1
|
Gong AJ, Ruchalski K, Kim HJ, Douek M, Gutierrez A, Patel M, Sai V, Coy H, Villegas B, Raman S, Goldin J. RECIST 1.1 Target Lesion Categorical Response in Metastatic Renal Cell Carcinoma: A Comparison of Conventional versus Volumetric Assessment. Radiol Imaging Cancer 2023; 5:e220166. [PMID: 37656041 PMCID: PMC10546365 DOI: 10.1148/rycan.220166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 07/05/2023] [Accepted: 07/18/2023] [Indexed: 09/02/2023]
Abstract
Purpose To investigate Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1) approximations of target lesion tumor burden by comparing categorical treatment response according to conventional RECIST versus actual tumor volume measurements of RECIST target lesions. Materials and Methods This is a retrospective cohort study of individuals with metastatic renal cell carcinoma enrolled in a clinical trial (from 2003 to 2017) and includes individuals who underwent baseline and at least one follow-up chest, abdominal, and pelvic CT study and with at least one target lesion. Target lesion volume was assessed by (a) Vmodel, a spherical model of conventional RECIST 1.1, which was extrapolated from RECIST diameter, and (b) Vactual, manually contoured volume. Volumetric responses were determined by the sum of target lesion volumes (Vmodel-sum TL and Vactual-sum TL, respectively). Categorical volumetric thresholds were extrapolated from RECIST. McNemar tests were used to compare categorical volume responses. Results Target lesions were assessed at baseline (638 participants), week 9 (593 participants), and week 17 (508 participants). Vmodel-sum TL classified more participants as having progressive disease (PD), compared with Vactual-sum TL at week 9 (52 vs 31 participants) and week 17 (57 vs 39 participants), with significant overall response discordance (P < .001). At week 9, 25 (48%) of 52 participants labeled with PD by Vmodel-sum TL were classified as having stable disease by Vactual-sum TL. Conclusion A model of RECIST 1.1 based on a single diameter measurement more frequently classified PD compared with response assessment by actual measured tumor volume. Keywords: Urinary, Kidney, Metastases, Oncology, Tumor Response, Volume Analysis, Outcomes Analysis ClinicalTrials.gov registration no. NCT01865747 © RSNA, 2023 Supplemental material is available for this article.
Collapse
Affiliation(s)
- Amanda J. Gong
- From the David Geffen School of Medicine, University of California,
Los Angeles, Calif (A.J.G., K.R., H.J.K., M.D., A.G., M.P., V.S., H.C., S.R.,
J.G.); Department of Radiological Sciences, UCLA, Los Angeles, Calif (K.R.,
H.J.K., M.D., A.G., M.P., V.S., S.R., J.G.); and UCLA Center for Computer Vision
and Imaging Biomarkers, 924 Westwood Blvd, Ste 615, Los Angeles, CA 90024
(A.J.G., H.J.K., H.C., B.V., J.G.)
| | - Kathleen Ruchalski
- From the David Geffen School of Medicine, University of California,
Los Angeles, Calif (A.J.G., K.R., H.J.K., M.D., A.G., M.P., V.S., H.C., S.R.,
J.G.); Department of Radiological Sciences, UCLA, Los Angeles, Calif (K.R.,
H.J.K., M.D., A.G., M.P., V.S., S.R., J.G.); and UCLA Center for Computer Vision
and Imaging Biomarkers, 924 Westwood Blvd, Ste 615, Los Angeles, CA 90024
(A.J.G., H.J.K., H.C., B.V., J.G.)
| | - Hyun J. Kim
- From the David Geffen School of Medicine, University of California,
Los Angeles, Calif (A.J.G., K.R., H.J.K., M.D., A.G., M.P., V.S., H.C., S.R.,
J.G.); Department of Radiological Sciences, UCLA, Los Angeles, Calif (K.R.,
H.J.K., M.D., A.G., M.P., V.S., S.R., J.G.); and UCLA Center for Computer Vision
and Imaging Biomarkers, 924 Westwood Blvd, Ste 615, Los Angeles, CA 90024
(A.J.G., H.J.K., H.C., B.V., J.G.)
| | - Michael Douek
- From the David Geffen School of Medicine, University of California,
Los Angeles, Calif (A.J.G., K.R., H.J.K., M.D., A.G., M.P., V.S., H.C., S.R.,
J.G.); Department of Radiological Sciences, UCLA, Los Angeles, Calif (K.R.,
H.J.K., M.D., A.G., M.P., V.S., S.R., J.G.); and UCLA Center for Computer Vision
and Imaging Biomarkers, 924 Westwood Blvd, Ste 615, Los Angeles, CA 90024
(A.J.G., H.J.K., H.C., B.V., J.G.)
| | - Antonio Gutierrez
- From the David Geffen School of Medicine, University of California,
Los Angeles, Calif (A.J.G., K.R., H.J.K., M.D., A.G., M.P., V.S., H.C., S.R.,
J.G.); Department of Radiological Sciences, UCLA, Los Angeles, Calif (K.R.,
H.J.K., M.D., A.G., M.P., V.S., S.R., J.G.); and UCLA Center for Computer Vision
and Imaging Biomarkers, 924 Westwood Blvd, Ste 615, Los Angeles, CA 90024
(A.J.G., H.J.K., H.C., B.V., J.G.)
| | - Maitraya Patel
- From the David Geffen School of Medicine, University of California,
Los Angeles, Calif (A.J.G., K.R., H.J.K., M.D., A.G., M.P., V.S., H.C., S.R.,
J.G.); Department of Radiological Sciences, UCLA, Los Angeles, Calif (K.R.,
H.J.K., M.D., A.G., M.P., V.S., S.R., J.G.); and UCLA Center for Computer Vision
and Imaging Biomarkers, 924 Westwood Blvd, Ste 615, Los Angeles, CA 90024
(A.J.G., H.J.K., H.C., B.V., J.G.)
| | - Victor Sai
- From the David Geffen School of Medicine, University of California,
Los Angeles, Calif (A.J.G., K.R., H.J.K., M.D., A.G., M.P., V.S., H.C., S.R.,
J.G.); Department of Radiological Sciences, UCLA, Los Angeles, Calif (K.R.,
H.J.K., M.D., A.G., M.P., V.S., S.R., J.G.); and UCLA Center for Computer Vision
and Imaging Biomarkers, 924 Westwood Blvd, Ste 615, Los Angeles, CA 90024
(A.J.G., H.J.K., H.C., B.V., J.G.)
| | - Heidi Coy
- From the David Geffen School of Medicine, University of California,
Los Angeles, Calif (A.J.G., K.R., H.J.K., M.D., A.G., M.P., V.S., H.C., S.R.,
J.G.); Department of Radiological Sciences, UCLA, Los Angeles, Calif (K.R.,
H.J.K., M.D., A.G., M.P., V.S., S.R., J.G.); and UCLA Center for Computer Vision
and Imaging Biomarkers, 924 Westwood Blvd, Ste 615, Los Angeles, CA 90024
(A.J.G., H.J.K., H.C., B.V., J.G.)
| | - Bianca Villegas
- From the David Geffen School of Medicine, University of California,
Los Angeles, Calif (A.J.G., K.R., H.J.K., M.D., A.G., M.P., V.S., H.C., S.R.,
J.G.); Department of Radiological Sciences, UCLA, Los Angeles, Calif (K.R.,
H.J.K., M.D., A.G., M.P., V.S., S.R., J.G.); and UCLA Center for Computer Vision
and Imaging Biomarkers, 924 Westwood Blvd, Ste 615, Los Angeles, CA 90024
(A.J.G., H.J.K., H.C., B.V., J.G.)
| | - Steven Raman
- From the David Geffen School of Medicine, University of California,
Los Angeles, Calif (A.J.G., K.R., H.J.K., M.D., A.G., M.P., V.S., H.C., S.R.,
J.G.); Department of Radiological Sciences, UCLA, Los Angeles, Calif (K.R.,
H.J.K., M.D., A.G., M.P., V.S., S.R., J.G.); and UCLA Center for Computer Vision
and Imaging Biomarkers, 924 Westwood Blvd, Ste 615, Los Angeles, CA 90024
(A.J.G., H.J.K., H.C., B.V., J.G.)
| | - Jonathan Goldin
- From the David Geffen School of Medicine, University of California,
Los Angeles, Calif (A.J.G., K.R., H.J.K., M.D., A.G., M.P., V.S., H.C., S.R.,
J.G.); Department of Radiological Sciences, UCLA, Los Angeles, Calif (K.R.,
H.J.K., M.D., A.G., M.P., V.S., S.R., J.G.); and UCLA Center for Computer Vision
and Imaging Biomarkers, 924 Westwood Blvd, Ste 615, Los Angeles, CA 90024
(A.J.G., H.J.K., H.C., B.V., J.G.)
| |
Collapse
|
2
|
Gao C, Ghodrati V, Shih SF, Wu HH, Liu Y, Nickel MD, Vahle T, Dale B, Sai V, Felker E, Surawech C, Miao Q, Finn JP, Zhong X, Hu P. Undersampling artifact reduction for free-breathing 3D stack-of-radial MRI based on a deep adversarial learning network. Magn Reson Imaging 2023; 95:70-79. [PMID: 36270417 PMCID: PMC10163826 DOI: 10.1016/j.mri.2022.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/06/2022] [Accepted: 10/14/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE Stack-of-radial MRI allows free-breathing abdominal scans, however, it requires relatively long acquisition time. Undersampling reduces scan time but can cause streaking artifacts and degrade image quality. This study developed deep learning networks with adversarial loss and evaluated the performance of reducing streaking artifacts and preserving perceptual image sharpness. METHODS A 3D generative adversarial network (GAN) was developed for reducing streaking artifacts in stack-of-radial abdominal scans. Training and validation datasets were self-gated to 5 respiratory states to reduce motion artifacts and to effectively augment the data. The network used a combination of three loss functions to constrain the anatomy and preserve image quality: adversarial loss, mean-squared-error loss and structural similarity index loss. The performance of the network was investigated for 3-5 times undersampled data from 2 institutions. The performance of the GAN for 5 times accelerated images was compared with a 3D U-Net and evaluated using quantitative NMSE, SSIM and region of interest (ROI) measurements as well as qualitative scores of radiologists. RESULTS The 3D GAN showed similar NMSE (0.0657 vs. 0.0559, p = 0.5217) and significantly higher SSIM (0.841 vs. 0.798, p < 0.0001) compared to U-Net. ROI analysis showed GAN removed streaks in both the background air and the tissue and was not significantly different from the reference mean and variations. Radiologists' scores showed GAN had a significant improvement of 1.6 point (p = 0.004) on a 4-point scale in streaking score while no significant difference in sharpness score compared to the input. CONCLUSION 3D GAN removes streaking artifacts and preserves perceptual image details.
Collapse
Affiliation(s)
- Chang Gao
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Inter-Departmental Graduate Program of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Vahid Ghodrati
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Inter-Departmental Graduate Program of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Shu-Fu Shih
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States
| | - Holden H Wu
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Inter-Departmental Graduate Program of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States; Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States
| | - Yongkai Liu
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Inter-Departmental Graduate Program of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | | | - Thomas Vahle
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Brian Dale
- MR R&D Collaborations, Siemens Medical Solutions USA, Inc., Cary, NC, United States
| | - Victor Sai
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Ely Felker
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Chuthaporn Surawech
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Department of Radiology, Division of Diagnostic Radiology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Qi Miao
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - J Paul Finn
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Inter-Departmental Graduate Program of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Xiaodong Zhong
- MR R&D Collaborations, Siemens Medical Solutions USA, Inc., Los Angeles, CA, United States
| | - Peng Hu
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Inter-Departmental Graduate Program of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States.
| |
Collapse
|
3
|
Ruchalski K, Kim HJ, Douek M, Raman S, Patel M, Sai V, Gutierrez A, Levine B, Fischer C, Allen-Auerbach M, Gupta P, Coy H, Villegas B, Brown M, Goldin J. Pretreatment visceral metastases in castration resistant metastatic prostate cancer: role in prediction versus actual site of disease progression. Cancer Imaging 2022; 22:34. [PMID: 35836271 PMCID: PMC9281063 DOI: 10.1186/s40644-022-00469-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To evaluate the anatomic site(s) of initial disease progression in patients with castration resistant metastatic prostate cancer (mCRPC) in the presence or absence of pre-treatment visceral metastases while on systemic therapy. METHODS This is a retrospective cohort study of mCRPC patients who have baseline and at least one follow up bone scan and CT chest, abdomen and pelvis (CAP). Disease progression was determined by RECIST and/or ≥ 30% increase in automated bone scan lesion area score. Kaplan-Meier plot was used to estimate the median progression free survival and log-rank tests were used to compare anatomic sites. RESULTS Of 203 patients, 61 (30%) had pre-treatment visceral metastases. Patients with baseline visceral disease were 1.5 times more likely to develop disease progression (HR = 1.53; 95% CI, 1.03-2.26). Disease progression was a result of worsening bone scan disease (42% (16/38)) versus visceral (32% (12/38)) or lymph node disease (3% (1/38)) by CT or a combination thereof (23% (9/38)). Median time to progression (TTP) did not differ by anatomic location of initial progression (p = 0.86). Development of new lesions occurred in 50% of those visceral patients with soft tissue only progression and was associated with a significantly longer TTP (3.1 months (2.8-4.3 months) than those with worsening of pre-existing lesions (1.8 months (1.6-2.7 months); p = 0.04. CONCLUSIONS Patients with pre-treatment visceral metastases in mCRPC are more likely to experience disease progression of bone disease with the initial anatomic site of progression similar to those without baseline visceral involvement.
Collapse
Affiliation(s)
| | - Hyun J Kim
- Department of Radiological Sciences, UCLA, Los Angeles, CA, USA.,UCLA Center for Computer Vision and Imaging Biomarkers, Los Angeles, CA, USA
| | - Michael Douek
- Department of Radiological Sciences, UCLA, Los Angeles, CA, USA
| | - Steven Raman
- Department of Radiological Sciences, UCLA, Los Angeles, CA, USA
| | - Maitraya Patel
- Department of Radiological Sciences, UCLA, Los Angeles, CA, USA
| | - Victor Sai
- Department of Radiological Sciences, UCLA, Los Angeles, CA, USA
| | | | - Benjamin Levine
- Department of Radiological Sciences, UCLA, Los Angeles, CA, USA
| | - Cheryce Fischer
- Department of Radiological Sciences, UCLA, Los Angeles, CA, USA
| | - Martin Allen-Auerbach
- Ahmanson Translational Theranostics Division, Department of Molecular & Medical Pharmacology, University of California Los Angeles, Los Angeles, CA, USA
| | - Pawan Gupta
- Ahmanson Translational Theranostics Division, Department of Molecular & Medical Pharmacology, University of California Los Angeles, Los Angeles, CA, USA
| | - Heidi Coy
- UCLA Center for Computer Vision and Imaging Biomarkers, Los Angeles, CA, USA
| | - Bianca Villegas
- UCLA Center for Computer Vision and Imaging Biomarkers, Los Angeles, CA, USA
| | - Matthew Brown
- Department of Radiological Sciences, UCLA, Los Angeles, CA, USA.,UCLA Center for Computer Vision and Imaging Biomarkers, Los Angeles, CA, USA
| | - Jonathan Goldin
- Department of Radiological Sciences, UCLA, Los Angeles, CA, USA.,UCLA Center for Computer Vision and Imaging Biomarkers, Los Angeles, CA, USA
| |
Collapse
|
4
|
Ruchalski K, Kim HJ, Dewan R, Douek M, Sai V, Villegas B, Wong KP, Lisberg AE, Goldman JW, Goldin J, Garon EB, Aberle DR. Inter-reader reliability of immune-specific response criteria (irRECIST & iRECIST). J Clin Oncol 2022. [DOI: 10.1200/jco.2022.40.16_suppl.e21108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
e21108 Background: RECIST 1.1 can underestimate treatment benefits of immunotherapy, with irRECIST and iRECIST accounting for atypical responses. Inter-reader discordances are known to occur in a dual reader paradigm. Our objective is to compare inter-reader reliability between RECIST 1.1, irRECIST, and iRECIST. Methods: This is a retrospective analysis of advanced NSCLC patients treated with pembrolizumab at our institution as part of the KEYNOTE-001 study. All trial imaging was interpreted by two radiologists. RECIST 1.1, irRECIST, and iRECIST categorical responses and agreement for progressive disease (PD) was compared by kappa statistic. Time to progression (TTP) or time to censor was compared between readers by paired t test. Relationship to disease progression and overall survival (OS) was assessed by log rank. Results: Of 98 patients, 77 had baseline and subsequent imaging available for 5.8 mean timepoints with 42.9 weeks of follow up. From this group, 45 patients had imaging beyond iUPD for confirmation and were analyzed. PD occurred by reader 1, reader 2 in 34, 33 patients by RECIST 1.1 (k = 0.591, CI = 0.320-0.863), 31, 29 patients by irRECIST (k = 0.501, CI = 0.234-0.768), and 27, 22 patients by iRECIST iCPD (confirmed-PD) (k = 0.690, CI = 0.485-0.896). There was no significant difference in reader agreement by RECIST 1.1, irRECIST, iRECIST (p = 0.38, 0.60, 0.26). There was a significant difference in time to progression between RECIST 1.1, irRECIST and iRECIST, with median PFS 3.4 months (2.6-4.6), 4.7 (3.5-6.8) and 8.7 (6.9-14.5) (p < 0.0001). PD by any criteria was not significantly correlated with OS. Conclusions: PD confirmation by iRECIST resulted in substantial reader agreement compared to moderate reader agreement by RECIST 1.1 and irRECIST. There were significant differences in TTP between the criteria, with iRECIST having the longest TTP. PD by each criteria did not correlate with a significant difference in OS.[Table: see text]
Collapse
Affiliation(s)
| | - Hyun J. Kim
- Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA
| | - Rohit Dewan
- UCLA Department of Radiological Sciences, Los Angeles, CA
| | - Michael Douek
- University of California Los Angeles Department of Radiological Sciences, Los Angeles, CA
| | - Victor Sai
- UCLA Department of Radiological Sciences, Los Angeles, CA
| | - Bianca Villegas
- UCLA Center for Computer Vision and Imaging Biomarkers, Los Angeles, CA
| | - Koon-Pong Wong
- UCLA Center for Computer Vision and Imaging Biomarkers, Los Angeles, CA
| | - Aaron E. Lisberg
- Department of Medicine, Division of Hematology/Oncology, UCLA, Los Angeles, CA
| | | | | | | | | |
Collapse
|
5
|
Ruchalski K, Dewan R, Sai V, McIntosh LJ, Braschi-Amirfarzan M. Imaging response assessment for oncology: An algorithmic approach. Eur J Radiol Open 2022; 9:100426. [PMID: 35693043 PMCID: PMC9184854 DOI: 10.1016/j.ejro.2022.100426] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 01/04/2023] Open
Abstract
Treatment response assessment by imaging plays a vital role in evaluating changes in solid tumors during oncology therapeutic clinical trials. Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 is the reference standard imaging response criteria and provides details regarding image acquisition, image interpretation and categorical response classification. While RECIST 1.1 is applied for the majority of clinical trials in solid tumors, other criteria and modifications have been introduced when RECIST 1.1 outcomes may be incomplete. Available criteria beyond RECIST 1.1 can be explored in an algorithmic fashion dependent on imaging modality, tumor type and method of treatment. Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST) is available for use with PET/CT. Modifications to RECIST 1.1 can be tumor specific, including mRECIST for hepatocellular carcinoma and mesothelioma. Choi criteria for gastrointestinal stromal tumors incorporate tumor density with alterations to categorical response thresholds. Prostate Cancer Working Group 3 (PCWG3) imaging criteria combine RECIST 1.1 findings with those of bone scans. In addition, multiple response criteria have been created to address atypical imaging responses in immunotherapy.
Collapse
|
6
|
Ruchalski K, Braschi-Amirfarzan M, Douek M, Sai V, Gutierrez A, Dewan R, Goldin J. A Primer on RECIST 1.1 for Oncologic Imaging in Clinical Drug Trials. Radiol Imaging Cancer 2021; 3:e210008. [PMID: 33988475 DOI: 10.1148/rycan.2021210008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Drug discovery and approval in oncology is mediated by the use of imaging to evaluate drug efficacy in clinical trials. Imaging is performed while patients receive therapy to evaluate their response to treatment. Response criteria, specifically Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1), are standardized and can be used at different time points to classify response into the categories of complete response, partial response, stable disease, or disease progression. At the trial level, categorical responses for all patients are summated into image-based trial endpoints. These outcome measures, including objective response rate (ORR) and progression-free survival (PFS), are characteristics that can be derived from imaging and can be used as surrogates for overall survival (OS). Similar to OS, ORR and PFS describe the efficacy of a drug. U.S. Food and Drug Administration (FDA) regulatory approval requires therapies to demonstrate direct evidence of clinical benefit, such as improved OS. However, multiple programs have been created to expedite drug approval for life-threatening illnesses, including advanced cancer. ORR and PFS have been accepted by the FDA as adequate predictors of OS on which to base drug approval decisions, thus substantially shortening the time and cost of drug development (1). Use of imaging surrogate markers for drug approval has become increasingly common, accounting for more than 90% of approvals through the Accelerated Approval Program and allowing for use of many therapies which have altered the course of cancer. Keywords: Oncology, Tumor Response RSNA, 2021.
Collapse
Affiliation(s)
- Kathleen Ruchalski
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095-1721 (K.R., M.D., V.S., A.G., R.D., J.G.); and Department of Radiology, Beth Israel Lahey Health, Burlington, Mass (M.B.A.)
| | - Marta Braschi-Amirfarzan
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095-1721 (K.R., M.D., V.S., A.G., R.D., J.G.); and Department of Radiology, Beth Israel Lahey Health, Burlington, Mass (M.B.A.)
| | - Michael Douek
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095-1721 (K.R., M.D., V.S., A.G., R.D., J.G.); and Department of Radiology, Beth Israel Lahey Health, Burlington, Mass (M.B.A.)
| | - Victor Sai
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095-1721 (K.R., M.D., V.S., A.G., R.D., J.G.); and Department of Radiology, Beth Israel Lahey Health, Burlington, Mass (M.B.A.)
| | - Antonio Gutierrez
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095-1721 (K.R., M.D., V.S., A.G., R.D., J.G.); and Department of Radiology, Beth Israel Lahey Health, Burlington, Mass (M.B.A.)
| | - Rohit Dewan
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095-1721 (K.R., M.D., V.S., A.G., R.D., J.G.); and Department of Radiology, Beth Israel Lahey Health, Burlington, Mass (M.B.A.)
| | - Jonathan Goldin
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095-1721 (K.R., M.D., V.S., A.G., R.D., J.G.); and Department of Radiology, Beth Israel Lahey Health, Burlington, Mass (M.B.A.)
| |
Collapse
|
7
|
Armstrong V, Tan N, Sekhar A, Richardson ML, Kanne JP, Sai V, Chernyak V, Godwin JD, Tammisetti VS, Eberhardt SC, Henry TS. Peer Learning Through Multi-Institutional Web-based Case Conferences: Perceived Value (and Challenges) From Abdominal, Cardiothoracic, and Musculoskeletal Radiology Case Conference Participants. Acad Radiol 2020; 27:1641-1646. [PMID: 31848074 DOI: 10.1016/j.acra.2019.11.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Revised: 11/02/2019] [Accepted: 11/12/2019] [Indexed: 11/19/2022]
Abstract
RATIONALE AND OBJECTIVES Peer learning is a case-based group-learning model intended to improve performance. In this descriptive paper, we describe multi-institutional, multi-subspecialty, web-based radiology case conferences and summarize the participants' experiences. MATERIALS AND METHODS A semi-structured, 27-question survey was administered to radiologists participating in abdominal, cardiothoracic, and musculoskeletal case conferences. Survey questions included demographics, perceived educational value and challenges experienced. Survey question formats were continuous, binary, five-point Likert scale or text-based. The measures of central tendencies, proportions of responses and patterns were tabulated. RESULTS From 57 responders, 12/57 (21.1%) were abdominal, 16/57 (28.1%) were cardiothoracic, and 29/57 (50.8%) were musculoskeletal conference participants; 50/56 (89.3%) represented academic practice. Median age was 45 years (range 35-74); 43/57 (75.4%) were male. Geographically, 16/52 (30.8%) of participants were from the East Coast, 16/52 (30.8%) Midwest, 18/52 (34.6%) West Coast, and 2/52 (3.8%) International. The median reported educational value was 5/5 (interquartile range 5-5). Benefits of the case conference included education (50/95, 52.6%) and networking (39/95, 41.1%). Participants reported presenting the following cases: "great call" 32/48 (66.7%), learning opportunity 32/48 (66.7%), new knowledge 41/49 (83.7%), "zebras" 46/49 (93.9%), and procedural-based 16/46 (34.8%). All 51/51 (100%) of responders reportedly gained new knowledge, 49/51 (96.1%) became more open to group discussion, 34/51 (66.7%) changed search patterns, and 50/51 (98%) would continue to participate. Reported challenges included time zone differences and support from departments for a protected time to participate. CONCLUSION Peer learning through multi-institutional case conferences provides educational and networking opportunities. Current challenges and desires include having department-supported protected time and ability to receive continuing medical education credit.
Collapse
Affiliation(s)
| | - Nelly Tan
- Loma Linda University Medical Center, Loma Linda, California.
| | - Aarti Sekhar
- Emory University School of Medicine, Atlanta, Georgia
| | | | | | | | | | | | | | | | | |
Collapse
|
8
|
Ruchalski K, Douek M, Raman S, Patel M, Gutierrez A, Sai V, Levine BD, Allen-Auerbach M, Gupta P, Kim HJ, Villegas B, Goldin J. Role of soft tissue metastases on tumor progression in castrate-resistant metastatic prostate cancer. J Clin Oncol 2020. [DOI: 10.1200/jco.2020.38.6_suppl.214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
214 Background: While visceral metastases in castrate resistant prostate cancer (mCRPC) are a negative prognostic factor, patients may still derive treatment benefits. Given varying anatomic sites of tumor may display differing treatment responses we investigated the role of soft tissue metastases in progressive disease (PD) by anatomic site. Methods: A retrospective review of anonymized patients enrolled in phase 3 clinical trials was performed. Patients were excluded if lacked Tc bone scan or CT chest, abdomen and pelvis (CT CAP) on trial. Subjects were included if they had soft tissue disease per RECIST 1.1. Bone disease was defined by automated BSLA criteria. All CT CAPs per clinical trial protocol were assessed by RECIST 1.1. Soft tissue PD was noted as target|nontarget lesion growth and/or presence of new lesions by anatomic site. Bone PD was defined by >20% increase in BSLA score. Results: Of 322 total patients, 138 met criteria of soft tissue disease at baseline. Baseline anatomic sites of metastases per RECIST included: visceral (74, 53.6%), lymph node only (52, 37.7%) and soft tissue bone (12, 7.1%). PD occurred in 46 patients with baseline soft tissue disease; with PD first detected by BSLA (23, 16.7 %), RECIST (19, 13.8 %) or with PD occurring simultaneously by BSLA and RECIST (4, 2.9%). Of the 138 patients with baseline soft tissue disease, PD occurred by RECIST in 25 patients. This occurred due to worsening visceral (11, 68.8%), lymph node (4, 25.0%) or soft tissue bone (1, 6.3%) disease. PD by new lesions only occurred in 9 patients (36.0%), as a result of 77.8% visceral and 22.2% lymph node. Conclusions: In mCRPC with soft tissue disease at baseline, progression by BSLA bone score still accounts for a large portion of PD. Further analyses must be performed to better differentiate disease phenotypes amongst mCRPC with combined visceral/osseous disease.
Collapse
Affiliation(s)
| | - Michael Douek
- University of California Los Angeles Department of Radiological Sciences, Los Angeles, CA
| | - Steven Raman
- David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA
| | - Maitraya Patel
- UCLA Department of Radiological Sciences, Los Angeles, CA
| | | | - Victor Sai
- UCLA Department of Radiological Sciences, Los Angeles, CA
| | - Benjamin D. Levine
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | | | - Pawan Gupta
- Department of Nuclear Medicine, University of California, Los Angeles, CA
| | - Hyun J. Kim
- Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA
| | - Bianca Villegas
- UCLA Center for Computer Vision and Imaging Biomarkers, Los Angeles, CA
| | | |
Collapse
|
9
|
Coy HJ, Douek ML, Ruchalski K, Kim HJ, Gutierrez A, Patel M, Sai V, Margolis DJA, Kaplan A, Brown M, Goldin J, Raman SS. Components of Radiologic Progressive Disease Defined by RECIST 1.1 in Patients with Metastatic Clear Cell Renal Cell Carcinoma. Radiology 2019; 292:103-109. [PMID: 31084479 DOI: 10.1148/radiol.2019182922] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background Progression-free survival (PFS) determined by Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1) is the reference standard to assess efficacy of treatments in patients with clear cell renal cell carcinoma. Purpose To assess the most common components of radiologic progressive disease as defined by RECIST 1.1 in patients with clear cell renal cell carcinoma and how the progression events impact PFS. Materials and Methods This secondary analysis of the phase III METEOR trial conducted between 2013 and 2014 included patients with metastatic clear cell renal cell carcinoma, with at least one target lesion at baseline and one follow-up time point, who were determined according to RECIST 1.1 to have progressive disease. A chest, abdominal, and pelvic scan were acquired at each time point. Kruskal-Wallis analysis was used to test differences in median PFS among the RECIST 1.1 progression events. The Holm-Bonferroni method was used to compare the median PFS of the progression events for the family-wise error rate of 5% to adjust P values for multiple comparisons. Results Of the 395 patients (296 men, 98 women, and one patient with sex not reported; mean age, 61 years ± 10), 73 (18.5%) had progression due to non-target disease, 105 (26.6%) had new lesions, and 126 (31.9%) had progression of target lesions (defined by an increase in the sum of diameters). Patients with progression of non-target disease and those with new lesions had shorter PFS than patients with progression defined by the target lesions (median PFS, 2.8 months [95% confidence interval {CI}: 1.9 months, 3.7 months] and 3.6 months [95% CI: 3.3 months, 3.7 months] vs 5.4 months [95% CI: 5.0 months, 5.5 months], respectively [P < .01]). Conclusion The most common causes for radiologic progression of renal cell carcinoma were based on non-target disease and new lesions rather than change in target lesions, despite this being considered uncommon in the Response Evaluation Criteria in Solid Tumors version 1.1 literature. © RSNA, 2019 See also the editorial by Kuhl in this issue.
Collapse
Affiliation(s)
- Heidi J Coy
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, 924 Westwood Blvd, Suite 615, Los Angeles, CA 90049 (H.J.C., M.L.D., K.R., H.J.K., A.G., M.P., V.S., A.K., M.B., J.G., S.S.R.); Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA (H.J.K.); Department of Radiology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY (D.M.); Department of Urology, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.); Department of Surgery, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.)
| | - Michael L Douek
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, 924 Westwood Blvd, Suite 615, Los Angeles, CA 90049 (H.J.C., M.L.D., K.R., H.J.K., A.G., M.P., V.S., A.K., M.B., J.G., S.S.R.); Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA (H.J.K.); Department of Radiology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY (D.M.); Department of Urology, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.); Department of Surgery, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.)
| | - Kathleen Ruchalski
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, 924 Westwood Blvd, Suite 615, Los Angeles, CA 90049 (H.J.C., M.L.D., K.R., H.J.K., A.G., M.P., V.S., A.K., M.B., J.G., S.S.R.); Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA (H.J.K.); Department of Radiology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY (D.M.); Department of Urology, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.); Department of Surgery, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.)
| | - Hyun J Kim
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, 924 Westwood Blvd, Suite 615, Los Angeles, CA 90049 (H.J.C., M.L.D., K.R., H.J.K., A.G., M.P., V.S., A.K., M.B., J.G., S.S.R.); Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA (H.J.K.); Department of Radiology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY (D.M.); Department of Urology, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.); Department of Surgery, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.)
| | - Antonio Gutierrez
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, 924 Westwood Blvd, Suite 615, Los Angeles, CA 90049 (H.J.C., M.L.D., K.R., H.J.K., A.G., M.P., V.S., A.K., M.B., J.G., S.S.R.); Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA (H.J.K.); Department of Radiology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY (D.M.); Department of Urology, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.); Department of Surgery, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.)
| | - Maitrya Patel
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, 924 Westwood Blvd, Suite 615, Los Angeles, CA 90049 (H.J.C., M.L.D., K.R., H.J.K., A.G., M.P., V.S., A.K., M.B., J.G., S.S.R.); Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA (H.J.K.); Department of Radiology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY (D.M.); Department of Urology, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.); Department of Surgery, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.)
| | - Victor Sai
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, 924 Westwood Blvd, Suite 615, Los Angeles, CA 90049 (H.J.C., M.L.D., K.R., H.J.K., A.G., M.P., V.S., A.K., M.B., J.G., S.S.R.); Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA (H.J.K.); Department of Radiology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY (D.M.); Department of Urology, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.); Department of Surgery, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.)
| | - Daniel J A Margolis
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, 924 Westwood Blvd, Suite 615, Los Angeles, CA 90049 (H.J.C., M.L.D., K.R., H.J.K., A.G., M.P., V.S., A.K., M.B., J.G., S.S.R.); Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA (H.J.K.); Department of Radiology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY (D.M.); Department of Urology, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.); Department of Surgery, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.)
| | - Andrew Kaplan
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, 924 Westwood Blvd, Suite 615, Los Angeles, CA 90049 (H.J.C., M.L.D., K.R., H.J.K., A.G., M.P., V.S., A.K., M.B., J.G., S.S.R.); Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA (H.J.K.); Department of Radiology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY (D.M.); Department of Urology, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.); Department of Surgery, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.)
| | - Matthew Brown
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, 924 Westwood Blvd, Suite 615, Los Angeles, CA 90049 (H.J.C., M.L.D., K.R., H.J.K., A.G., M.P., V.S., A.K., M.B., J.G., S.S.R.); Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA (H.J.K.); Department of Radiology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY (D.M.); Department of Urology, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.); Department of Surgery, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.)
| | - Jonathan Goldin
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, 924 Westwood Blvd, Suite 615, Los Angeles, CA 90049 (H.J.C., M.L.D., K.R., H.J.K., A.G., M.P., V.S., A.K., M.B., J.G., S.S.R.); Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA (H.J.K.); Department of Radiology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY (D.M.); Department of Urology, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.); Department of Surgery, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.)
| | - Steven S Raman
- From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, 924 Westwood Blvd, Suite 615, Los Angeles, CA 90049 (H.J.C., M.L.D., K.R., H.J.K., A.G., M.P., V.S., A.K., M.B., J.G., S.S.R.); Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, CA (H.J.K.); Department of Radiology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY (D.M.); Department of Urology, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.); Department of Surgery, David Geffen School of Medicine at UCLA, Ronald Reagan UCLA Medical Center, Los Angeles, CA (S.S.R.)
| |
Collapse
|
10
|
Babu R, Venkatachalapathy E, Sai V. Hydronephrosis severity score: an objective assessment of hydronephrosis severity in children-a preliminary report. J Pediatr Urol 2019; 15:68.e1-68.e6. [PMID: 30392886 DOI: 10.1016/j.jpurol.2018.09.020] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Accepted: 09/28/2018] [Indexed: 11/28/2022]
Abstract
UNLABELLED The main challenge in the management of antenatally diagnosed hydronephrosis and ureteropelvic junction obstruction (UPJO) is to differentiate the one that is likely to resolve from the pathological one. In this study, a new hydronephrosis severity score (HSS), combining ultrasonographic and renographic parameters, has been developed. Hydronephrosis severity score was analyzed with regard to its usefulness in assessing the severity of UPJO, postoperative resolution, and interobserver reliability. METHODS Hydronephrosis severity score was devised with three parameters: differential renal function (DRF), drainage curve pattern, and ultrasonogram grade (Table 1). Hydronephrosis severity score ranges were divided as 0-4, mild; 5-8, moderate; and 9-12, severe and compared with clinical outcomes (resolution, persistence, or surgical intervention) by retrospective case record review of children with unilateral UPJO. Among those who underwent surgery, surgical outcomes were compared with changes in HSS at 6-month follow-up. Hydronephrosis severity score was computed by three observers, and interobserver variability was calculated. RESULTS A total of 125 case records (male:female = 93:32; right:left = 44:81) were analyzed. Among the patients analyzed, none (0/59) with HSS 0-4 warranted surgery, whereas 1 of 35 patients with HSS 5-8 underwent surgery, and all (31/31) with HSS 9-12 underwent surgery (P = 0.001). Overall, hydronephrosis resolved in 65, persisted in 28, and required surgery in 32 patients. Mean (standard deviation) HSS was 2.1 (0.75) in whom it resolved, 6.2 (0.78) in whom it persisted, and 10.2 (0.79) in those who underwent surgery (analysis of variance P = 0.001). Among those who underwent surgery, a better recovery of HSS was noted in younger infants (aged 2-5 months) with higher pre-operative DRF. There was 94.4% median agreement between radiologists and the surgeon (kappa 0.851), indicating a very good interobserver agreement. DISCUSSION Loss of DRF on progressive renograms remains the accepted criterion of significant UPJO although the lost function does not always recover after pyeloplasty. Newer scoring systems keep evolving to predict the need for surgery as well as assess resolution of UPJO, and the study's preliminary report suggests that HSS could turn out to be one such useful tool. In this study, those who deteriorated were the ones with HSS ≥9. One can use this as a criterion and decide on intervention before DRF deterioration. Hydronephrosis severity score could also be applied as an objective parameter for quantifying improvement/deterioration after surgery and comparing outcomes across centers. The drawbacks of the present study are its small size and the retrospective nature. Further prospective studies are required to validate the usefulness of HSS.
Collapse
Affiliation(s)
- R Babu
- Department of Pediatric Urology, Sri Ramachandra Medical College and Research Institute, Porur, Chennai 600116, India.
| | - E Venkatachalapathy
- Department of Nuclear Medicine, Sri Ramachandra Medical College and Research Institute, Porur, Chennai 600116, India
| | - V Sai
- Department of Radiology, Sri Ramachandra Medical College and Research Institute, Porur, Chennai 600116, India
| |
Collapse
|
11
|
Keshari KR, Wilson DM, Sai V, Bok R, Jen KY, Larson P, Van Criekinge M, Kurhanewicz J, Wang ZJ. Noninvasive in vivo imaging of diabetes-induced renal oxidative stress and response to therapy using hyperpolarized 13C dehydroascorbate magnetic resonance. Diabetes 2015; 64:344-52. [PMID: 25187363 PMCID: PMC4303960 DOI: 10.2337/db13-1829] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Oxidative stress has been proposed to be a unifying cause for diabetic nephropathy and a target for novel therapies. Here we apply a new endogenous reduction-oxidation (redox) sensor, hyperpolarized (HP) (13)C dehydroascorbate (DHA), in conjunction with MRI to noninvasively interrogate the renal redox capacity in a mouse diabetes model. The diabetic mice demonstrate an early decrease in renal redox capacity, as shown by the lower in vivo HP (13)C DHA reduction to the antioxidant vitamin C (VitC), prior to histological evidence of nephropathy. This correlates with lower tissue reduced glutathione (GSH) concentration and higher NADPH oxidase 4 (Nox4) expression, consistent with increased superoxide generation and oxidative stress. ACE inhibition restores the HP (13)C DHA reduction to VitC with concomitant normalization of GSH concentration and Nox4 expression in diabetic mice. HP (13)C DHA enables rapid in vivo assessment of altered redox capacity in diabetic renal injury and after successful treatment.
Collapse
Affiliation(s)
- Kayvan R Keshari
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY Molecular Pharmacology and Chemistry Program, Memorial Sloan Kettering Cancer Center, New York, NY
| | - David M Wilson
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | - Victor Sai
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | - Robert Bok
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | - Kuang-Yu Jen
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | - Peder Larson
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | - Mark Van Criekinge
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | - John Kurhanewicz
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | - Zhen J Wang
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| |
Collapse
|
12
|
|
13
|
Sai V, Rakow-Penner R, Yeh BM, Coakley FV, Westphalen AC, Webb EM, Wang ZJ. Renal cyst pseudoenhancement at 16- and 64-dector row MDCT. Clin Imaging 2013; 37:520-5. [DOI: 10.1016/j.clinimag.2012.09.016] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2012] [Revised: 08/26/2012] [Accepted: 09/20/2012] [Indexed: 11/17/2022]
|
14
|
Keshari KR, Sai V, Wang ZJ, Vanbrocklin HF, Kurhanewicz J, Wilson DM. Hyperpolarized [1-13C]dehydroascorbate MR spectroscopy in a murine model of prostate cancer: comparison with 18F-FDG PET. J Nucl Med 2013; 54:922-8. [PMID: 23575993 DOI: 10.2967/jnumed.112.115402] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
UNLABELLED Reduction and oxidation (redox) chemistry is increasingly implicated in cancer pathogenesis. To interrogate the redox status of prostate tumors noninvasively, we developed hyperpolarized [1-(13)C]dehydroascorbate ((13)C-DHA), the oxidized form of vitamin C, as an MR probe. In a model of transgenic adenocarcinoma of the mouse prostate (TRAMP), increased reduction of hyperpolarized (13)C-DHA to vitamin C was observed in tumor, as compared with normal prostate and surrounding benign tissue. We hypothesized that this difference was due to higher concentrations of glutathione and increased transport of hyperpolarized (13)C-DHA via the glucose transporters (GLUT1, GLUT3, and GLUT4) in TRAMP tumor. To test these hypotheses, hyperpolarized (13)C-DHA MR spectroscopy (MRS) and (18)F-FDG PET were applied as complementary technologies in the TRAMP model. METHODS Late-stage TRAMP tumors (>4 cm(3)) were studied at similar time points (MR studies conducted < 24 h after PET) in fasting mice by (18)F-FDG PET and hyperpolarized (13)C-DHA MR imaging on a small-animal PET/CT scanner and a (1)H/(3)C 3-T MR scanner. PET data were processed using open-source AMIDE software to compare the standardized uptake values of tumor with those of surrounding muscle, and (13)C-DHA MRS data were processed using custom software to compare the metabolite ratios (vitamin C/[vitamin C + (13)C-DHA]). After in vivo studies, the tumor glutathione concentrations were determined using a spectrophotometric assay, and thiol staining was performed using mercury orange. Real-time polymerase chain reaction was used to evaluate the relevant transporters GLUT1, GLUT3, and GLUT4 and vitamin C transporters SVCT1 and SVCT2. GLUT1 was also evaluated by immunohistochemistry. RESULTS The average metabolite ratio was 0.28 ± 0.02 in TRAMP tumor, versus 0.11 ± 0.02 in surrounding benign tissue (n = 4), representing a 2.5-fold difference. The corresponding tumor-to-nontumor (18)F-FDG uptake ratio was 3.0. The total glutathione was 5.1 ± 0.4 mM in tumor and 1.0 ± 0.2 mM in normal prostate, whereas reduced glutathione was 2.0 ± 0.3 mM and 0.8 ± 0.3 mM, respectively, corresponding to a 2.5-fold difference. In TRAMP tumor, mercury orange staining demonstrated increased thiols. Real-time polymerase chain reaction showed no significant difference in GLUT1 messenger RNA between TRAMP tumor and normal prostate, with immunohistochemistry (anti-GLUT1) also showing comparable staining. CONCLUSION Both hyperpolarized (13)C-DHA and (18)F-FDG provide similar tumor contrast in the TRAMP model. Our findings suggest that the mechanism of in vivo hyperpolarized (13)C-DHA reduction and the resulting tumor contrast correlates most strongly with glutathione concentration. In the TRAMP model, GLUT1 is not significantly upregulated and is unlikely to account for the contrast obtained using hyperpolarized (13)C-DHA or (18)F-FDG.
Collapse
Affiliation(s)
- Kayvan R Keshari
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California 94143, USA
| | | | | | | | | | | |
Collapse
|
15
|
Wen JC, Sai V, Straatsma BR, McCannel TA. Radiation-related cancer risk associated with surveillance imaging for metastasis from choroidal melanoma. JAMA Ophthalmol 2013; 131:56-61. [PMID: 23307209 DOI: 10.1001/jamaophthalmol.2013.564] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
OBJECTIVE To estimate the lifetime attributable risk of cancer associated with whole-body positron emission tomography (PET)/computed tomography (CT) and with CT of the chest, abdomen, and pelvis if performed at various frequencies and for different durations for surveillance of patients with primary choroidal or ciliary body melanoma for distant metastasis. METHODS Effective radiation doses for whole-body CT and for CT of the chest, abdomen, and pelvis were calculated using Monte Carlo simulation studies. The effective dose of the PET scan was estimated by multiplying fludeoxyglucose F18 radioactivity with dose coefficients. Lifetime attributable risks of cancer were calculated using the approach described in the Biological Effects of Ionizing Radiation VII report. RESULTS For a 50-year-old patient, an annual CT of the chest, abdomen, and pelvis for 10 years carries an estimated lifetime attributable risk of cancer of 0.9% for male patients and 1.3% for female patients, whereas an annual PET/CT each year for 10 years carries an estimated lifetime attributable risk of cancer of 1.6% for male patients and 1.9% for female patients. Lifetime risk was found to be higher in younger, female patients. The lifetime attributable risk of cancer was estimated to be as high as 7.9% for a 20-year-old female patient receiving a PET/CT scan every 6 months for 10 years. CONCLUSIONS Aggressive surveillance protocols incorporating CT scanning or PET/CT scanning for detection of metastasis from primary choroidal or ciliary body melanoma appear to confer a significant substantial risk of a secondary malignant tumor in patients who do not succumb to metastatic melanoma within the first few posttreatment years.
Collapse
Affiliation(s)
- Joanne C Wen
- Jules Stein Eye Institute, University of California, Los Angeles, CA 90095-7000, USA
| | | | | | | |
Collapse
|
16
|
Muthamilselvan S, Vinoth PN, Vilvanathan V, Ninan B, Amboiram P, Sai V, Anand V, Scott JX. Hepatic haemangioma of infancy: role of propranolol. ACTA ACUST UNITED AC 2011; 30:335-8. [PMID: 21118629 DOI: 10.1179/146532810x12858955921393] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
Abstract
A newborn girl with a haemangioma of the liver failed to respond to cortico-steroid therapy. Ultrasonic evidence of the haemangioma disappeared after 2 months of treatment with propranolol. Propranolol, a non-selective beta-blocker, is a promising therapy in the management of haemangiomas.
Collapse
Affiliation(s)
- S Muthamilselvan
- Departments of Pediatrics, Sri Ramachandra Medical Centre, Porur, Chennai, India
| | | | | | | | | | | | | | | |
Collapse
|
17
|
Pope WB, Kim HJ, Huo J, Alger J, Brown MS, Gjertson D, Sai V, Young JR, Tekchandani L, Cloughesy T, Mischel PS, Lai A, Nghiemphu P, Rahmanuddin S, Goldin J. Recurrent Glioblastoma Multiforme: ADC Histogram Analysis Predicts Response to Bevacizumab Treatment. Radiology 2009; 252:182-9. [DOI: 10.1148/radiol.2521081534] [Citation(s) in RCA: 275] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
18
|
Ledezma CJ, Chen W, Sai V, Freitas B, Cloughesy T, Czernin J, Pope W. 18F-FDOPA PET/MRI fusion in patients with primary/recurrent gliomas: initial experience. Eur J Radiol 2008; 71:242-8. [PMID: 18511228 DOI: 10.1016/j.ejrad.2008.04.018] [Citation(s) in RCA: 88] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2007] [Revised: 03/05/2008] [Accepted: 04/21/2008] [Indexed: 11/28/2022]
Abstract
BACKGROUND AND PURPOSE (18)F-FDOPA PET demonstrates higher sensitivity and specificity for gliomas than traditional [(18)F] FDG PET imaging. However, PET provides limited anatomic localization. The purpose of this study was to determine whether (18)F-FDOPA PET/MRI fusion can provide precise anatomic localization of abnormal tracer uptake and how this activity corresponds to MR signal abnormality. METHODS Two groups of patients were analyzed. Group I consisted of 21 patients who underwent (18)F-FDOPA PET and MRI followed by craniotomy for tumor resection. Group II consisted of 70 patients with a pathological diagnosis of glioma that had (18)F-FDOPA PET and MRI but lacked additional pathologic follow-up. Fused (18)F-FDOPA PET and MRI images were analyzed for concordance and correlated with histopathologic data. RESULTS Fusion technology facilitated precise anatomical localization of (18)F-FDOPA activity. In group I, all 21 cases showed pathology-confirmed tumor. Of these, (18)F-FDOPA scans were positive in 9/10 (90%) previously unresected tumors, and 11/11 (100%) of recurrent tumors. Of the 70 patients in group II, concordance between MRI and (18)F-FDOPA was found in 49/54 (90.1%) of patients with sufficient follow-up; in the remaining 16 patients concordance could not be determined due to lack of follow-up. (18)F-FDOPA labeling was comparable in both high- and low-grade gliomas and identified both enhancing and non-enhancing tumor equally well. In some cases, (18)F-FDOPA activity preceded tumor detection on MRI. CONCLUSION (18)F-FDOPA PET/MRI fusion provides precise anatomic localization of tracer uptake and labels enhancing and non-enhancing tumor well. In a small minority of cases, (18)F-FDOPA activity may identify tumor not visible on MRI.
Collapse
Affiliation(s)
- Carlos J Ledezma
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 10833 Le Conte Avenue, Los Angeles, CA 90095, USA.
| | | | | | | | | | | | | |
Collapse
|