in osteosarcoma have shown a significant negative correlation between ΔSUV (standard uptake value) and ΔADC\u003csub\u003emean\u003c/sub\u003e after chemotherapy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This is partly explainable because the different effectiveness of treatment influenced by radiosensitivity or chemosensitivity between these patients. Effective treatment may substantially increase tumor necrosis and apoptosis. Accordingly, greater changes of both ADC and TBR/SUV values after treatment potentially suggest that the tumors are more sensitive to treatment.\u003c/p\u003e \u003cp\u003eProstate cancer often has a long natural history, so it can take many years to determine whether a new treatment strategy for prostate result in improved prostate cancer patients\u0026rsquo; survival. Combined use of \u003csup\u003e99m\u003c/sup\u003eTc-labeled\u0026ensp;PSMA-SPECT/CT and DWI imaging modalities can provide various biological information and thus may overcome the limitations of SPECT/CT and DWI. The higher predictive power achieved by a combination of DWI and \u003csup\u003e99m\u003c/sup\u003eTc-labeled\u0026ensp;PSMA-SPECT/CT parameters enable early predict the treatment response and then optimize the prescription dose, fraction size or hormone therapy during time. Our study also indicated that PSMA high expressed area is potential biological target volume for radiotherapy dose escalation in the future.\u003c/p\u003e \u003cp\u003eIn this study, endocrine therapy combined with CIRT should be taken into account. All of those patients in our study were concurrently treated endocrine therapy with CIRT. So the change of these image findings might contribute to endocrine therapy beyond the CIRT. However, in our clinical practice, Dose-escalated radiotherapy RT with endocrine therapy is a standard definitive treatment of localized prostate cancer. So our results still have clinical implication.\u003c/p\u003e \u003cp\u003eThere were several limitations in our study. First, the number of enrolled patients was relatively small and the follow-up duration was short. The short follow up of 38.3 months is not sufficient to identify the true recurrent patients with Phoenix criteria. Primary study [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] showed that 6 months post-treatment PSA\u0026thinsp;\u0026gt;\u0026thinsp;0.1\u0026nbsp;ng/mL in prostate cancer patients treated with concurrent radiotherapy is associated with worse bRFS, DMFS, and PCSM. Therefore, our study use PSA response at 6 months after therapy as our clinical outcome endpoint instead of biochemical relapse free survival or overall survival rate. Meanwhile, by the date of follow-up, 2 of the 5 patients in the poor response group had progressed, 1 patient had biochemical recurrence and another one had bone metastasis. These results indicated that this surrogate endpoint was credible. A future study with larger population and longer follow-up is necessary for validating our preliminary results. Second, we investigated the utility of tumor ADC\u003csub\u003emean\u003c/sub\u003e and TBR out of many quantitative imaging parameters (10th percentile ADC, SUV et, al). Third, there is no standard method for measuring the ADC value of prostate cancer [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and we did not compare different methods in current study.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eIn conclusion, both \u003csup\u003e99m\u003c/sup\u003eTc-labeled\u0026ensp;PSMA-SPECT/CT and DWI are useful for predicting therapeutic response after CIRT in prostate cancer. The ΔTBR was a more powerful prognostic factor than ΔADC\u003csub\u003emean\u003c/sub\u003e in prostate cancer treated with CIRT. The combined use of ΔTBR and ΔADC\u003csub\u003emean\u003c/sub\u003e served to better distinguish the good responders from poor responders.\u003c/p\u003e "},{"header":"Abbreviations","content":" \u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEBRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExternal beam radiotherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOARs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOrgans at risks\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCIRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCarbon ion radiotherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPHIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eShanghai Proton and Heavy Ion Center\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSMA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProstate-specific membrane antigen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eApparent diffusion coefficient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDWI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiffusion-weighted image\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPECT/CT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle-photon emission computed tomography/computed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMagnetic resonance imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTBR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor/background ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegion of interest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCombined androgen blockade\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUCs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAreas under the curves\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical target volume\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProstate specific antigen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ebRFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBiochemical relapse free survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDMFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDistant metastasis free survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCSM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProstate cancer specific mortality\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInter quartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePET/CT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePositron emission tomography/computed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSUV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard uptake value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the patients who participated in this trial.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article has drawn on a program of research funded by the National Key Research and Development Program of China (2017YFC0107600), National Natural Science Foundation of China (81773225), Pudong New area science and technology development foundation (PKJ2016-Y43), Shanghai Municipal Health commission (201940121).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePing Li and Chang Liu contributed equally to this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAffiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDepartment of Radiation Oncology, Shanghai Proton and Heavy Ion Center, ,Shanghai, China \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePing Li, Xin Cai, Qing Zhang, Shen Fu, Xiaomao Guo\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDepartment of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eChang Liu, Silong Hu, Jingyi Cheng, Xiaoping Xu, Yingjian Zhang\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDepartment of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLin Deng, Guangyuan Zhang, Bin Wu\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDepartment of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eShuang Wu\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePL, SF, QZ and YJZ conceived and designed the study; CL, JYC and XPX contributed to PSMA-SPECT/CT imaging analysis; LD, GYZ, BW contributed to MRI imaging analysis; SW Contributed data analysis and reagents; PL, XC and XMG participated in patient treatment and follow up; PL, CL and SW wrote the manuscript. All authors have read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Shen Fu, Qing zhang, Yingjian Zhang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the ethical review board of Shanghai Proton and Heavy Ion Center (approval reference number 1510-03-03-1907). All patients provided written informed consent before enrollment. The informed consent form also contained an agreement for the publication of the study results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSchulz-Ertner D, Tsujii H. Particle radiation therapy using proton and heavier ion beams. J Clin Oncol 2007;25:953-64.\u003c/li\u003e\n\u003cli\u003eNomiya T, Tsuji H, Kawamura H, et al. A multi-institutional analysis of prospective studies of\u0026nbsp;carbon\u0026nbsp;ion\u0026nbsp;radiotherapy\u0026nbsp;for\u0026nbsp;prostate cancer: A report from the Japan\u0026nbsp;Carbon\u0026nbsp;ion\u0026nbsp;Radiation Oncology Study Group (J-CROS). Radiother Oncol 2016;121:288-93.\u003c/li\u003e\n\u003cli\u003eHabl G, Uhl M, Katayama S, et al. Acute Toxicity and Quality of Life in Patients With\u0026nbsp;Prostate Cancer\u0026nbsp;Treated With Protons or\u0026nbsp;Carbon\u0026nbsp;Ions in a Prospective Randomized Phase II Study--The IPI Trial. Int J Radiat Oncol Biol Phys 2016;95:435-43.\u003c/li\u003e\n\u003cli\u003eSchwarzenboeck SM, Rauscher I, Bluemel C, et al. PSMA Ligands for PET Imaging of Prostate Cance J Nucl Med 2017;58:1545-52.\u003c/li\u003e\n\u003cli\u003eSawicki LM, Kirchner J, Buddensieck C, et al. Prospective comparison of whole-body MRI and 68Ga-PSMA PET/CT for the detection of biochemical recurrence of prostate cancer after radical prostatectomy. Eur J Nucl Med Mol Imaging 2019;46:1542-50.\u003c/li\u003e\n\u003cli\u003eQi WX, Zhang Q, Li P, et al. The predictive role of ADC values in prostate cancer patients treated with carbon‑ion radiotherapy: initial clinical experience at Shanghai Proton and Heavy Ion Center (SPHIC). J Cancer Res Clin Oncol 2016;142:1361-7.\u003c/li\u003e\n\u003cli\u003eHicks RM, Simko JP, Westphalen AC, et al. Diagnostic Accuracy of 68Ga-PSMA-11 PET/MRI Compared with Multiparametric MRI in the Detection of Prostate Cancer. Radiology 2018;289:730-7.\u003c/li\u003e\n\u003cli\u003eXu X, Zhang J, Hu S, et al. \u003csup\u003e99m\u003c/sup\u003eTc-labeling and evaluation of a HYNIC modified small-molecular inhibitor of prostate-specific membrane antigen. Nucl Med Biol 2017;48:69-75.\u003c/li\u003e\n\u003cli\u003eGoffin KE, Joniau S, Tenke P, et al. Phase 2 Study of 99mTc-Trofolastat SPECT/CT to Identify and Localize Prostate Cancer in Intermediate- and High-Risk Patients Undergoing Radical Prostatectomy and Extended Pelvic LN Dissection. J Nucl Med 2017;58:1408-13.\u003c/li\u003e\n\u003cli\u003eNaik M, Reddy CA, Stephans KL, et al. Posttreatment\u0026nbsp;Prostate-Specific\u0026nbsp;Antigen\u0026nbsp;6\u0026nbsp;Months\u0026nbsp;After\u0026nbsp;Radiation\u0026nbsp;With\u0026nbsp;Androgen\u0026nbsp;Deprivation Therapy\u0026nbsp;Predicts\u0026nbsp;for\u0026nbsp;Distant\u0026nbsp;Metastasis-Free\u0026nbsp;Survival\u0026nbsp;and\u0026nbsp;Prostate\u0026nbsp;Cancer-Specific\u0026nbsp;Mortality. Int J Radiat Oncol Biol Phys 2016;96:617-23.\u003c/li\u003e\n\u003cli\u003ePerera M, Papa N, Roberts M, et al. Gallium-68 Prostate-specific Membrane Antigen Positron Emission Tomography in Advanced Prostate Cancer-Updated Diagnostic Utility, Sensitivity, Specificity, and Distribution of Prostate-specific Membrane Antigen-avid Lesions: A Systematic Review and Meta-analy Eur Urol 2019. pii: S0302-2838(19)30095-8.\u003c/li\u003e\n\u003cli\u003eCaromile LA, Dortche K, Rahman MM, et al. PSMA redirects cell survival signaling from the MAPK to the PI3K-AKT pathways to promote the progression of prostate cancer. Sci Signal 2017;14:10(470).\u003c/li\u003e\n\u003cli\u003eCeci F, Herrmann K, Hadaschik B, et al. Therapy assessment in prostate cancer using choline and PSMA PET/CT. Eur J Nucl Med Mol Imaging 2017;44:78-83.\u003c/li\u003e\n\u003cli\u003eHillier SM, Kern AM, Maresca KP, et al. 123I-MIP-1072, a small-molecule inhibitor of prostate-specific membrane antigen, is effective at monitoring tumor response to taxane therapy. J Nucl Med 2011;52:1087-93.\u003c/li\u003e\n\u003cli\u003eSeitz AK, Rauscher I, Haller B, et al. Preliminary results on response assessment using (68)Ga-HBED-CC-PSMA PET/CT in patients with metastatic prostate cancer undergoing docetaxel chemotherapy. Eur J Nucl Med Mol Imaging 2018;45:602-12.\u003c/li\u003e\n\u003cli\u003eSchmidkonz C, Cordes M, Beck M, et al. Assessment of Treatment Response by \u003csup\u003e99m\u003c/sup\u003eTc-MIP-1404 SPECT/CT: A Pilot Study in Patients With Metastatic Prostate Cancer. Clin Nucl Med 2018;43:e250-8.\u003c/li\u003e\n\u003cli\u003eThomas L, Kantz S, Hung A, et al. \u003csup\u003e68\u003c/sup\u003eGa-PSMA-PET/CT imaging of localized primary prostate cancer patients for intensity modulated radiation therapy treatment planning with integrated boost. Eur J Nucl Med Mol Imaging 2018;45:1170-8.\u003c/li\u003e\n\u003cli\u003eCalais J, Czernin J, Cao M, et al. \u003csup\u003e68\u003c/sup\u003eGa-PSMA-11 PET/CT Mapping of Prostate Cancer Biochemical Recurrence After Radical Prostatectomy in 270 Patients with a PSA Level of Less Than 1.0 ng/mL: Impact on Salvage Radiotherapy Plann J Nucl Med 2018;59:230-7.\u003c/li\u003e\n\u003cli\u003eWolf MB, Edler C, Tichy D, et al. Diffusion-Weighted MRI Treatment Monitoring of Primary Hypofractionated Proton and Carbon Ion Prostate Cancer Irradiation Using Raster Scan Techniqu J Magn Reson Imaging 2017;46:850-60.\u003c/li\u003e\n\u003cli\u003eChen M, Zhang Q, Zhang C, et al. Combination of \u003csup\u003e68\u003c/sup\u003eGa-PSMA PET/CT and multiparameter MRI improves the detection of clinically significant prostate cancer: a lesion by lesion analysis. J Nucl Med 2019;60:944-9.\u003c/li\u003e\n\u003cli\u003eGrubm\u0026uuml;ller B, Baltzer P, Hartenbach S, et al. PSMA Ligand PET/MRI for Primary\u0026ensp;Prostate Cancer: Staging Performance and Clinical Impact. Clin Cancer Res 2018;24:6300-7.\u003c/li\u003e\n\u003cli\u003eByun BH, Kong CB, Lim I, et al. Combination of 18F-FDG PET/CT and diffusion-weighted MR imaging as a predictor of histologic response to neoadjuvant chemotherapy: preliminary results in osteosarcoma. J Nucl Med 2013;54:1053-9.\u003c/li\u003e\n\u003cli\u003eDonati OF, Mazaheri Y, Afaq A, et al. Prostate cancer aggressiveness: assessment with whole-lesion histogram analysis of the apparent diffusion coefficient. Radiology 2014;271:143-52.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PSMA, DWI, Prostate cancer, Carbon ion radiotherapy","lastPublishedDoi":"10.21203/rs.3.rs-16861/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-16861/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground \u003c/p\u003e\u003cp\u003eThe purpose of this study was to assess the potential of 99mTc-labeled PSMA-SPECT/CT and Diffusion-Weighted Image (DWI) for predicting treatment response after carbon ion radiotherapy (CIRT) in prostate cancer. \u003c/p\u003e\u003cp\u003eMethods \u003c/p\u003e\u003cp\u003eWe prospectively registered 26 patients with localized prostate cancer treated with CIRT. All patients underwent 99m Tc-labeled PSMA-SPECT/CT and multiparametric MRI before and after CIRT. The tumor/background ratio (TBR) and mean apparent diffusion coefficient (ADC mean ) were measured on the tumor and the percentage changes between 2 time points (ΔTBR and ΔADC mean ) were calculated. Patients were divided into two groups: good response and poor response according to clinical follow-up. \u003c/p\u003e\u003cp\u003eResults \u003c/p\u003e\u003cp\u003eThe median follow up time was 38.3months. The TBR was significantly decreased ( p =0.001), while the ADC mean was significantly increased compared with the pretreatment value ( p \u0026lt;0.001). The ΔTBR and ΔADC mean were negatively correlated with each other ( p = 0.002). On ROC curve analysis for predicting treatment response, the area under the ROC curve (AUC) of ΔTBR (0.867) for predicting good response was higher than that of ΔADC mean (0.819). The AUC of combined with ΔTBR and ΔADC mean (0.895) was higher than that of either ΔADC mean or ΔTBR alone. The combined use of ΔTBR and ΔADC mean showed 91.4% sensitivity and 95.2% specificity. \u003c/p\u003e\u003cp\u003eConclusions \u003c/p\u003e\u003cp\u003eOur preliminary data indicate that the changes of TBR and ADC mean maybe an early bio-marker for predicting prognosis after CIRT in localized prostate cancer patients. In addition, the ΔTBR was a more powerful prognostic factor than ΔADC mean in prostate cancer treated with CIRT.\u003c/p\u003e","manuscriptTitle":"Combination of 99mTc-labeled PSMA-SPECT/CT and Diffusion-Weighted MRI in the prediction of early response after carbon ion therapy in prostate cancer: a prospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-03-11 21:43:44","doi":"10.21203/rs.3.rs-16861/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"65c47213-71c9-4424-a5a3-00a824bc0fbb","owner":[],"postedDate":"March 11th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":69060,"name":"Oncology"},{"id":69061,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2020-05-29T03:02:18+00:00","versionOfRecord":[],"versionCreatedAt":"2020-03-11 21:43:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-16861","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-16861","identity":"rs-16861","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}