Faculty Bibliography

PURPOSE/OBJECTIVE:The use of multivariable clinical models to assess postoperative prognosis in ovarian cancer increased. All published models incorporate surgical debulking. However, postoperative CT can detect residual disease (CT-RD) in 40% of optimally resected patients. The aim of our study was to investigate the added value of incorporating CT-RD evaluation into clinical models for assessment of overall survival (OS) and progression free survival (PFS) in patients after primary cytoreductive surgery (PCS). METHODS:212 women with PCS for advanced ovarian cancer between 01/1997 and 12/2011, and a contrast enhanced abdominal CT 1-7 weeks after surgery were included in this IRB approved retrospective study. Two radiologists blinded to clinical data, evaluated all CT for the presence of CT-RD, and Cohen's kappa assessed agreement. Cox proportional hazards regression with stepwise selection was used to develop OS and PFS models, with CT-RD incorporated afterwards. Model fit was assessed with bootstrapped Concordance Probability Estimates (CPE), accounting for over-fitting bias by correcting the initial estimate after repeated subsampling. RESULTS:Readers agreed on the majority of cases (179/212, k=0.68). For OS and PFS, CT-RD was significant after adjusting for clinical factors with a CPE 0.663 (p=0.0264) and 0.649 (p=0.0008). CT-RD was detected in 37% of patients assessed as optimally debulked (RD<1cm) and increased the risk of death (HR: 1.58, 95% CI: 1.06-2.37%). CONCLUSION/CONCLUSIONS:CT-RD is a significant predictor after adjusting for clinical factors for both OS and PFS. Incorporating CT-RD into the clinical model improved the prediction of OS and PFS in patients after PCS for advanced ovarian cancer.

Diffusion-weighted magnetic resonance imaging (DW-MRI) is considered part of the standard imaging protocol for the evaluation of patients with prostate cancer. It has been proven valuable as a functional tool for qualitative and quantitative analysis of prostate cancer beyond anatomical MRI sequences such as T2-weighted imaging. This review discusses ongoing controversies in DW-MRI acquisition, including the optimal number of b-values to be used for prostate DWI, and summarizes the current literature on the use of advanced DW-MRI techniques. These include intravoxel incoherent motion imaging, which better accounts for the non-mono-exponential behavior of the apparent diffusion coefficient as a function of b-value and the influence of perfusion at low b-values. Another technique is diffusion kurtosis imaging (DKI). Metrics from DKI reflect excess kurtosis of tissues, representing its deviation from Gaussian diffusion behavior. Preliminary results suggest that DKI findings may have more value than findings from conventional DW-MRI for the assessment of prostate cancer.

PURPOSE/OBJECTIVE:To develop a preoperative CT-based nomogram for predicting overall survival (OS) in patients with non-endometrioid carcinomas of the uterine corpus. METHODS:Waiving informed consent, the institutional review board approved this HIPAA-compliant, retrospective study of 193 women with histopathologically proven uterine papillary serous carcinomas (UPSC), uterine clear cell carcinomas (UCCC), and uterine carcinosarcomas (UCS) who underwent primary surgical resection between May 1998 and December 2011, and had a preoperative CT ≤ 6 weeks before surgery. All CT scans were reviewed for local or/and regional tumor extent, presence of pelvic or/and para-aortic adenopathy, and presence of distant metastases. Univariate survival analysis was performed using log-rank test and Cox regression. Variables shown significant by the univariate analysis were evaluated with the multivariable Cox regression analysis and the results were used to create a nomogram for predicting OS. The predictive accuracy of the nomogram was assessed with the concordance probability index (c-index) and a 3-year calibration plot. RESULTS:Mean patient age was 67.2 years (range 49.0-85.9); histologies included UPSC (n = 116), UCCC (n = 27), and UCS (n = 50). Median follow-up was 38.1 months (0.9-168.5 months). At multivariate analysis, patient age, ascites, and omental implants on CT were significant adverse predictors of OS and were used to build the nomogram. Concordance index for the nomogram was 0.640 ± 0.028. CONCLUSION/CONCLUSIONS:We developed a nomogram with a good concordance probability at predicting OS based on readily available pretreatment clinical and imaging characteristics. This preoperative nomogram has the potential to improve initial treatment planning and patient counseling.

OBJECTIVE:The purpose of this article is to investigate the added value of second-opinion evaluation of prostate MRI by subspecialized genitourinary oncologic radiologists for the assessment of extracapsular extension (ECE) of prostate cancer. MATERIALS AND METHODS/METHODS:We performed a retrospective evaluation of initial and second-opinion radiology reports of 76 patients who underwent MRI of the prostate before prostatectomy for histologically proven prostate cancer. Initial outside reports and second-opinion reports were unpaired and reviewed in random order by a urologist who was blinded to patients' clinical details and histopathologic data. Histopathologic analysis of the prostatectomy specimen served as the reference standard. RESULTS:Among cases with diagnostic-quality images available (71/76; 93%), disagreement between the initial report and the second-opinion report was observed in 30% of cases (21/71; κ = 0.35); in 18 of these 21 cases (86%), histopathologic analysis proved that the second-opinion report was correct. The second-opinion interpretations had statistically significantly higher sensitivity (66% vs 24%; p < 0.0001) than did the initial reports, whereas there was no statistically significant difference in specificity (87% vs 93%; p = 0.317). On ROC curve analysis, the second-opinion reports yielded a statistically significantly higher AUC for the detection of ECE (0.80 vs 0.65; p = 0.004). CONCLUSION/CONCLUSIONS:The reinterpretation of prostate MRI examinations by subspecialized genitourinary oncologic radiologists improved the detection of ECE of prostate cancer.

UNLABELLED:The epidemiology of prostate cancer has dramatically changed since the introduction of prostate-specific antigen (PSA) screening in the 1980's. Most prostate cancers today are detected at early stages of the disease and are considered 'indolent'; however, some patients' prostate cancers demonstrate a more aggressive behaviour which leads to rapid progression and death. Increasing understanding of the biology underlying the heterogeneity that characterises this disease has led to a continuously evolving role of imaging in the management of prostate cancer. Functional and metabolic imaging techniques are gaining importance as the impact on the therapeutic paradigm has shifted from structural tumour detection alone to distinguishing patients with indolent tumours that can be managed conservatively (e.g., by active surveillance) from patients with more aggressive tumours that may require definitive treatment with surgery or radiation. In this review, we discuss advanced imaging techniques that allow direct visualisation of molecular interactions relevant to prostate cancer and their potential for translation to the clinical setting in the near future. The potential use of imaging to follow molecular events during drug therapy as well as the use of imaging agents for therapeutic purposes will also be discussed. KEY POINTS/CONCLUSIONS:• Advanced imaging techniques allow direct visualisation of molecular interactions in prostate cancer. • MRI/PET, optical and Cerenkov imaging facilitate the translation of molecular biology. • Multiple compounds targeting PSMA expression are currently undergoing clinical translation. • Other targets (e.g., PSA, prostate-stem cell antigen, GRPR) are in development.

PURPOSE: To investigate associations among imaging traits observed on computed tomographic (CT) images, Classification of Ovarian Cancer (CLOVAR) gene signatures, and survival in women with high-grade serous ovarian cancer (HGSOC). MATERIALS AND METHODS: The institutional review board approved this HIPAA-compliant retrospective study of CT images obtained before cytoreductive surgery in 46 women with HGSOC, whose tumors were subjected to molecular analysis performed by the Cancer Genome Atlas Research Network. Two readers independently evaluated the CT features of the primary ovarian mass and sites of metastatic spread if present, including size, outline, and texture. Fisher exact test was used to examine the relationship between imaging traits and CLOVAR subtypes (CLOVAR differentiated, immunoreactive, mesenchymal, and proliferative). Kaplan-Meier and Cox proportional hazards regression survival analyses were performed. RESULTS: The presence of mesenteric infiltration and diffuse peritoneal involvement by tumor at CT were significantly associated with CLOVAR subtype (P = .002-.004 for reader 1 and P = .005-.012 for reader 2). Mesenteric infiltration at CT was associated with CLOVAR mesenchymal subtype. Patients with mesenteric infiltration had shorter median progression-free survival than patients without mesenteric involvement (14.7 months vs 25.6 months according to both readers; P = .019 for reader 1 and .015 for reader 2) and overall survival (49.0 vs 58.2 months; P = .014 [reader 1] and 50.0 vs 59.1 months; P = .015 [reader 2]). No other imaging features were significantly associated with CLOVAR subtype or survival. CONCLUSION: Specific CT imaging traits were associated with the CLOVAR subtypes and survival in patients with HGSOC.

OBJECTIVES/OBJECTIVE:To evaluate if computed tomographic (CT) features of intradiverticular bladder cancer can predict clinical outcome. METHODS:Retrospective study of 34 patients with intradiverticular bladder cancer. Two radiologists independently evaluated all CT exams. RESULTS:CT tumor length and width were significantly associated with survival for both readers [hazard ratios (HRs) 1.31-1.62, P<.001-.043]. No other tumor features were significantly associated with survival. The interreader agreement for the assessment of CT features was fair to substantial (k=0.34-0.78, concordance correlation coefficient=0.56-0.66). There was no association between transurethral resection pathology stage and survival (HR 2.10, P=.21). CONCLUSIONS:In patients with intradiverticular bladder cancer, the tumor length and width measured on the pretreatment CT predicted survival.

Pelvic exenteration (PE) is a radical surgical procedure used for the past 6 decades to treat locally advanced malignant diseases confined to the pelvis, particularly persistent or recurrent gynecologic cancers in the irradiated pelvis. The traditional surgical technique known as total PE consists of resection of all pelvic viscera followed by reconstruction. Depending on the tumor extent, the procedure can be tailored to remove only anterior or posterior structures, including the bladder (anterior exenteration) or rectum (posterior exenteration). Conversely, more extended pelvic resection can be performed if the pelvic sidewall is invaded by cancer. Preoperative imaging evaluation with magnetic resonance (MR) imaging and fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is central to establishing tumor resectability and therefore patient eligibility for the procedure. These imaging modalities complement each other in diagnosis of tumor recurrence and differentiation of persistent disease from posttreatment changes. MR imaging can accurately demonstrate local tumor extent and show adjacent organ invasion. FDG PET/CT is useful in excluding nodal and distant metastases. In addition, FDG PET/CT metrics may serve as predictive biomarkers for overall and disease-free survival. This pictorial review describes different types of exenterative surgical procedures and illustrates the central role of imaging in accurate patient selection, treatment planning, and postsurgical surveillance.

Multiparametric MRI of the prostate consists of T1- and T2-weighted sequences, which provide anatomical information, and one or more 'functional' sequences, that is, diffusion-weighted imaging, dynamic contrast-enhanced sequences and magnetic resonance spectroscopy. Prostate MRI is the most accurate imaging method for local staging of prostate cancer and can also be used for the noninvasive evaluation of tumor aggressiveness. By magnetic resonance-guided prostate biopsy it is possible to target the most cancer-suspicious areas of the gland, especially in patients with a negative transrectal biopsy. In patients with biochemical recurrence after radical treatment, MRI is a valuable tool for the detection of local tumor recurrence and whole-body MRI can be used for the diagnosis of distant metastases.

PURPOSE/OBJECTIVE:To assess whether an artificial neural network (ANN) model is a useful tool for automatic detection of cancerous voxels in the prostate from (1)H-MRSI datasets and whether the addition of information about anatomical segmentation improves the detection of cancer. MATERIALS AND METHODS/METHODS:The Institutional Review Board approved this HIPAA-compliant study and waived informed consent. Eighteen men with prostate cancer (median age, 55 years; range, 36-71 years) who underwent endorectal MRI/MRSI before radical prostatectomy were included in this study. These patients had at least one cancer area on whole-mount histopathological map and at least one matching MRSI voxel suspicious for cancer detected. Two ANN models for automatic classification of MRSI voxels in the prostate were implemented and compared: model 1, which used only spectra as input, and model 2, which used the spectra plus information from anatomical segmentation. The models were trained, tested and validated using spectra from voxels that the spectroscopist had designated as cancer and that were verified on histopathological maps. RESULTS:At ROC analysis, model 2 (AUC = 0.968) provided significantly better (P = 0.03) classification of cancerous voxels than did model 1 (AUC = 0.949). CONCLUSION/CONCLUSIONS:Automatic analysis of prostate MRSI to detect cancer using ANN model is feasible. Application of anatomical segmentation from MRI as an additional input to ANN improves the accuracy of detecting cancerous voxels from MRSI.