Tata Memorial Centre Evidence Based Use of Nuclear medicine diagnostic and treatment modalities in cancer.

ABSTRACT: PET/CT and radioisotope therapy are diagnostic and therapeutic arms of Nuclear Medicine, respectively. With the emergence of better technology, PET/CT has become an accessible modality. Diagnostic tracers exploring disease-specific targets has led the clinicians to look beyond FDG PET. Moreover, with the emergence of theranostic pairs of radiopharmaceuticals, radioisotope therapy is gradually making it's way into treatment algorithm of common cancers in India. We therefore would like to discuss in detail the updates in PET/CT imaging and radionuclide therapy and generate a consensus-driven evidence based document which would guide the practitioners of Oncology.

Comparing the performance of a deep learning-based lung gross tumour volume segmentation algorithm before and after transfer learning in a new hospital.

Objectives: Radiation therapy for lung cancer requires a gross tumour volume (GTV) to be carefully outlined by a skilled radiation oncologist (RO) to accurately pinpoint high radiation dose to a malignant mass while simultaneously minimizing radiation damage to adjacent normal tissues. This is manually intensive and tedious however, it is feasible to train a deep learning (DL) neural network that could assist ROs to delineate the GTV. However, DL trained on large openly accessible data sets might not perform well when applied to a superficially similar task but in a different clinical setting. In this work, we tested the performance of DL automatic lung GTV segmentation model trained on open-access Dutch data when used on Indian patients from a large public tertiary hospital, and hypothesized that generic DL performance could be improved for a specific local clinical context, by means of modest transfer-learning on a small representative local subset. Methods: X-ray computed tomography (CT) series in a public data set called "NSCLC-Radiomics" from The Cancer Imaging Archive was first used to train a DL-based lung GTV segmentation model (Model 1). Its performance was assessed using a different open access data set (Interobserver1) of Dutch subjects plus a private Indian data set from a local tertiary hospital (Test Set 2). Another Indian data set (Retrain Set 1) was used to fine-tune the former DL model using a transfer learning method. The Indian data sets were taken from CT of a hybrid scanner based in nuclear medicine, but the GTV was drawn by skilled Indian ROs. The final (after fine-tuning) model (Model 2) was then re-evaluated in "Interobserver1" and "Test Set 2." Dice similarity coefficient (DSC), precision, and recall were used as geometric segmentation performance metrics. Results: Model 1 trained exclusively on Dutch scans showed a significant fall in performance when tested on "Test Set 2." However, the DSC of Model 2 recovered by 14 percentage points when evaluated in the same test set. Precision and recall showed a similar rebound of performance after transfer learning, in spite of using a comparatively small sample size. The performance of both models, before and after the fine-tuning, did not significantly change the segmentation performance in "Interobserver1." Conclusions: A large public open-access data set was used to train a generic DL model for lung GTV segmentation, but this did not perform well initially in the Indian clinical context. Using transfer learning methods, it was feasible to efficiently and easily fine-tune the generic model using only a small number of local examples from the Indian hospital. This led to a recovery of some of the geometric segmentation performance, but the tuning did not appear to affect the performance of the model in another open-access data set. Advances in knowledge: Caution is needed when using models trained on large volumes of international data in a local clinical setting, even when that training data set is of good quality. Minor differences in scan acquisition and clinician delineation preferences may result in an apparent drop in performance. However, DL models have the advantage of being efficiently "adapted" from a generic to a locally specific context, with only a small amount of fine-tuning by means of transfer learning on a small local institutional data set.

[90Y]Yttria Alumino Silicate Glass Microspheres: A Biosimilar Formulation to "TheraSphere" for Cost-Effective Treatment of Liver Cancer.

Background: Selective internal radiation therapy (SIRT) using a suitable ß--emitting radionuclide is a promising treatment modality for unresectable liver carcinoma. Yttrium-90 (90Y) [T1/2 = 64.2 h, Eß(max) = 2.28 MeV, no detectable γ-photon] is the most preferred radioisotope for SIRT owing to its favorable decay characteristics. Objective: The present study describes indigenous development and evaluation of intrinsically radiolabeled [90Y]yttria alumino silicate ([90Y]YAS) glass microsphere, a formulation biosimilar to "TheraSphere" (commercially available, U.S. FDA-approved formulation), for SIRT of unresectable liver carcinoma in human patients. Methods: YAS glass microspheres of composition 40Y2O3-20Al2O3-40SiO2 (w/w) and diameter ranging between 20 and 36 µm were synthesized with almost 100% conversion efficiency and >99% sphericity. Intrinsically labeled [90Y]YAS glass microspheres were produced by thermal neutron irradiation of cold YAS glass microspheres in a research reactor. Subsequent to in vitro evaluations and in vivo studies in healthy Wistar rats, customized doses of [90Y]YAS glass microspheres were administered in human patients. Results: [90Y]YAS glass microspheres were produced with 137.7 ± 8.6 MBq/mg YAS glass (∼6800 Bq per microsphere) specific activity and 99.94% ± 0.02% radionuclidic purity at the end of irradiation. The formulation exhibited excellent in vitro stability in human serum and showed >97% retention in the liver up to 7 d post-administration when biodistribution studies were carried out in healthy Wistar rats. Yttrium-90 positron emission tomography scans recorded at different time points post-administration of customized dose of [90Y]YAS glass microspheres in human patients showed near-quantitative retention of the formulation in the injected lobe. Conclusions: The study confirmed the suitability of indigenously prepared [90Y]YAS glass microspheres for clinical use in the treatment of unresectable hepatocellular carcinoma.

Intensity Modulated Radiation Therapy Alone Versus Intensity Modulated Radiation Therapy and Brachytherapy for Early-Stage Oropharyngeal Cancers: Results From a Randomized Controlled Trial.

PURPOSE: The objective of this study was to compare clinical outcomes of intensity modulated radiation therapy (IMRT) alone versus IMRT + brachytherapy (BT) in patients with T1-T2N0M0 oropharyngeal squamous cell cancers (OPSCC). METHODS AND MATERIALS: This open-label randomized controlled trial was conducted at Tata Memorial Hospital, Mumbai, India. Patients with stage I and II OPSCC were considered for IMRT to a dose of 50 Gy/25 fractions/5 weeks in phase I followed by randomization (1:1) to further treatment with IMRT (20 Gy/10 fractions/2 weeks) or BT (192Ir high dose rate, 21 Gy/7 fractions/2 fractions per day). The primary endpoint of the trial was the reduction in xerostomia at 6 months evaluated using 99mTc salivary scintigraphy. Severe salivary toxicity (xerostomia) was defined as posttreatment salivary excretion fraction ratio <45%. Secondary endpoints were local control, disease-free survival, and overall survival. RESULTS: Between November 2010 and February 2020, 90 patients were randomized to IMRT (n = 46) alone or IMRT + BT (n = 44). Eleven patients (8 residual/recurrent disease, 2 lost to follow-up, 1 second primary) in the IMRT arm and 9 patients (8 residual/recurrence, 1 lost to follow-up) in the BT arm were not evaluable at 6 months for the primary endpoint. At 6 months, xerostomia rates using salivary scintigraphy were 14% (5/35: 95% CI, 5%-30%) in the BT arm while it was seen in 44% (14/32: 95% CI, 26%-62%) in the IMRT arm (P = .008). Physician-rated Radiation Therapy Oncology Group grade ≥2 xerostomia at any time point was observed in 30% of patients (9/30) in the IMRT arm and 6.7% (2/30) in the BT arm (P = .02). At a median follow-up of 42.5 months, the 3-year local control in the IMRT arm was 56.4% (95% CI, 43%-73%) while it was 66.2% (95% CI, 53%-82%) in the BT arm (P = .24). CONCLUSIONS: The addition of BT to IMRT for T1-T2N0M0 OPSCC results in a significant reduction in xerostomia. This strongly supports the addition of BT to IMRT in suitable cases.

Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology.

Cancer is a fatal disease and the second most cause of death worldwide. Treatment of cancer is a complex process and requires a multi-modality-based approach. Cancer detection and treatment starts with screening/diagnosis and continues till the patient is alive. Screening/diagnosis of the disease is the beginning of cancer management and continued with the staging of the disease, planning and delivery of treatment, treatment monitoring, and ongoing monitoring and follow-up. Imaging plays an important role in all stages of cancer management. Conventional oncology practice considers that all patients are similar in a disease type, whereas biomarkers subgroup the patients in a disease type which leads to the development of precision oncology. The utilization of the radiomic process has facilitated the advancement of diverse imaging biomarkers that find application in precision oncology. The role of imaging biomarkers and artificial intelligence (AI) in oncology has been investigated by many researchers in the past. The existing literature is suggestive of the increasing role of imaging biomarkers and AI in oncology. However, the stability of radiomic features has also been questioned. The radiomic community has recognized that the instability of radiomic features poses a danger to the global generalization of radiomic-based prediction models. In order to establish radiomic-based imaging biomarkers in oncology, the robustness of radiomic features needs to be established on a priority basis. This is because radiomic models developed in one institution frequently perform poorly in other institutions, most likely due to radiomic feature instability. To generalize radiomic-based prediction models in oncology, a number of initiatives, including Quantitative Imaging Network (QIN), Quantitative Imaging Biomarkers Alliance (QIBA), and Image Biomarker Standardisation Initiative (IBSI), have been launched to stabilize the radiomic features.

Evaluation of post-chemotherapy residual seminomatous masses by 18F-fluorodeoxyglucose PET/CT using tumor-to-liver ratio - conundrum or solution?

OBJECTIVE: Assessment of diagnostic accuracy of FDG-PET/CT in the detection of viable disease in post-chemotherapy seminomatous residual masses using visual interpretation, SUVmax, and T/L ratio. METHODS: This is a retrospective study assessing the post-chemotherapy seminomatous residual masses of size >3 cm. The PET/CT scan findings were interpreted visually for presence of residual disease which were validated from histopathology reports or imaging follow-up for a maximum of 3 years. SUVmax and T/L ratios were also determined for all the residual lesions. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value NPV were calculated and compared for all three parameters along with ROC analysis to obtain an optimal cutoff value for SUVmax and T/L ratio, respectively. RESULTS: Sample size was 49. Out of these 49 patients, 8 had validation of PET results with histopathology. Rest was validated with imaging follow-up. FDG-PET was positive in 30 patients and negative in 19 patients by visual interpretation. The sensitivity, specificity, PPV, and NPV by this method were 100%, 62.5%, 73%, and 100%, respectively. The SUVmax and T/L ratios were also calculated for these lesions. The cutoff for these two variables was 4.56 and 1.21, respectively. The sensitivity, specificity, PPV, and NPV at these cutoffs were 76%, 87.5%, 86%, 77.7%, and 92%, 87.5%, 88%, 91%, respectively. CONCLUSION: FDG-PET has a favorable diagnostic value in predicting viable disease in post-chemotherapy seminomatous residual masses and using T/L ratio cutoff of 1.21 will increase the specificity of the test.

External Validation of Robust Radiomic Signature to Predict 2-Year Overall Survival in Non-Small-Cell Lung Cancer.

Lung cancer is the second most fatal disease worldwide. In the last few years, radiomics is being explored to develop prediction models for various clinical endpoints in lung cancer. However, the robustness of radiomic features is under question and has been identified as one of the roadblocks in the implementation of a radiomic-based prediction model in the clinic. Many past studies have suggested identifying the robust radiomic feature to develop a prediction model. In our earlier study, we identified robust radiomic features for prediction model development. The objective of this study was to develop and validate the robust radiomic signatures for predicting 2-year overall survival in non-small cell lung cancer (NSCLC). This retrospective study included a cohort of 300 stage I-IV NSCLC patients. Institutional 200 patients' data were included for training and internal validation and 100 patients' data from The Cancer Image Archive (TCIA) open-source image repository for external validation. Radiomic features were extracted from the CT images of both cohorts. The feature selection was performed using hierarchical clustering, a Chi-squared test, and recursive feature elimination (RFE). In total, six prediction models were developed using random forest (RF-Model-O, RF-Model-B), gradient boosting (GB-Model-O, GB-Model-B), and support vector(SV-Model-O, SV-Model-B) classifiers to predict 2-year overall survival (OS) on original data as well as balanced data. Model validation was performed using 10-fold cross-validation, internal validation, and external validation. Using a multistep feature selection method, the overall top 10 features were chosen. On internal validation, the two random forest models (RF-Model-O, RF-Model-B) displayed the highest accuracy; their scores on the original and balanced datasets were 0.81 and 0.77 respectively. During external validation, both the random forest models' accuracy was 0.68. In our study, robust radiomic features showed promising predictive performance to predict 2-year overall survival in NSCLC.

Primary Neuroendocrine Tumor of Prostate in a Case of Metastatic Adenocarcinoma of Lung: Rare Entity with Histopathological and Gallium 68 DOTANOC Positron Emission Tomography Correlation.

Neuroendocrine tumor (NET) of the prostate is an extremely rare entity which represents <1% of the prostatic cancers, but with increasing incidence. Its spectrum encompasses several histological variants ranging from well-differentiated tumor which are often indolent in nature; to aggressive neuroendocrine carcinoma which portends aggressive management. Hence, such rare entities are to be characterized and treated accordingly. We report an unusual case of well-differentiated NET of prostate which was flagged on fluorodeoxyglucose positron emission tomography computed tomography (PET/CT) performed for other indication and confirmed on Gallium-68 DOTANOC PET/CT. Histopathology and immunohistochemistry confirmed the findings subsequently.

Metabolic tumor parameters complement clinicopathological factors in prognosticating advanced stage Hodgkin Lymphoma.

Objectives: Advanced Hodgkin Lymphoma has a higher probability of relapse and recurrence. Classical clinicopathological parameters including the International Prognostic Score (IPS) have not been reliable in predicting prognosis or tailoring treatment. Since FDG PET/CT is the standard of care in staging Hodgkin Lymphoma, this study attempted to evaluate the clinical utility of baseline metabolic tumor parameters in a cohort of advanced Hodgkin lymphoma (stage III and IV). Methods: Histology-proven advanced Hodgkin Patients presenting to our institute between 2012-2016 and treated with chemo-radiotherapy (ABVD / AEVD) were followed up till 2019. Quantitative PET/CT and clinicopathological parameters were used to estimate the Event Free Survival (EFS) in 100 patients. Kaplan-Meier method with log-rank test was used to compare the survival times of prognostic factors. Results: At a median follow-up of 48.83 months (IQR:33.31-63.05 months), the five-year-EFS was 81%. Of the 100 patients, 16 had relapsed (16%) and none died at the last follow-up. On Univariate analysis, among non-PET parameters bulky disease (P=0.03) and B-symptoms (P=0.04) were significant while among PET/CT parameters SUVmax (p=0.001), SUVmean (P=0.002), WBMTV2.5 (P<0.001), WBMTV41% (P<0.001), WBTLG2.5 (P<0.001) and WBTLG41% (P <0.001) predicted poorer EFS. 5-year EFS for patients with low WBMTV2.5 [<1038.3 cm3] was 89% and 35% for patients with high WBMTV2.5 [≥1038.3 cm3] (p <0.001). In a multivariate model, only WBMTV2.5 (P=0.03) independently predicted poorer EFS. Conclusion: PET-based metabolic parameter (WBMTV2.5) was able to prognosticate and complement the classical clinical prognostic factors in advanced Hodgkin Lymphoma. This parameter could have a surrogate value for prognosticating advanced Hodgkin lymphoma. Better prognostication at baseline translates to tailored or risk-modified treatment and hence higher survival.

Machine-Learning-Based Radiomics for Classifying Glioma Grade from Magnetic Resonance Images of the Brain.

Grading of gliomas is a piece of critical information related to prognosis and survival. Classifying glioma grade by semantic radiological features is subjective, requires multiple MRI sequences, is quite complex and clinically demanding, and can very often result in erroneous radiological diagnosis. We used a radiomics approach with machine learning classifiers to determine the grade of gliomas. Eighty-three patients with histopathologically proven gliomas underwent MRI of the brain. Whenever available, immunohistochemistry was additionally used to augment the histopathological diagnosis. Segmentation was performed manually on the T2W MR sequence using the TexRad texture analysis softwareTM, Version 3.10. Forty-two radiomics features, which included first-order features and shape features, were derived and compared between high-grade and low-grade gliomas. Features were selected by recursive feature elimination using a random forest algorithm method. The classification performance of the models was measured using accuracy, precision, recall, f1 score, and area under the curve (AUC) of the receiver operating characteristic curve. A 10-fold cross-validation was adopted to separate the training and the test data. The selected features were used to build five classifier models: support vector machine, random forest, gradient boost, naive Bayes, and AdaBoost classifiers. The random forest model performed the best, achieving an AUC of 0.81, an accuracy of 0.83, f1 score of 0.88, a recall of 0.93, and a precision of 0.85 for the test cohort. The results suggest that machine-learning-based radiomics features extracted from multiparametric MRI images can provide a non-invasive method for predicting glioma grades preoperatively. In the present study, we extracted the radiomics features from a single cross-sectional image of the T2W MRI sequence and utilized these features to build a fairly robust model to classify low-grade gliomas from high-grade gliomas (grade 4 gliomas).

A Rare Case of Plasmablastic Lymphoma of the Ovary with Synchronous Lung Malignancy.

Primary ovarian lymphoma is a rare malignancy with <1% incidence. Plasmablastic lymphoma usually associated with immunocompromised diseases such as HIV rarely involves the ovary; only two case studies are reported in the literature - plasmablastic lymphomatous involvement of an ovarian teratoma and another of plasmablastic variant of B-cell lymphoma involving bilateral ovaries. There are reported case series of synchronous presentation of carcinomas usually including lung, stomach, and colon and nonaggressive lymphomas. Here, we report a rare case of synchronous aggressive primary plasmablastic ovarian lymphoma with adenocarcinoma of the lung, both of which are associated with immune-compromised conditions.

Demonstration of Multiple Metastatic Sites by Positron Emission Tomography/Computed Tomography in a Rare Case of Epithelioid Angiosarcoma of the Scalp.

Epithelioid angiosarcoma is a rare subtype of angiosarcoma, with metastases occurring in more than 50% of cases and the lung is the most organ which is involved. Whole-body fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) has demonstrated its clinical utility in the early detection of metastases in angiosarcoma. It is helpful to differentiate between benign lesions with low FDG uptake as compared to malignancies with high FDG avidity. Here, we present a rare case of a young man with epithelioid angiosarcoma, in which FDG PET/CT has demonstrated metastatic sites (especially lung metastases).

A dosimetric comparison of systemic peptide receptor radionuclide therapy and intra-arterial peptide receptor radionuclide therapy in patients with liver dominant gastroenteropancreatic neuroendocrine tumours.

OBJECTIVES: Intra-arterial radionuclide therapy (IART) treatment allows direct delivery of 177 Lu-DOTATATE to the overexpressed somatostatin-positive neuroendocrine liver metastases, which led to higher tumour concentration compared with systemic radionuclide therapy (SRT). The aim was to evaluate and compare the absorbed doses of both IART and SRT to organs and hepatic metastatic sites. METHODS: A total of 48 patients received SRT and IART. In SRT, activity was administered intravenously, whereas in IART, activity was administered directly into hepatic arteries. The sequential whole-body images were acquired at 2, 4, 24, 72 and 160 h. The reconstructed whole-body planar and single-photon emission computed tomography-computed tomography images were processed using the Dosimetry Toolkit for the estimation of normalized cumulated activity in the organs and tumour lesions. The absorbed dose was computed using OLINDA EXM 2.0 software. RESULTS: The median absorbed dose (mGy/MBq) of kidneys and spleen in IART was compared with SRT and found to be decreased by 30.7% ( P  = 0.03) and 37.5% ( P  = 0.08), whereas it was found to be increased by 40% ( P  = 0.26) and 8.1% ( P  = 0.28) in the liver and lungs. The median dose (mGy/MBq) of tumours determined in IART was found to be increased by 62.2% ( P  = 0.04). CONCLUSION: IART with 177 Lu-DOTATATE significantly increases tumour dose while reducing overall systemic toxicity in comparison to SRT treatment. After considering the maximum tolerance limit of kidneys in peptide receptor radionuclide therapy, the number of treatment cycles and injected activity can be optimized further with IART for better response and survival.

Systematic review and meta-analysis of prediction models used in cervical cancer.

BACKGROUND: Cervical cancer is one of the most common cancers in women with an incidence of around 6.5 % of all the cancer in women worldwide. Early detection and adequate treatment according to staging improve the patient's life expectancy. Outcome prediction models might aid treatment decisions, but a systematic review on prediction models for cervical cancer patients is not available. DESIGN: We performed a systematic review for prediction models in cervical cancer following PRISMA guidelines. Key features that were used for model training and validation, the endpoints were extracted from the article and data were analyzed. Selected articles were grouped based on prediction endpoints i.e. Group1: Overall survival, Group2: progression-free survival; Group3: recurrence or distant metastasis; Group4: treatment response; Group5: toxicity or quality of life. We developed a scoring system to evaluate the manuscript. As per our criteria, studies were divided into four groups based on scores obtained in our scoring system, the Most significant study (Score > 60 %); Significant study (60 % > Score > 50 %); Moderately Significant study (50 % > Score > 40 %); least significant study (score < 40 %). A meta-analysis was performed for all the groups separately. RESULTS: The first line of search selected 1358 articles and finally 39 articles were selected as eligible for inclusion in the review. As per our assessment criteria, 16, 13 and 10 studies were found to be the most significant, significant and moderately significant respectively. The intra-group pooled correlation coefficient for Group1, Group2, Group3, Group4, and Group5 were 0.76 [0.72, 0.79], 0.80 [0.73, 0.86], 0.87 [0.83, 0.90], 0.85 [0.77, 0.90], 0.88 [0.85, 0.90] respectively. All the models were found to be good (prediction accuracy [c-index/AUC/R2] >0.7) in endpoint prediction. CONCLUSIONS: Prediction models of cervical cancer toxicity, local or distant recurrence and survival prediction show promising results with reasonable prediction accuracy [c-index/AUC/R2 > 0.7]. These models should also be validated on external data and evaluated in prospective clinical studies.

FAPI PET Positivity in Fibrolamellar Hepatocellular Carcinoma.

ABSTRACT: Fibrolamellar hepatocellular carcinoma (HCC) is a variant of HCC. It is a malignant tumor, but its imaging features often overlap focal nodular hyperplasia, which is a benign entity. FDG PET/CT is also not much help in these cases because both lesions do not concentrate FDG. We present one such case of fibrolamellar HCC with FAPI PET/CT positivity.

Diagnostic Performance of Response Assessment FDG-PET/CECT in HNSCC Treated With Definitive Radio(chemo)therapy Using NI-RADS.

OBJECTIVE: To assess the diagnostic performance of response assessment 18F-fluorodeoxyglucose positron emission tomography/contrast-enhanced computed tomography (FDG-PET/CECT) following definitive radio(chemo)therapy in head and neck squamous cell carcinoma (HNSCC) using Neck Imaging Reporting and Data System (NI-RADS). STUDY DESIGN: A retrospective analysis from a prospectively maintained dataset. SETTING: Tertiary-care comprehensive cancer center in a low-middle-income country. METHODS: Adults with newly diagnosed, biopsy-proven, nonmetastatic HNSCC treated with definitive radio(chemo)therapy were included. Posttreatment response assessment FDG-PET/CECT scans were retrospectively assigned NI-RADS categories (1-3) for the primary site, neck, and both sites combined. Locoregional recurrence occurring within 2-years was defined as the event of interest. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy were calculated. Locoregional control stratified by NI-RADS categories was computed with the Kaplan-Meier method and compared using the log-rank test. RESULTS: Posttreatment FDG-PET/CECT scans were available in 190 patients constituting the present study cohort. Sensitivity, specificity, PPV, NPV, and overall accuracy of the NI-RADS template for the primary site was 73.5%, 81.4%, 46.3%, 93.4%, and 80.0%, respectively. Similar metrics for the neck were 72.7%, 87.5%, 43.2%, 96.1%, and 85.8%, respectively. Combining primary site and neck, the corresponding metrics of diagnostic accuracy were 84.4%, 69.7%, 46.3%, 93.5%, and 73.2%, respectively. At a median follow-up of 40 months, Kaplan-Meier estimates of 2-year locoregional control were significantly higher for NI-RADS category 1 (94.2%) compared to NI-RADS category 2 (69.4%) and category 3 (20.4%), respectively (stratified log-rank p < .0001). CONCLUSION: FDG-PET/CECT using the NI-RADS template is associated with good diagnostic performance and prognostic utility in HNSCC treated with definitive radio(chemo)therapy.

Diagnostic performance of F-18 FDG PET/CT in recurrent adenocarcinoma gallbladder and its impact on post-recurrence survival.

PURPOSE: To analyze diagnostic performance of F-18 FDG PET/CT in recurrent adenocarcinoma gallbladder (GBC) and to establish its possible impact on post-recurrence survival. METHOD: FDG PET/CT studies of suspected recurrent GBC were retrospectively analyzed alongside tumor markers serum CEA and CA 19-9. Abnormal FDG-avid lesions and abnormal morphological lesions were considered positive for recurrence, and were categorized as isolated abdominal wall recurrence, loco-regional recurrence, and distant metastatic disease. Histopathology, definite progression on imaging and positive response to treatment was considered as reference standard. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were used as diagnostic performance parameters. Post-recurrence survival was calculated whenever appropriate follow-up was available, based on the abovementioned categories of sites of recurrence using survival curves and log-rank test. RESULTS: Out of 117 PET/CT studies, 93 (79.5%) were positive and 24 (20.5%) were negative for recurrence. 86 out of 93 were true positive and 23 of 24 were true negative. PET/CT demonstrated sensitivity, specificity, PPV, NPV and accuracy of 98.8%, 76.7%, 92.5%, 95.8% and 93.1%, respectively. Diagnostic performance of PET/CT was significantly better than combination tumor markers. Of 66 cases with available follow-up, isolated abdominal wall (port/scar site) recurrence and loco-regional recurrence demonstrated significantly higher post-recurrence survival as compared to distant metastasis; median survival being 39, 25 and 12 months, respectively. CONCLUSION: F-18 FDG PET/CT has better diagnostic performance than tumor markers combination. Isolated abdominal wall (port/scar site) recurrence and loco-regional recurrence on PET/CT demonstrated better survival than non-regional metastatic disease. These results suggest a possible role of PET/CT as a surveillance modality, as well as a guide to therapeutic decision-making in cases of recurrent GBC.

Sporadic Cerebellar Hemangioblastoma With Strong SSTR Expression on 68 Ga-DOTANOC PET/CT.

ABSTRACT: Sporadic cerebellar hemangioblastomas are rare with majority of these tumors presenting as a part of von Hippel-Lindau syndrome. We demonstrate an unusual case of a symptomatic sporadic cerebellar hemangioblastoma mimicking a meningioma on MRI and 68 Ga-DOTANOC PET imaging.