Lesion segmentation on 18F-fluciclovine PET/CT images using deep learning

Background and purposeA novel radiotracer, 18F-fluciclovine (anti-3-18F-FACBC), has been demonstrated to be associated with significantly improved survival when it is used in PET/CT imaging to guide postprostatectomy salvage radiotherapy for prostate cancer. We aimed to investigate the feasibility of using a deep learning method to automatically detect and segment lesions on 18F-fluciclovine PET/CT images.Materials and methodsWe retrospectively identified 84 patients who are enrolled in Arm B of the Emory Molecular Prostate Imaging for Radiotherapy Enhancement (EMPIRE-1) trial. All 84 patients had prostate adenocarcinoma and underwent prostatectomy and 18F-fluciclovine PET/CT imaging with lesions identified and delineated by physicians. Three different neural networks with increasing levels of complexity (U-net, Cascaded U-net, and a cascaded detection segmentation network) were trained and tested on the 84 patients with a fivefold cross-validation strategy and a hold-out test, using manual contours as the ground truth. We also investigated using both PET and CT or using PET only as input to the neural network. Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), center-of-mass distance (CMD), and volume difference (VD) were used to quantify the quality of segmentation results against ground truth contours provided by physicians.ResultsAll three deep learning methods were able to detect 144/155 lesions and 153/155 lesions successfully when PET+CT and PET only, respectively, served as input. Quantitative results demonstrated that the neural network with the best performance was able to segment lesions with an average DSC of 0.68 ± 0.15 and HD95 of 4 ± 2 mm. The center of mass of the segmented contours deviated from physician contours by approximately 2 mm on average, and the volume difference was less than 1 cc. The novel network proposed by us achieves the best performance compared to current networks. The addition of CT as input to the neural network contributed to more cases of failure (DSC = 0), and among those cases of DSC > 0, it was shown to produce no statistically significant difference with the use of only PET as input for our proposed method.ConclusionQuantitative results demonstrated the feasibility of the deep learning methods in automatically segmenting lesions on 18F-fluciclovine PET/CT images. This indicates the great potential of 18F-fluciclovine PET/CT combined with deep learning for providing a second check in identifying lesions as well as saving time and effort for physicians in contouring

An electronic application for rapidly calculating Charlson comorbidity score

BACKGROUND: Uncertainty regarding comorbid illness, and ability to tolerate aggressive therapy has led to minimal enrollment of elderly cancer patients into clinical trials and often substandard treatment. Increasingly, comorbid illness scales have proven useful in identifying subgroups of elderly patients who are more likely to tolerate and benefit from aggressive therapy. Unfortunately, the use of such scales has yet to be widely integrated into either clinical practice or clinical trials research. METHODS: This article reviews evidence for the validity of the Charlson Comorbidity Index (CCI) in oncology and provides a Microsoft Excel (MS Excel) Macro for the rapid and accurate calculation of CCI score. The interaction of comorbidity and malignant disease and the validation of the Charlson Index in oncology are discussed. RESULTS: The CCI score is based on one year mortality data from internal medicine patients admitted to an inpatient setting and is the most widely used comorbidity index in oncology. An MS Excel Macro file was constructed for calculating the CCI score using Microsoft Visual Basic. The Macro is provided for download and dissemination. The CCI has been widely used and validated throughout the oncology literature and has demonstrated utility for most major cancers. The MS Excel CCI Macro provides a rapid method for calculating CCI score with or without age adjustments. The calculator removes difficulty in score calculation as a limitation for integration of the CCI into clinical research. The simple nature of the MS Excel CCI Macro and the CCI itself makes it ideal for integration into emerging electronic medical records systems. CONCLUSIONS: The increasing elderly population and concurrent increase in oncologic disease has made understanding the interaction between age and comorbid illness on life expectancy increasingly important. The MS Excel CCI Macro provides a means of increasing the use of the CCI scale in clinical research with the ultimate goal of improving determination of optimal treatments for elderly cancer patients

Optimum imaging strategies for advanced prostate cancer: ASCO guideline

PURPOSE Provide evidence- and expert-based recommendations for optimal use of imaging in advanced prostate cancer. Due to increases in research and utilization of novel imaging for advanced prostate cancer, this guideline is intended to outline techniques available and provide recommendations on appropriate use of imaging for specified patient subgroups. METHODS An Expert Panel was convened with members from ASCO and the Society of Abdominal Radiology, American College of Radiology, Society of Nuclear Medicine and Molecular Imaging, American Urological Association, American Society for Radiation Oncology, and Society of Urologic Oncology to conduct a systematic review of the literature and develop an evidence-based guideline on the optimal use of imaging for advanced prostate cancer. Representative index cases of various prostate cancer disease states are presented, including suspected high-risk disease, newly diagnosed treatment-naïve metastatic disease, suspected recurrent disease after local treatment, and progressive disease while undergoing systemic treatment. A systematic review of the literature from 2013 to August 2018 identified fully published English-language systematic reviews with or without meta-analyses, reports of rigorously conducted phase III randomized controlled trials that compared $ 2 imaging modalities, and noncomparative studies that reported on the efficacy of a single imaging modality. RESULTS A total of 35 studies met inclusion criteria and form the evidence base, including 17 systematic reviews with or without meta-analysis and 18 primary research articles. RECOMMENDATIONS One or more of these imaging modalities should be used for patients with advanced prostate cancer: conventional imaging (defined as computed tomography [CT], bone scan, and/or prostate magnetic resonance imaging [MRI]) and/or next-generation imaging (NGI), positron emission tomography [PET], PET/CT, PET/MRI, or whole-body MRI) according to the clinical scenario

Differences in failure-free survival after salvage radiotherapy guided by conventional imaging versus 18F-fluciclovine PET/CT in postprostatectomy patients : a post hoc substratification analysis of the EMPIRE-1 trial

Please read abstract in the article.The National Institutes of Health and Blue Earth Diagnostics.http://jnm.snmjournals.orghj2023Nuclear Medicin

Biological-effective versus conventional dose volume histograms correlated with late genitourinary and gastrointestinal toxicity after external beam radiotherapy for prostate cancer: a matched pair analysis

BACKGROUND: To determine whether the dose-volume histograms (DVH's) for the rectum and bladder constructed using biological-effective dose (BED-DVH's) better correlate with late gastrointestinal (GI) and genitourinary (GU) toxicity after treatment with external beam radiotherapy for prostate cancer than conventional DVH's (C-DVH's). METHODS: The charts of 190 patients treated with external beam radiotherapy with a minimum follow-up of 2 years were reviewed. Six patients (3.2%) were found to have RTOG grade 3 GI toxicity, and similarly 6 patients (3.2%) were found to have RTOG grade 3 GU toxicity. Average late C-DVH's and BED-DVH's of the bladder and rectum were computed for these patients as well as for matched-pair control patients. For each matched pair the following measures of normalized difference in the DVH's were computed: (a) δ(AUC )= (Area Under Curve [AUC] in grade 3 patient – AUC in grade 0 patient)/(AUC in grade 0 patient) and (b) δ(V60 )= (Percent volume receiving = 60 Gy [V60] in grade 3 patient – V60 in grade 0 patient)/(V60 in grade 0 patient). RESULTS: As expected, the grade 3 curve is to the right of and above the grade 0 curve for all four sets of average DVH's – suggesting that both the C-DVH and the BED-DVH can be used for predicting late toxicity. δ(AUC )was higher for the BED-DVH's than for the C-DVH's – 0.27 vs 0.23 (p = 0.036) for the rectum and 0.24 vs 0.20 (p = 0.065) for the bladder. δ(V60 )was also higher for the BED-DVH's than for the C-DVH's – 2.73 vs 1.49 for the rectum (p = 0.021) and 1.64 vs 0.71 (p = 0.021) for the bladder. CONCLUSIONS: When considering well-established dosimetric endpoints used in evaluating treatment plans, BED-DVH's for the rectum and bladder correlate better with late toxicity than C-DVH's and should be considered when attempting to minimize late GI and GU toxicity after external beam radiotherapy for prostate cancer

Performance Evaluation of Calypso® 4D Localization and Kilovoltage Image Guidance Systems for Interfraction Motion Management of Prostate Patients

Prostate cancer represents a model site for advances in understanding inter-and intrafraction motion for radiotherapy. In this study, we examined the correlation of the electromagnetic transponder system/Calypso® 4D Localization System with conventional on-board imaging (OBI) using kilovoltage imaging. Initially using a quality assurance (QA) phantom and subsequently using data of seven patients, the vector distances between Calypso-and OBI-recorded shifts were compared using the t-test. For the 30 phantom measurements, the average differences between the measured Calypso offset and the calculated OBI shift were 0.4 ± 0.4, 0.2 ± 0.3, and 0.4 ± 0.3 mm in the lateral, longitudinal, and vertical directions, respectively (p = 0.73, p = 0.91, and p = 0.99, respectively), and the average difference vector for all sessions was 0.8 ± 0.4 mm. For the 259 patient measurements, the average differences between the measured Calypso offset and the calculated OBI shift were 0.7 ± 0.5, 1.1 ± 0.9, and 1.2 ± 0.9 mm in the lateral, longitudinal, and vertical directions, respectively (p = 0.45, p = 0.28, and p = 0.56, respectively), and the average difference vector for all sessions was 2.1 ± 1.0 mm. Our results demonstrated good correlation between Calypso and OBI. While other studies have explored the issue of Calypso/OBI correlation, our analysis is unique in our use of phantom validation and in our performing the patient analysis on an initial population prior to routine setup using Calypso without OBI. Implications for Calypso's role as a QA tool are discussed