Radioisotope-guided Lymphadenectomy for Pelvic Lymph Node Staging in Patients With Intermediate- and High-risk Prostate Cancer (The Prospective SENTINELLE Study).

PURPOSE: Our aim was to prospectively evaluate the diagnostic accuracy of sentinel lymph node biopsy-guided lymph node dissection compared to extended pelvic lymph node dissection in patients with intermediate- or high-risk prostate cancer. MATERIALS AND METHODS: We conducted a prospective, single-arm, multicenter study at 3 tertiary centers in France between February 2012 and May 2019. Eligible patients had clinically localized intermediate- or high-risk prostate cancer. After intraprostatic injection of (99m)Tc-nanocolloid, the locations of the sentinel lymph nodes were defined by preoperative lymphoscintigraphy. Surgical excision of the sentinel lymph nodes was performed using intraoperative gamma probe guidance. After resection of the sentinel lymph nodes, extended pelvic lymph node dissection was performed in all patients. We assessed the diagnostic accuracy of the sentinel lymph node biopsy method using extended pelvic lymph node dissection as the reference standard. This trial was registered in ClinicalTrials.gov (NCT02732392). RESULTS: A total of 162 men cN0M0 (CT scan and bone scan) were enrolled: 106 (65.4%) and 56 (34.6%) patients had intermediate- and high-risk prostate cancer, respectively. The median number of nodes retrieved was 14 (mean 16, IQR 10-21) per patient. At final pathological analysis, 22 patients (13.6%) were pN+. Sensitivity, specificity, negative predictive value, and positive predictive value of sentinel lymph node biopsy method in detecting patients with at least 1 lymph node metastasis were 95.4% (95% CI, 75.1-99.7), 100% (95% CI, 96.6-100), 99.2% (95% CI, 95.5-99.9), and 100% (95% CI, 80.7-100), respectively. CONCLUSIONS: Our multicenter prospective study supports that sentinel lymph node biopsy is a very effective and sensitive method for pelvic lymph node staging in patients with intermediate- or high-risk localized prostate cancer.

The EPICURE study: a pilot prospective cohort study of heterogeneous and massive data integration in metastatic breast cancer patients.

BACKGROUND: Breast cancer is the most common cancer in women and the first cancer concerning mortality. Metastatic breast cancer remains a disease with a poor prognosis and about 30% of women diagnosed with an early stage will have a secondary progression. Metastatic breast cancer is an incurable disease despite significant therapeutic advances in both supportive cares and targeted specific therapies. In the management of a metastatic patient, each clinician follows a highly complex and strictly personal decision making process. It is based on a number of objective and subjective parameters which guides therapeutic choice in the most individualized or adapted manner. METHODS/DESIGN: The main objective is to integrate massive and heterogeneous data concerning the patient's environment, personal and familial history, clinical and biological data, imaging, histological results (with multi-omics data), and microbiota analysis. These characteristics are multiple and in dynamic interaction overtime. With the help of mathematical units with biological competences and scientific collaborations, our project is to improve the comprehension of treatment response, based on health clinical and molecular heterogeneous big data investigation. DISCUSSION: Our project is to prove feasibility of creation of a clinico-biological database prospectively by collecting epidemiological, socio-economic, clinical, biological, pathological, multi-omic data and to identify characteristics related to the overall survival status before treatment and within 15 years after treatment start from a cohort of 300 patients with a metastatic breast cancer treated in the institution. TRIAL REGISTRATION: ClinicalTrials.gov identifier (NCT number): NCT03958136 . Registration 21st of May, 2019; retrospectively registered.

68Ga-PSMA-11 PET-CT study in prostate cancer patients with biochemical recurrence and non-contributive 18F-Choline PET-CT: Impact on therapeutic decision-making and biomarker changes.

BACKGROUND: In this retrospective study, we investigated the impact of 68 Ga-PSMA-11 PET-CT (PSMA PET-CT) upon the treatment plan and therapeutic response obtained for Prostate Cancer (PCa) patients presenting an occult biochemical recurrence. METHODS: Forty-two patients with previously negative or doubtful 18F-Choline (FCH) were enrolled. PET images were recorded 1 h after injection of tracer. Only a few months after treatment ended, a PSA assay was requested to evaluate the therapeutic efficacy of the treatment based on PSMA results. RESULTS: PSMA-positive lesions were detected in 34/42 (80.9%) patients. Detection rates were 85.7% and 89.3% for serum PSA levels lower than 2 ng/mL, and >2 ng/mL, respectively. One hundred seventy-three lesions were detected: 132/173 in lymph nodes (76.3%), 22/173 as metastatic sites (bone or lung) (12.7%), and 19/173 in the prostate bed (10.9%). As a result of the PSMA PET-CT, therapeutic management changed in 31/42 patients (73.8%). With a follow-up of 4.9 ± 2.27 months, 32/42 (76.2%) PSA assays after treatment guided by PSMA PET-CT were collected. For 37.5% (12/32) of patients, the serum PSA level was lower than 0.2 ng/mL and a PSA decrease of over 50% in 8 (25.0%) other patients were obtained. CONCLUSION: Performing a PSMA PET-CT when FCH PET-CT was doubtful or negative allows the recurrence localization in more 80% of patients and this had a major clinical impact, as it resulted in treatment change in more than 70% of patients as well as a significant decrease in PSA levels in more than 60% of them.

Development and validation of core entrustable professional activities for abdominal radiology.

OBJECTIVES: To develop and validate European entrustable professional activities (EPAs) for sub-specialised hepatobiliary and gastrointestinal (HB/GI) diagnostic imaging. MATERIALS AND METHODS: Both European Society of Radiology and national curricula in HB/GI diagnostic radiology were thoroughly reviewed, resulting in preliminary EPAs drafted by a pilot group of expert radiologists in 2 different countries. Each EPA was fully described with 7 components (Specification/limitations; Potential risks of failing; Relevant domains of competence; Required experience, knowledge, skills, attitude and behaviour; Assessment information sources to assess progress and ground a summative entrustment decision; Entrustment for which level of supervision is to be reached; and Expiration date). The modified Delphi method with 3 Delphi rounds was chosen for validation. Content validity index (CVI) and median values were used for validation. RESULTS: There were 15 preliminary EPAs, some of them divided according to 2 levels: resident and fellow level. The 37 members of the Delphi group were based in 2 different European countries with a background experience of 10 represented countries. Subsequent to the first Delphi round, 6 EPAs were accepted (CVI ≥ 0.8, median ≥ 4), 6 needed major revisions (CVI 0.7-0.79, median ≥ 4), 3 were rejected (CVI < 0.7) and 1 was added. After the second Delphi round, both the 6 revised EPAs and the additional one met the validation criteria (CVI ≥ 0.8, median ≥ 4). Finally, 13 EPAs were validated during the 3rd Delphi round with an agreement percentage of 95-100%. CONCLUSION: This study creates and validates EPAs for sub-specialised HB/GI diagnostic imaging. CRITICAL RELEVANCE STATEMENT: Thirteen EPAs for sub-specialised hepatobiliary and gastrointestinal diagnostic imaging were created with a strong methodology, and as a first example set in sub-specialised diagnostic imaging, they provide a template for others to be created. KEY POINTS: • The competence-based teaching in medical studies has recently been reintroduced through EPAs. • Thirteen EPAs have been developed for hepatobiliary and gastrointestinal sub-specialised diagnostic imaging. • These EPAs were validated using a Delphi modified method and provide a template for other to be created.

Deformable image registration with deep network priors: a study on longitudinal PET images.

Objective.This paper proposes a novel approach for the longitudinal registration of PET imaging acquired for the monitoring of patients with metastatic breast cancer. Unlike with other image analysis tasks, the use of deep learning (DL) has not significantly improved the performance of image registration. With this work, we propose a new registration approach to bridge the performance gap between conventional and DL-based methods: medical image registration method regularized by architecture (MIRRBA).Approach.MIRRBAis a subject-specific deformable registration method which relies on a deep pyramidal architecture to parametrize the deformation field. Diverging from the usual deep-learning paradigms,MIRRBAdoes not require a learning database, but only a pair of images to be registered that is used to optimize the network's parameters. We appliedMIRRBAon a private dataset of 110 whole-body PET images of patients with metastatic breast cancer. We used different architecture configurations to produce the deformation field and studied the results obtained. We also compared our method to several standard registration approaches: two conventional iterative registration methods (ANTs and Elastix) and two supervised DL-based models (LapIRN and Voxelmorph). Registration accuracy was evaluated using the Dice score, the target registration error, the average Hausdorff distance and the detection rate, while the realism of the registration obtained was evaluated using Jacobian's determinant. The ability of the different methods to shrink disappearing lesions was also computed with the disappearing rate.Main results.MIRRBA significantly improved all metrics when compared to DL-based approaches. The organ and lesion Dice scores of Voxelmorph improved by 6% and 52% respectively, while the ones of LapIRN increased by 5% and 65%. Regarding conventional approaches, MIRRBA presented comparable results showing the feasibility of our method.Significance.In this paper, we also demonstrate the regularizing power of deep architectures and present new elements to understand the role of the architecture in DL methods used for registration.

Influence of inputs for bone lesion segmentation in longitudinal 18F-FDG PET/CT imaging studies.

In metastatic breast cancer, bone metastases are prevalent and associated with multiple complications. Assessing their response to treatment is therefore crucial. Most deep learning methods segment or detect lesions on a single acquisition while only a few focus on longitudinal studies. In this work, 45 patients with baseline (BL) and follow-up (FU) images recruited in the context of the EPICUREseinmeta study were analyzed. The aim was to determine if a network trained for a particular timepoint can generalize well to another one, and to explore different improvement strategies. Four networks based on the same 3D U-Net framework to segment bone lesions on BL and FU images were trained with different strategies and compared. These four networks were trained 1) only with BL images 2) only with FU images 3) with both BL and FU images 4) only with FU images but with BL images and bone lesion segmentations registered as input channels. With the obtained segmentations, we computed the PET Bone Index (PBI) which assesses the bone metastases burden of patients and we analyzed its potential for treatment response evaluation. Dice scores of 0.53, 0.55, 0.59 and 0.62 were respectively obtained on FU acquisitions. The under-performance of the first and third networks may be explained by the lower SUV uptake due to treatment response in FU images compared to BL images. The fourth network gives better results than the second network showing that the addition of BL PET images and bone lesion segmentations as prior knowledge has its importance. With an AUC of 0.86, the difference of PBI between two acquisitions could be used to assess treatment response. Clinical relevance- To assess the response to treatment of bone metastases, it is crucial to detect and segment them on several acquisitions from a same patient. We proposed a completely automatic method to detect and segment these metastases on longitudinal 18F-FDG PET/CT images in the context of metastatic breast cancer. We also proposed an automatic PBI to quantitatively assess the evolution of the bone metastases burden of patient and to automatically evaluate their response to treatment.

Automatic Segmentation of Metastatic Breast Cancer Lesions on 18F-FDG PET/CT Longitudinal Acquisitions for Treatment Response Assessment.

Metastatic breast cancer patients receive lifelong medication and are regularly monitored for disease progression. The aim of this work was to (1) propose networks to segment breast cancer metastatic lesions on longitudinal whole-body PET/CT and (2) extract imaging biomarkers from the segmentations and evaluate their potential to determine treatment response. Baseline and follow-up PET/CT images of 60 patients from the EPICUREseinmeta study were used to train two deep-learning models to segment breast cancer metastatic lesions: One for baseline images and one for follow-up images. From the automatic segmentations, four imaging biomarkers were computed and evaluated: SULpeak, Total Lesion Glycolysis (TLG), PET Bone Index (PBI) and PET Liver Index (PLI). The first network obtained a mean Dice score of 0.66 on baseline acquisitions. The second network obtained a mean Dice score of 0.58 on follow-up acquisitions. SULpeak, with a 32% decrease between baseline and follow-up, was the biomarker best able to assess patients' response (sensitivity 87%, specificity 87%), followed by TLG (43% decrease, sensitivity 73%, specificity 81%) and PBI (8% decrease, sensitivity 69%, specificity 69%). Our networks constitute promising tools for the automatic segmentation of lesions in patients with metastatic breast cancer allowing treatment response assessment with several biomarkers.

Deep learning approaches for bone and bone lesion segmentation on 18FDG PET/CT imaging in the context of metastatic breast cancer.

18FDG PET/CT imaging is commonly used in diagnosis and follow-up of metastatic breast cancer, but its quantitative analysis is complicated by the number and location heterogeneity of metastatic lesions. Considering that bones are the most common location among metastatic sites, this work aims to compare different approaches to segment the bones and bone metastatic lesions in breast cancer.Two deep learning methods based on U-Net were developed and trained to segment either both bones and bone lesions or bone lesions alone on PET/CT images. These methods were cross-validated on 24 patients from the prospective EPICUREseinmeta metastatic breast cancer study and were evaluated using recall and precision to measure lesion detection, as well as the Dice score to assess bones and bone lesions segmentation accuracy.Results show that taking into account bone information in the training process allows to improve the precision of the lesions detection as well as the Dice score of the segmented lesions. Moreover, using the obtained bone and bone lesion masks, we were able to compute a PET bone index (PBI) inspired by the recognized Bone Scan Index (BSI). This automatically computed PBI globally agrees with the one calculated from ground truth delineations.Clinical relevance- We propose a completely automatic deep learning based method to detect and segment bones and bone lesions on 18FDG PET/CT in the context of metastatic breast cancer. We also introduce an automatic PET bone index which could be incorporated in the monitoring and decision process.

Combining Superpixels and Deep Learning Approaches to Segment Active Organs in Metastatic Breast Cancer PET Images.

Semi-automatic measurements are performed on 18FDG PET-CT images to monitor the evolution of metastatic sites in the clinical follow-up of metastatic breast cancer patients. Apart from being time-consuming and prone to subjective approximation, semi-automatic tools cannot make the difference between cancerous regions and active organs, presenting a high 18FDG uptake.In this work, we combine a deep learning-based approach with a superpixel segmentation method to segment the main active organs (brain, heart, bladder) from full-body PET images. In particular, we integrate a superpixel SLIC algorithm at different levels of a convolutional network. Results are compared with a deep learning segmentation network alone. The methods are cross-validated on full-body PET images of 36 patients and tested on the acquisitions of 24 patients from a different study center, in the context of the ongoing EPICUREseinmeta study. The similarity between the manually defined organ masks and the results is evaluated with the Dice score. Moreover, the amount of false positives is evaluated through the positive predictive value (PPV).According to the computed Dice scores, all approaches allow to accurately segment the target organs. However, the networks integrating superpixels are better suited to transfer knowledge across datasets acquired on multiple sites (domain adaptation) and are less likely to segment structures outside of the target organs, according to the PPV.Hence, combining deep learning with superpixels allows to segment organs presenting a high 18FDG uptake on PET images without selecting cancerous lesion, and thus improves the precision of the semi-automatic tools monitoring the evolution of breast cancer metastasis.Clinical relevance- We demonstrate the utility of combining deep learning and superpixel segmentation methods to accurately find the contours of active organs from metastatic breast cancer images, to different dataset distributions.

Haute Couture or Ready-to-Wear? Tailored Pelvic Radiotherapy for Prostate Cancer Based on Individualized Sentinel Lymph Node Detection.

Prostate cancer (PCa) pelvic radiotherapy fields are defined by guidelines that do not consider individual variations in lymphatic drainage. We examined the feasibility of personalized sentinel lymph node (SLN)-based pelvic irradiation in PCa. Among a SLN study of 202 patients, we retrospectively selected 57 patients with a high risk of lymph node involvement. Each single SLN clinical target volume (CTV) was individually segmented and pelvic CTVs were contoured according to Radiation Therapy Oncology Group (RTOG) guidelines. We simulated a radiotherapy plan delivering 46 Gy and calculated the dose received by each SLN. Among a total of 332 abdominal SLNs, 305 pelvic SLNs (beyond the aortic bifurcation) were contoured (mean 5.4/patient). Based on standard guidelines, CTV missed 67 SLNs (22%), mostly at the common iliac level (40 SLNs). The mean distance between iliac vessels and the SLN was 11mm, and despite a 15mm margin around the iliac vessels, 9% of SLNs were not encompassed by the CTV. Moreover, 42 SLNs (63%) did not receive 95% of the prescribed dose. Despite a consensus on contouring guidelines, a significant proportion of SLNs were not included in the pelvic CTV and did not receive the prescribed dose. A tailored approach based on individual SLN detection would avoid underdosing pelvic lymph nodes that potentially contain tumor cells.

Initial Clinical Results of a Novel Immuno-PET Theranostic Probe in Human Epidermal Growth Factor Receptor 2-Negative Breast Cancer.

This prospective study evaluated the imaging performance of a novel pretargeting immunologic PET (immuno-PET) method in patients with human epidermal growth factor receptor 2 (HER2)-negative, carcinoembryonic antigen (CEA)-positive metastatic breast cancer, compared with CT, bone MRI, and 18F-FDG PET. Methods: Twenty-three patients underwent whole-body immuno-PET after injection of 150 MBq of 68Ga-IMP288, a histamine-succinyl-glycine peptide given after initial targeting of a trivalent anti-CEA, bispecific, antipeptide antibody. The gold standards were histology and imaging follow-up. Tumor SUVs (SUVmax and SUVmean) were measured, and tumor burden was analyzed using total tumor volume and total lesion activity. Results: The total lesion sensitivity of immuno-PET and 18F-FDG PET were 94.7% (1,116/1,178) and 89.6% (1,056/1,178), respectively. Immuno-PET had a somewhat higher sensitivity than CT or 18F-FDG PET in lymph nodes (92.4% vs. 69.7% and 89.4%, respectively) and liver metastases (97.3% vs. 92.1% and 94.8%, respectively), whereas sensitivity was lower for lung metastases (48.3% vs. 100% and 75.9%, respectively). Immuno-PET showed higher sensitivity than MRI or 18F-FDG PET for bone lesions (95.8% vs. 90.7% and 89.3%, respectively). In contrast to 18F-FDG PET, immuno-PET disclosed brain metastases. Despite equivalent tumor SUVmax, SUVmean, and total tumor volume, total lesion activity was significantly higher with immuno-PET than with 18F-FDG PET (P = 0.009). Conclusion: Immuno-PET using anti-CEA/anti-IMP288 bispecific antibody, followed by 68Ga-IMP288, is a potentially sensitive theranostic imaging method for HER2-negative, CEA-positive metastatic breast cancer patients and warrants further research.

Advantages of systematic trunk SPECT/CT to planar bone scan (PBS) in more than 300 patients with breast or prostate cancer.

PROPOSE: The aim of our study was to evaluate the potential benefit of a systematic trunk SPECT/CT associated with a Planar Bone Scan (PBS) in breast cancer (BC) and prostate cancer (PCa) patients at initial staging or recurrence. RESULTS: In 328 patients, sensitivities and specificities were between 74.4-93% and 78.8-97.5% for PBS and 97.7-100% and 96.8-98.6% for SPECT/CT respectively. PBS was considered equivocal for 67 compared to only 6 patients for trunk SPECT/CT. Regardless of "optimistic" or "pessimistic" analysis of equivocal trunk SPECT/CT lesions, the trunk SPECT/CT was almost perfect, allowing to rely on this result for excluding metastatic disease which was corroborated by ROC curve analysis. The trunk SPECT/CT allowed downstaging for 62 patients (19%) and upstaging for 11 patients. MATERIALS AND METHODS: PBS and a trunk SPECT/CT were systematically performed in all patients. Independent review of PBS and trunk SPECT/CT was performed for each patient and an abnormality interpretative score (Sc) with 3 levels was built: Sc 1: metastatic or probably metastatic pattern, Sc 2: equivocal pattern, Sc 3: benign or probably benign pattern or no abnormality. The bone pattern status was defined by at least 1 year follow-up. The clinical impact was evaluated in terms of down and upstaging in patient analysis. CONCLUSIONS: Trunk SPECT/CT improves the performance of PBS in BC and PCa assessments and results in improvements in both the detection performance of bone metastases as well as a better characterization of equivocal lesions.

Incidental Detection of a Hodgkin Lymphoma on 18F-Choline PET/CT and Comparison With 18F-FDG in a Patient With Prostate Cancer.

Combined PET/CT scanning with (18)F-FDG is in current use in Hodgkin lymphoma. New tracers have been developed, such as (18)F-choline in prostate cancer. Its use is under investigation in other solid tumors (eg, brain, liver, lung). We report a case of Hodgkin lymphoma incidentally detected on (18)F-choline PET/CT in a prostate cancer patient and show a comparison with (18)F-FDG PET/CT. (18)F-choline PET/CT detected more lymph node lesions than the (18)F-FDG PET/CT for this patient. Comparative studies of the 2 tracers might help fine-tune treatments and, in particular, delineate target zones in radiation therapy.

Assessment of Lymph Nodes and Prostate Status Using Early Dynamic Curves with (18)F-Choline PET/CT in Prostate Cancer.

INTRODUCTION: Dynamic image acquisition with (18)F-Choline [fluorocholine (FCH)] PET/CT in prostate cancer is mostly used to overcome the bladder repletion, which could obstruct the loco-regional analysis. The aim of our study was to analyze early dynamic FCH acquisitions to define pelvic lymph node or prostate pathological status. MATERIAL AND METHODS: Retrospective analysis was performed on 39 patients for initial staging (n = 18), or after initial treatment (n = 21). Patients underwent 10-min dynamic acquisitions centered on the pelvis, after injection of 3-4 MBq/kg of FCH. Whole-body images were acquired about 1 h after injection using a PET/CT GE Discovery LS (GE-LS) or Siemens Biograph mCT (mCT). Maximum and mean SUV according to time were measured on nodal and prostatic lesions. SUVmean was corrected for partial volume effect (PVEC) with suitable recovery coefficients. The status of each lesion was based on histological results or patient follow-up (>6 months). A Mann-Whitney test and ANOVA were used to compare mean and receiver operating characteristic (ROC) curve analysis. RESULTS: The median PSA was 8.46 ng/mL and the median Gleason score was 3 + 4. Ninety-two lesions (43 lymph nodes and 49 prostate lesions) were analyzed, including 63 malignant lesions. In early dynamic acquisitions, the maximum and mean SUV were significantly higher, respectively, on mCT and GE-LS, in malignant versus benign lesions (p < 0.001, p < 0.001). Mean SUV without PVEC, allowed better discrimination of benign from malignant lesions, in comparison with maximum and mean SUV (with PVEC), for both early and late acquisitions. For patients acquired on mCT, area under the ROC curve showed a trend to better sensitivity and specificity for early acquisitions, compared with late acquisitions (SUVmax AUC 0.92 versus 0.85, respectively). CONCLUSION: Assessment of lymph nodes and prostate pathological status with early dynamic imaging using PET/CT FCH allowed prostate cancer detection in situations where proof of malignancy is difficult to obtain.

Focus on the Controversial Aspects of (64)Cu-ATSM in Tumoral Hypoxia Mapping by PET Imaging.

Mapping tumor hypoxia is a great challenge in positron emission tomography (PET) imaging as the precise functional information of the biological processes is needed for many effective therapeutic strategies. Tumor hypoxia has been widely reported as a poor prognostic indicator and is often associated with tumor aggressiveness, chemo- and radio-resistance. An accurate diagnosis of hypoxia is a challenge and is crucial for providing accurate treatment for patients' survival benefits. This challenge has led to the emergence of new and novel PET tracers for the functional and metabolic characterization of tumor hypoxia non-invasively. Among these tracers, copper semicarbazone compound [64Cu]-diacetyl-bis(N (4)-methylthiosemicarbazone) (=64Cu-ATSM) has been developed as a tracer for hypoxia imaging. This review focuses on 64Cu-ATSM PET imaging and the concept is presented in two sections. The first section describes its in vitro development and pre-clinical testing and particularly its affinity in different cell lines. The second section describes the controversial reports on its specificity for hypoxia imaging. The review concludes that 64Cu-ATSM - more than a hypoxic tracer, exhibits tracer accumulation in tumor, which is linked to the redox potential and reactive oxygen species. The authors concluded that 64Cu-ATSNM is a marker of over-reduced cell state and thus an indirect marker for hypoxia imaging. The affinity of 64Cu-ATSM for over-reduced cells was observed to be a complex phenomenon. And to provide a definitive and convincing mechanism, more in vivo studies are needed to prove the diagnostic utility of 64Cu-ATSM.

Clinical and Survival Impact of FDG PET in Patients with Suspicion of Recurrent Ovarian Cancer: A 6-Year Follow-Up.

BACKGROUND: The aim of this retrospective study was to evaluate the contribution of fluorine-18-fluoro-deoxyglucose (FDG) positron emission tomography (PET) to the clinical management and survival outcome of patients (pts) suspected of recurrent ovarian carcinoma, with the hypothesis that early diagnosis of recurrent ovarian cancer may improve overall survival (OS). METHODS: Fifty-three FDG PET/CT scans were retrospectively analyzed for 42 pts. CT and PET/CT findings were confirmed by imaging and clinical follow-up, and/or pathology, which were considered as the gold standard diagnosis. The treatment plan based on CT staging was compared with that based on PET/CT findings. Medical records were reviewed for pts characteristics, progression-free survival (PFS), and OS. PFS and OS were analyzed using the Cox proportional hazards regression model. RESULTS: The final diagnosis of recurrence was established pathologically (n = 16), or by a median clinical follow-up of 6.5 years (range 0.5-7.5) after the PET/CT (n = 37). PET/CT provided a higher detection sensitivity (92.2%, 47/51) than CT (60.8%, 31/51) (p < 0.001). Globally, PET/CT modified the treatment plan in 56.6% (30/53) and in 65.2% (15/23) when the CT was negative prior to PET/CT. In 30 cases, those benefited from a modified treatment plan, these changes led to the intensification of a previous treatment procedure in 83.3% (25/30), and to a reduction in the previous treatment procedure in 16.6% of cases (5/30). The Cox regression multivariate analysis showed that the number of lesions visualized by CT and presence of lung lesions detected by PET/CT were significantly associated with PFS (p = 0.002 and p = 0.035, respectively). CONCLUSION: On account of its impact on treatment planning, and especially in predicting patient outcome, FDG PET is a valuable diagnostic tool for cases of suspected ovarian cancer recurrence.

Unexpected finding on I-123 FP-CIT SPECT leading to the diagnosis of cerebral meningioma.

A 69-year-old woman with a 20-year history of psychiatric disorders was referred to our Nuclear Medicine Department because of an extrapyramidal syndrome. Because the differential diagnosis between neuroleptic-induced parkinsonism and Parkinson disease was difficult, I-123 FP-CIT brain SPECT was performed. The transverse slices demonstrated an unexpected image of the left striatum which seemed moved backward and toward the midline. A space occupying lesion was suspected and later confirmed by CT. This case highlights the significance of structural neuroimaging and of careful check of the striatum morphology.