PRRT with Lu-177 DOTATATE in Treatment-Refractory Progressive Meningioma: Initial Experience from a Tertiary-Care Neuro-Oncology Center.

PURPOSE: Refractory and/or recurrent meningiomas have poor outcomes, and the treatment options are limited. Peptide receptor radionuclide therapy (PRRT) has been used in this setting with promising results. We have documented our experience of using intravenous (IV) and intra-arterial (IA) approaches of Lu-177 DOTATATE PRRT. METHODS: Eight patients with relapsed/refractory high-grade meningioma received PRRT with Lu-177 DOTATATE by IV and an IA route. At least 2 cycles were administered. Time to progression was calculated from the first PRRT session to progression. The response was assessed on MRI using RANO criteria, and visual analysis of uptake was done on Ga-68 DOTANOC PET/CT. Post-therapy dosimetry calculations for estimating the absorbed dose were performed. RESULTS: Median time to progression was 8.9 months. One patient showed disease progression, whereas seven patients showed stable disease at 4 weeks following 2 cycles of PRRT. Dosimetric analysis showed higher dose and retention time by IA approach. No significant peri-procedural or PRRT associated toxicity was seen. CONCLUSION: PRRT is a safe and effective therapeutic option for relapsed/refractory meningioma. The IA approach yields better dose delivery and should be routinely practised.

Can 18F FDG PET/CT metabolic parameters be used to noninvasively differentiate between different histopathological subtypes and Fuhrman grades of renal cell cancer?

AIM: To evaluate relationship between metabolic PET metabolic parameters and size of the primary tumor, various histopathological subtypes of renal cell carcinoma (RCC) and Fuhrman grade of the tumors. MATERIAL AND METHODS: Retrospective analysis of 93 biopsy-proven RCC patients who underwent pretreatment flourine 18 flourodeoxyglucose PET/computed tomography (18F FDG PET/CT) was performed. Quantitative PET parameters, size of the primary tumor, histopathological subtypes and Fuhrman grades of the tumor were extracted. We tried to assess if there was any significant difference in the metabolic patterns of various histopathological subtypes of RCCs, Fuhrman grade of the tumors and size of the primary tumor. RESULTS: A significant correlation was noted between the size of primary tumor and maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) (P < 0.01, P < 0.001 and P < 0.001, respectively). SUVmax values correlated significantly with the histopathological subtype (P < 0.001). Further sub-analyses was also done by segregating the patients into Low grade (Fuhrman grade 1 and 2) vs. High grade (Fuhrman grade 3 and 4). SUVmax, MTV and TLG were significantly different between high grade vs. low grade tumors. ROC analysis yielded cut off values for SUVmax, MTV and TLG to differentiate between high grade from low grade tumors. CONCLUSION: FDG PET/CT with the use of metabolic PET parameters can differentiate between different histopathological subtypes of RCC. Incorporation of metabolic parameters into clinical practice can potentially noninvasively identify patients with low-grade vs. high-grade RCC.

SALTT study: A retrospective analysis of 111 SAlivary gland tumors of Lung and Tracheobronchial Tree.

INTRODUCTION: Primary pulmonary salivary gland-type tumours (PPSGT) are rare lung neoplasms arising from submucosal seromucinous glands in the central airway. METHODS AND RESULTS: We retrospectively analysed the clinicopathological features of 111 PPSGTs diagnosed at our institute between 2003 and 2021. The mean age at diagnosis was 43.8 years(range 6-78 years) and a male-to-female ratio of 2:1. On imaging, 92 % of cases had centrally located tumours and 37.3 % were early stage. The histopathological types included 70 cases (63 %) of mucoepidermoid carcinoma (MEC), 31 cases (27.7 %) of adenoid cystic carcinoma (ADCC), two cases of myoepithelial carcinoma, one case each of acinic cell carcinoma (ACC), clear cell carcinoma (CCC), epithelial myoepithelial carcinoma (EMC) and 5 others [including adenocarcinoma of minor salivary gland origin(n = 3), carcinoma with sebaceous differentiation(n = 1) and poorly differentiated carcinoma of salivary gland type(n = 1)]. The size of the tumours found in the resection specimens ranged from 1 cm to 13 cm, with an average size of 4.9 cm. High-risk attributes such as lymphovascular invasion (LVI), perineural invasion (PNI), pleural involvement, positive resection margins, and nodal metastasis were identified in 15.3 %, 15.3 %, 13.6 %,15.2 % and 6.7 % of cases, respectively. These attributes were found to be more frequent in ADCC than in MEC. Surgery was the main treatment modality [68/84 (80 %) cases]. ADCC cases had more recurrence and distant metastasis than MEC cases. The 3- year overall-survival (OS) and recurrence-free survival(RFS) were better in patients with age lesser than 60 years(p-value <0.0001), low pT stage (p-value 0.00038) and lower grade of MEC(p-value-0.0067). CONCLUSION: It is crucial to have an acquaintance with the morphologic spectrum and immunophenotypic characteristics of PPSGT to recognize them in this unusual location. In tandem, it is crucial to differentiate them from conventional primary non-small cell lung carcinoma, as the management protocols and prognostic implications differ significantly.

Clinical outcomes of ROS1-positive non-small cell lung cancer with limited access to ROS1-tyrosine kinase inhibitors (TKIs): experience from an Indian tertiary referral centre.

Introduction: ROS1 as a driver mutation is observed in approximately 1%-2% of all non-small cell lung cancer (NSCLC). Given its rarity, we share our experience regarding ROS1-positive NSCLC including the access to ROS1 tyrosine kinase inhibitors (TKIs) in a low-middle income country like India. Methods: It is a retrospective analysis of ROS1-positive NSCLC patients registered between January 2015 to December 2021 for demographics, treatment patterns and outcomes i.e., overall survival (OS) and progression free survival (PFS). Results: Baseline characteristics were available for 70 patients of 78 patients positive for ROS1 by fluorescent in situ hybridisation. Median age at presentation was 52 years, 39 (55.7%) were males, most (51, 72.86%) were non-smokers and ten patients (14.3%) had poor Eastern Cooperative Oncology Group (ECOG) performance status (PS) i.e., PS >2 at presentation. A total of 67 patients receiving cancer directed therapy were analysed for survival. The first line (1L) therapies included - ROS1 TKIs in 38, chemotherapy in 20, epidermal growth factor receptor TKI in eight and chemotherapy-bevacizumab in one only. ROS1 TKI was provided to 20 patients as part of an assistance programme. The median OS for patients who received ROS1 TKI was not attained (95% CI 37.85-NA), while it was 8.11 (95% CI 6.31-NA) months for those who did not (HR-0.1673). The median PFS for the 1L ROS1 TKI compared to the no-TKI group was 27.07 (95% CI 24.28-NA) months versus 5.78 (95% CI 3.42-12) months (HR: 0.2047). Poor ECOG PS at presentation was the only independent prognosticator for survival. Conclusion: Using ROS1 TKI improves clinical outcomes in all-comers though statistically not significant. To further improve outcomes, future trials should pay special attention to patients with poor PS and find a way to increase the current limited access to TKI.

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.

A Comparative Study Evaluating the Quality of Life and Survival Outcomes in Patients Receiving Chemotherapy Versus Oral Tyrosine Kinase Inhibitor in the Third Line and Beyond Setting for Advanced NSCLC.

Introduction: The outcomes in advanced NSCLC have improved owing to the availability of more effective systemic and improved supportive care. This has increased the number of patients who seek treatment in the third line and beyond setting. We conducted this study to compare the quality of life (QoL), toxicity, and outcomes in patients receiving chemotherapy and EGFR tyrosine kinase inhibitors (TKIs) in this setting. Methods: In this phase 3, randomized, open-label study, patients with stage III or IV NSCLC with disease progression on at least two prior lines of chemotherapy, with a life expectancy of at least 3 months, without prior EGFR TKI exposure, and stable brain metastases (if any) were included. Patients were randomized to receive chemotherapy (gemcitabine or docetaxel or paclitaxel or vinorelbine) or an EGFR TKI (erlotinib or gefitinib). The primary end point was the change in QoL at 8 to 10 weeks; the secondary outcomes were safety and overall survival (OS). Patients underwent clinical evaluation at every visit, and toxicity was assessed as per Common Terminology Criteria for Adverse Events version 4.03. A radiological tumor response assessment was done every 8 to 12 weeks from the start of therapy. The QoL was assessed using the EORTC QLQ C30 and LC13 questionnaires. The change in QoL scores was calculated as the difference between scores at baseline and scores at 8 to 10 weeks (Δ) for each QoL domain. The Mann-Whitney U test was used to compare the mean difference (Δ) for each domain. OS and progression-free survival (PFS) were determined using the Kaplan-Meier method and Cox proportional regression analysis. Results: A total of 246 patients were enrolled in the study, with 123 in each arm. There was a male predominance with 69.1% male patients in the chemotherapy arm and 70.7% in the EGFR TKI arm. The median age of patients in the chemotherapy arm was 54 years and 55 years in the chemotherapy and EGFR TKI arms, respectively. There was no significant difference in the change in QoL at baseline and the second visit (Δ) in both arms in all domains of EORTC QLQ C30 except cognitive function (p = 0.0045) and LC13 except alopecia (0.01249). The mean Δ Global Health Status was -28 in the chemotherapy arm and -26.8 in the EGFR TKI arm; this was not statistically significant (p = 0.973). The median follow-up was 88.1 months (95% confidence interval [CI]: 39.04-137.15). On the intention-to-treat analysis, the median PFS was 3.13 months (95% CI: 2.15-4.11) in the chemotherapy arm and 2.26 months (95% CI: 2.1-2.43) in the EGFR TKI arm, with hazard ratio at 1.074 (95% CI: 0.83-1.38) (p = 0.58). There were 120 deaths in each arm. The median OS was 7.63 months (95% CI: 5.96-9.30) in the chemotherapy arm and 7.5 months in the EGFR TKI arm (95% CI: 5.85-9.14); hazard ratio at 1.033 (95% CI: 0.80-1.33) (p = 0.805). The toxicity profile was similar in both arms except for a significantly higher incidence of fatigue (p = 0.043), peripheral neuropathy (0.000), alopecia, hypokalemia (0.037), and pedal edema (0.007) in the chemotherapy arm and dry skin (p = 0.000) and skin rash (p = 0.019) in the EGFR TKI arm. Conclusions: There was no significant difference in most QoL scales (except cognitive function and alopecia), OS, and PFS of patients with advanced NSCLC receiving an EGFR TKI as compared with chemotherapy TKI in the third-line setting. The toxicity profile is consistent with the known toxicities of the agents.

Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer.

Aim: The aim of this study was to investigate the feasibility of developing a deep learning (DL) algorithm for classifying brain metastases from non-small cell lung cancer (NSCLC) into epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement groups and to compare the accuracy with classification based on semantic features on imaging. Methods: Data set of 117 patients was analysed from 2014 to 2018 out of which 33 patients were EGFR positive, 43 patients were ALK positive and 41 patients were negative for either mutation. Convolutional neural network (CNN) architecture efficient net was used to study the accuracy of classification using T1 weighted (T1W) magnetic resonance imaging (MRI) sequence, T2 weighted (T2W) MRI sequence, T1W post contrast (T1post) MRI sequence, fluid attenuated inversion recovery (FLAIR) MRI sequences. The dataset was divided into 80% training and 20% testing. The associations between mutation status and semantic features, specifically sex, smoking history, EGFR mutation and ALK rearrangement status, extracranial metastasis, performance status and imaging variables of brain metastasis were analysed using descriptive analysis [chi-square test (χ2)], univariate and multivariate logistic regression analysis assuming 95% confidence interval (CI). Results: In this study of 117 patients, the analysis by semantic method showed 79.2% of the patients belonged to ALK positive were non-smokers as compared to double negative groups (P = 0.03). There was a 10-fold increase in ALK positivity as compared to EGFR positivity in ring enhancing lesions patients (P = 0.015) and there was also a 6.4-fold increase in ALK positivity as compared to double negative groups in meningeal involvement patients (P = 0.004). Using CNN Efficient Net DL model, the study achieved 76% accuracy in classifying ALK rearrangement and EGFR mutations without manual segmentation of metastatic lesions. Analysis of the manually segmented dataset resulted in improved accuracy of 89% through this model. Conclusions: Both semantic features and DL model showed comparable accuracy in classifying EGFR mutation and ALK rearrangement. Both methods can be clinically used to predict mutation status while biopsy or genetic testing is undertaken.

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.

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.

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.

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).

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.

Dual-phase computed tomography for localization of parathyroid lesions in children and adolescents with primary hyperparathyroidism.

BACKGROUND: Childhood and adolescent primary hyperparathyroidism (PHPT) is a rare disease caused by single adenomas in 65-94% of patients. In this patient group, there is no data on computed tomography (CT) for pre-operative parathyroid localization that may facilitate focused parathyroidectomy. METHODS: Two radiologists reviewed dual-phase (nonenhanced and arterial) CT images of twenty-three operated children and adolescents [20:single-gland disease(SGD), 3:multi-glandular disease(MGD)] with proven histopathological PHPT. Percentage arterial enhancement (PAE) was calculated as [100*{arterial-phase Hounsfield unit (HU)-nonenhanced phase HU}/nonenhanced HU] of the parathyroid lesion(s), thyroid, and lymph node. RESULTS: Dual-phase CT lateralized 100%, localized to the correct quadrant/site 85% SGD (including 3/3 ectopic), and identified 1/3 MGD. PAE (cutoff ≥ 112.3%) was sensitive (91.3%) and specific (99.5%) in distinguishing parathyroid lesions from local mimics (P<0.001). The average effective dose was 3.16±1.01mSv, comparable to the planar/single photon emission CT (SPECT) Technetium 99m(Tc)-sestamibi and choline positron emission tomography (PET)/CT scans. Solid-cystic morphology identified in 4 patients harboring pathogenic germline variants (3:CDC73, 1:CASR) may serve as a radiological clue to molecular diagnosis. Nineteen out of 20 (95%) patients with SGD who had undergone single gland resection based on pre-operative CT findings were in remission over a median follow-up of 18 months. CONCLUSION: As most children/adolescents with PHPT have SGD, dual-phase CT protocols which reduce the effective radiation dose with high localization sensitivity for single parathyroid lesions may be a sustainable pre-operative imaging modality in this patient group.

Clinical Concept-Based Radiology Reports Classification Pipeline for Lung Carcinoma.

Rising incidence and mortality of cancer have led to an incremental amount of research in the field. To learn from preexisting data, it has become important to capture maximum information related to disease type, stage, treatment, and outcomes. Medical imaging reports are rich in this kind of information but are only present as free text. The extraction of information from such unstructured text reports is labor-intensive. The use of Natural Language Processing (NLP) tools to extract information from radiology reports can make it less time-consuming as well as more effective. In this study, we have developed and compared different models for the classification of lung carcinoma reports using clinical concepts. This study was approved by the institutional ethics committee as a retrospective study with a waiver of informed consent. A clinical concept-based classification pipeline for lung carcinoma radiology reports was developed using rule-based as well as machine learning models and compared. The machine learning models used were XGBoost and two more deep learning model architectures with bidirectional long short-term neural networks. A corpus consisting of 1700 radiology reports including computed tomography (CT) and positron emission tomography/computed tomography (PET/CT) reports were used for development and testing. Five hundred one radiology reports from MIMIC-III Clinical Database version 1.4 was used for external validation. The pipeline achieved an overall F1 score of 0.94 on the internal set and 0.74 on external validation with the rule-based algorithm using expert input giving the best performance. Among the machine learning models, the Bi-LSTM_dropout model performed better than the ML model using XGBoost and the Bi-LSTM_simple model on internal set, whereas on external validation, the Bi-LSTM_simple model performed relatively better than other 2. This pipeline can be used for clinical concept-based classification of radiology reports related to lung carcinoma from a huge corpus and also for automated annotation of these reports.