Indian Journal of Radiology and Imaging最新文献

{"title":"Pretherapeutic PSMA PET-Derived Semiquantitative Parameters as Predictors of PSA Response in Patients with mCRPC Receiving [ ¹⁷⁷ Lu]Lu-PSMA-617 Radioligand Therapy.","authors":"Dheeratama Siripongsatian, Attapon Jantarato, Chetsadaporn Promteangtrong, Anchisa Kunawudhi, Peerapon Kiatkittikul, Natphimol Boonkawin, Sukanya Yaset, Sirinsuda Somboon, Chanisa Chotipanich","doi":"10.1055/s-0044-1779634","DOIUrl":"https://doi.org/10.1055/s-0044-1779634","url":null,"abstract":"<p><p><b>Objective</b>  [ ¹⁷⁷ Lu]Lu-prostate-specific membrane antigen (PSMA)-617 radioligand therapy (RLT) shows promise for metastatic castration-resistant prostate cancer (mCRPC) patients with positive PSMA positron emission tomography (PET) imaging. Identifying high-risk patients is crucial. We evaluated pretherapeutic PSMA PET-derived parameters to predict prostate-specific antigen (PSA) response in patients undergoing [ ¹⁷⁷ Lu]Lu-PSMA-617 RLT. <b>Materials and Methods</b>  We conducted a retrospective analysis among 27 patients (mean age: 71.0 ± 9.5 years; range: 52-85 years) who underwent PSMA PET/computed tomography (CT) and subsequent [ ¹⁷⁷ Lu]Lu-PSMA-617 RLT between March 2019 and January 2023. After excluding patients with liver metastases, the number of patients left for analysis was 21 (14 responders and 7 nonresponders). Tumors were semiautomatically delineated with calculation of total tumor volume (PSMA-TV), lesion uptake (PSMA-TLU = PSMA-TV * standardized uptake value [SUV]mean), and lesion quotient (PSMA-TLQ = PSMA-TV/SUVmean) for each patient. Semiquantitative parameters were analyzed only in patients with mCRPC and no liver metastasis. <b>Results</b>  In total, 17/27 patients (62.96%) had a decline in PSA levels; 15/27 patients (55.56%) experienced a decline of > 50%. Pretherapeutic PSMA PET/CT results revealed significant differences in PSMA-TV ( <i>p</i>  = 0.003), PSMA-TLU ( <i>p</i>  = 0.013), and PSMA-TLQ ( <i>p</i>  = 0.011) between responders and nonresponders. SUVmax was significantly correlated to the best percentage change in PSA response after ¹⁷⁷ Lu-PSMA-617 treatment ( <i>r</i>  = -0.79, <i>p</i>  = 0.006). No association was observed between PSMA-TV ( <i>p</i>  = 0.367), PSMA-TLU ( <i>p</i>  = 0.128), and PSMA-TLQ ( <i>p</i>  = 0.556), with the best percentage change in PSA response after ¹⁷⁷ Lu-PSMA-617 therapy. <b>Conclusion</b>  Pretherapeutic PSMA PET-derived PSMA-TV, PSMA-TLU, and PSMA-TLQ were significant negative predictors of PSA response in patients with mCRPC and no liver metastasis receiving [ ¹⁷⁷ Lu]Lu-PSMA-617 RLT.</p>","PeriodicalId":51597,"journal":{"name":"Indian Journal of Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11419753/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142332165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiomics-Based Machine Learning Classification Strategy for Characterization of Hepatocellular Carcinoma on Contrast-Enhanced Ultrasound in High-Risk Patients with LI-RADS Category M Nodules","authors":"Lingling Li, Xiaoxin Liang, Yiwen Yu, Rushuang Mao, Jing Han, Chuan Peng, Jianhua Zhou","doi":"10.1055/s-0043-1777993","DOIUrl":"https://doi.org/10.1055/s-0043-1777993","url":null,"abstract":"Abstract Objective  Accurate differentiation within the LI-RADS category M (LR-M) between hepatocellular carcinoma (HCC) and non-HCC malignancies (mainly intrahepatic cholangiocarcinoma [CCA] and combined hepatocellular and cholangiocarcinoma [cHCC-CCA]) is an area of active investigation. We aimed to use radiomics-based machine learning classification strategy for differentiating HCC from CCA and cHCC-CCA on contrast-enhanced ultrasound (CEUS) images in high-risk patients with LR-M nodules. Methods  A total of 159 high-risk patients with LR-M nodules (69 HCC and 90 CCA/cHCC-CCA) who underwent CEUS within 1 month before pathologic confirmation from January 2006 to December 2019 were retrospectively included (111 patients for training set and 48 for test set). The training set was used to build models, while the test set was used to compare models. For each observation, six CEUS images captured at predetermined time points (T1, peak enhancement after contrast injection; T2, 30 seconds; T3, 45 seconds; T4, 60 seconds; T5, 1–2 minutes; and T6, 2–3 minutes) were collected for tumor segmentation and selection of radiomics features, which included seven types of features: first-order statistics, shape (2D), gray-level co-occurrence matrix, gray-level size zone matrix, gray-level run length matrix, neighboring gray tone difference matrix, and gray-level dependence matrix. Clinical data and key radiomics features were employed to develop the clinical model, radiomics signature (RS), and combined RS-clinical (RS-C) model. The RS and RS-C model were built using the machine learning framework. The diagnostic performance of these three models was calculated and compared. Results  Alpha-fetoprotein (AFP), CA19-9, enhancement pattern, and time of washout were included as independent factors for clinical model (all p  < 0.05). Both the RS and RS-C model performed better than the clinical model in the test set (area under the curve [AUC] of 0.698 [0.571–0.812] for clinical model, 0.903 [0.830–0.970] for RS, and 0.912 [0.838–0.977] for the RS-C model; both p  < 0.05). Conclusions  Radiomics-based machine learning classifiers may be competent for differentiating HCC from CCA and cHCC-CCA in high-risk patients with LR-M nodules.","PeriodicalId":51597,"journal":{"name":"Indian Journal of Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139527536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of CT Features to Differentiate Pulmonary Sarcoma from Carcinoma","authors":"S. Mohan, E. Dhamija, S. Bakhshi, PrabhatSingh Malik, S. Rastogi, Chandrashekhara Sheragaru Hanumanthappa, D. Jain, R. Pandey","doi":"10.1055/s-0043-1777834","DOIUrl":"https://doi.org/10.1055/s-0043-1777834","url":null,"abstract":"Abstract Background  Primary lung sarcoma (PLS) differs in management protocols and prognosis from the more common primary lung carcinoma (PLC). It becomes imperative to raise a high index of suspicion on radiological and pathological features. Purpose  The aim of this study is to highlight the variable imaging appearances of PLS compared with PLC, which impacts radiologic - pathologic correlation. Materials and Methods  A retrospective observational study of 68 patients with biopsy-proven lung tumors who underwent baseline imaging at our tertiary care cancer hospital was conducted between January 2018 and March 2022. The patient details and imaging parameters of the mass on contrast-enhanced computed tomography (CECT) were recorded and analyzed for patients with PLS and compared with PLC. Follow-up imaging was available in 9/12 PLS and 52/56 PLC patients. Results  Among 12 patients with PLS, 5 patients had synovial sarcoma on histopathology. PLS was seen in patients with a mean age of 40.8 years; the mass showed a mean size of 13.2 cm, lower lobe (75%), parahilar (75%), hilar involvement (41.7%), oval shape (41.7%), circumscribed (25%) or lobulated (75%) margins, lower mean postcontrast attenuation of 57.3 HU, fissural extension (50%), calcification (50%), and no organ metastasis other than to the lung. PLC (56 patients) was seen in the elderly with a mean age of 54.8 years; the mass showed a mean size of 5.7 cm, irregular shape (83.9%), spiculated margins (73.2%), higher mean postcontrast attenuation (77.3 HU), chest wall infiltration (30.4%), and distant metastasis (58.9%) at baseline imaging. A statistically significant difference ( p  < 0.05) was seen between sarcoma and carcinoma in the mean age, size, site, shape, margins, postcontrast attenuation, presence of calcifications, fissural extension, and distant metastasis. Conclusion  The distinct imaging features of sarcoma help in differentiating it from carcinoma. This can also be used to corroborate with histopathology to achieve concordance and guide clinicians on further approach.","PeriodicalId":51597,"journal":{"name":"Indian Journal of Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139527539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic Utility of Integration of Dynamic Contrast-Enhanced and Dynamic Susceptibility Contrast MR Perfusion Employing Split Bolus Technique in Differentiating High-Grade Glioma","authors":"Virender Malik, C. Kesavadas, B. Thomas, Deepti A. N., Krishna Kumar K.","doi":"10.1055/s-0043-1777742","DOIUrl":"https://doi.org/10.1055/s-0043-1777742","url":null,"abstract":"Abstract Background : Despite documented correlation between glioma grades and dynamic contrast-enhanced (DCE) magnetic resonance (MR) perfusion-derived parameters, and its inherent advantages over dynamic susceptibility contrast (DSC) perfusion, the former remains underutilized in clinical practice. Given the inherent spatial heterogeneity in high-grade diffuse glioma (HGG) and assessment of different perfusion parameters by DCE (extravascular extracellular space volume [Ve] and volume transfer constant in unit time [k-trans]) and DSC (rCBV), integration of the two into a protocol could provide a holistic assessment. Considering therapeutic and prognostic implications of differentiating WHO grade 3 from 4, we analyzed the two grades based on a combined DCE and DSC perfusion. Methods : Perfusion sequences were performed on 3-T MR. Cumulative dose of 0.1 mmol/kg of gadodiamide, split into two equal boluses, was administered with an interval of 6 minutes between the DCE and DSC sequences. DCE data were analyzed utilizing commercially available GenIQ software. Results : Of the 41 cases of diffuse gliomas analyzed, 24 were WHO grade III and 17 grade IV gliomas (2016 WHO classification). To differentiate grade III and IV gliomas, Ve cutoff value of 0.178 provided the best combination of sensitivity (88.24%) and specificity (87.50%; AUC: 0.920; p  < 0.001). A relative cerebral blood volume (rCBV) of value 3.64 yielded a sensitivity of 70.59% and specificity of 62.50% ( p  = 0.018). The k-trans value, although higher in grade III than in grade IV gliomas, did not reach statistical significance ( p  = 0.108). Conclusion : Uniqueness of employed combined perfusion technique, treatment naïve patients at imaging, user-friendly postprocessing software utilization, and ability of Ve and rCBV to differentiate between grade III and IV gliomas ( p  < 0.05) are the strengths of the present study, contributing to the existing literature and moving a step closer to achieving accurate MR perfusion-based glioma grading.","PeriodicalId":51597,"journal":{"name":"Indian Journal of Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139527673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Congenital Lateral Abdominal Wall Hernia","authors":"S. Chawla, Aditya Charan","doi":"10.1055/s-0043-1777290","DOIUrl":"https://doi.org/10.1055/s-0043-1777290","url":null,"abstract":"","PeriodicalId":51597,"journal":{"name":"Indian Journal of Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139527076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the Capability of ChatGPT, Google Bard, and Microsoft Bing in Solving Radiology Case Vignettes","authors":"Pradosh Kumar Sarangi, Ravi Kant Narayan, S. Mohakud, Aditi Vats, Debabrata Sahani, Himel Mondal","doi":"10.1055/s-0043-1777746","DOIUrl":"https://doi.org/10.1055/s-0043-1777746","url":null,"abstract":"Abstract Background  The field of radiology relies on accurate interpretation of medical images for effective diagnosis and patient care. Recent advancements in artificial intelligence (AI) and natural language processing have sparked interest in exploring the potential of AI models in assisting radiologists. However, limited research has been conducted to assess the performance of AI models in radiology case interpretation, particularly in comparison to human experts. Objective  This study aimed to evaluate the performance of ChatGPT, Google Bard, and Bing in solving radiology case vignettes (Fellowship of the Royal College of Radiologists 2A [FRCR2A] examination style questions) by comparing their responses to those provided by two radiology residents. Methods  A total of 120 multiple-choice questions based on radiology case vignettes were formulated according to the pattern of FRCR2A examination. The questions were presented to ChatGPT, Google Bard, and Bing. Two residents wrote the examination with the same questions in 3 hours. The responses generated by the AI models were collected and compared to the answer keys and explanation of the answers was rated by the two radiologists. A cutoff of 60% was set as the passing score. Results  The two residents (63.33 and 57.5%) outperformed the three AI models: Bard (44.17%), Bing (53.33%), and ChatGPT (45%), but only one resident passed the examination. The response patterns among the five respondents were significantly different ( p  = 0.0117). In addition, the agreement among the generative AI models was significant (intraclass correlation coefficient [ICC] = 0.628), but there was no agreement between the residents (Kappa = –0.376). The explanation of generative AI models in support of answer was 44.72% accurate. Conclusion  Humans exhibited superior accuracy compared to the AI models, showcasing a stronger comprehension of the subject matter. All three AI models included in the study could not achieve the minimum percentage needed to pass an FRCR2A examination. However, generative AI models showed significant agreement in their answers where the residents exhibited low agreement, highlighting a lack of consistency in their responses.","PeriodicalId":51597,"journal":{"name":"Indian Journal of Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139143939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bedside Ultrasound-Guided Percutaneous Cholecystostomy in Critically Ill Patients—Outcomes in 51 Patients","authors":"Rozil Gandhi, K. Gala, Mohd Shariq, Aditi Gandhi, Manish Gandhi, Amit Shah","doi":"10.1055/s-0043-1777744","DOIUrl":"https://doi.org/10.1055/s-0043-1777744","url":null,"abstract":"Abstract Purpose  The aim of this study was to report technical and clinical success of bedside ultrasound-guided percutaneous cholecystostomy (PC) tube placement in intensive care unit (ICU). Materials and Methods  This is a retrospective study of 51 patients (36 males:15 females, mean age: 67 years) who underwent ultrasound-guided PC from May 2015 to January 2020. The indication for cholecystostomy tube placement, comorbidities, imaging finding, technical success, clinical success, timing of surgery post-cholecystostomy tube placement, indwelling catheter time, complications, and follow-up were recorded. Results  Indications for cholecystostomy tube placement were acute calculous cholecystitis ( n  = 43; 84.3%), perforated cholecystitis ( n  = 5; 9.8%), and emphysematous cholecystitis ( n  = 3; 5.9%). Most of the patients had multiple comorbidities; these were diabetes mellitus, hypertension, cardiovascular disease, chronic renal disease, underlying malignancy, and multisystem disease with sepsis. All patients had undergone PC through transhepatic approach under ultrasound guidance in ICU. Technical success rate of the procedure was 100%. Clinical success rate was 92.1% (47/51) and among these 44/51 (86.2%) patients underwent definitive elective cholecystectomy, 3/51 (5.9%) patients had elective tube removal. Three of fifty-one (5.9%) patients did not improve; among these two underwent emergency surgery, while there was 1/51 (1.9%) mortality due to ongoing sepsis and multiorgan dysfunction. There were no procedure-related mortalities or procedure-related major complications. One patient had bile leak due to multiple attempts for cholecystostomy placement. Mean tube indwelling time was 13 days (range: 3–45 days). Conclusion  Ultrasound-guided PC can be safely performed in ICU in critically ill patients unfit for surgery with high technical and clinical success rates. Early laparoscopic cholecystectomy should be preferred after stabilization of clinical condition following cholecystostomy.","PeriodicalId":51597,"journal":{"name":"Indian Journal of Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139152442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Macrodystrophia Lipomatosa: A Rare Case of Ulnar Nerve Territory Involvement","authors":"Sonali Ullal, Shivani Arora","doi":"10.1055/s-0043-1777745","DOIUrl":"https://doi.org/10.1055/s-0043-1777745","url":null,"abstract":"Abstract Macrodystrophia lipomatosa (MDL) is a rare congenital, nonhereditary anomaly characterized by overgrowth of all the mesenchymal elements, predominantly the fibroadipose tissue in a sclerotomal distribution commonly involving the median nerve territory in the upper extremity and plantar nerve territory in the lower extremity. It can be either static or progressive, with the former being the more common. MDL is usually present since birth and the affected digit/region increases in length and girth, and growth ceases after puberty. We discuss a rare case of ulnar nerve territory involvement that progressed to grow even after puberty.","PeriodicalId":51597,"journal":{"name":"Indian Journal of Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139149452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiological Differential Diagnoses Based on Cardiovascular and Thoracic Imaging Patterns: Perspectives of Four Large Language Models","authors":"Pradosh Kumar Sarangi, A. Irodi, Swaha Panda, Debasish Swapnesh Kumar Nayak, Himel Mondal","doi":"10.1055/s-0043-1777289","DOIUrl":"https://doi.org/10.1055/s-0043-1777289","url":null,"abstract":"Abstract Background  Differential diagnosis in radiology is a critical aspect of clinical decision-making. Radiologists in the early stages may find difficulties in listing the differential diagnosis from image patterns. In this context, the emergence of large language models (LLMs) has introduced new opportunities as these models have the capacity to access and contextualize extensive information from text-based input. Objective  The objective of this study was to explore the utility of four LLMs—ChatGPT3.5, Google Bard, Microsoft Bing, and Perplexity—in providing most important differential diagnoses of cardiovascular and thoracic imaging patterns. Methods  We selected 15 unique cardiovascular ( n  = 5) and thoracic ( n  = 10) imaging patterns. We asked each model to generate top 5 most important differential diagnoses for every pattern. Concurrently, a panel of two cardiothoracic radiologists independently identified top 5 differentials for each case and came to consensus when discrepancies occurred. We checked the concordance and acceptance of LLM-generated differentials with the consensus differential diagnosis. Categorical variables were compared by binomial, chi-squared, or Fisher's exact test. Results  A total of 15 cases with five differentials generated a total of 75 items to analyze. The highest level of concordance was observed for diagnoses provided by Perplexity (66.67%), followed by ChatGPT (65.33%) and Bing (62.67%). The lowest score was for Bard with 45.33% of concordance with expert consensus. The acceptance rate was highest for Perplexity (90.67%), followed by Bing (89.33%) and ChatGPT (85.33%). The lowest acceptance rate was for Bard (69.33%). Conclusion  Four LLMs—ChatGPT3.5, Google Bard, Microsoft Bing, and Perplexity—generated differential diagnoses had high level of acceptance but relatively lower concordance. There were significant differences in acceptance and concordance among the LLMs. Hence, it is important to carefully select the suitable model for usage in patient care or in medical education.","PeriodicalId":51597,"journal":{"name":"Indian Journal of Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139149597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Atypical Meningioma with Perineural Spread Along Hypoglossal Nerve","authors":"S. Rastogi, K. Bhattacharya, Aayush Mathur, Arpita A. Sahu, Amit Chaudhari, Epari Shridhar","doi":"10.1055/s-0043-1777743","DOIUrl":"https://doi.org/10.1055/s-0043-1777743","url":null,"abstract":"A 50-year-old female presented with acute onset bilateral limb weakness, episodes of severe occipital headache with multiple episodes of loss ofconsciousnesswith a background history of left side neck pain, and occipital headache in the past 4 years. There was no signi fi cant past medical/surgical, social, or familyhistory.On examination,therewasleft-sided deviation of the tongue with left-sided face weakness. Bilateral upper limb has power was 2/5 and lowerlimb power was 3/5 without any bowel and bladder incontinence. On imaging, magnetic resonance imaging (MRI) revealed a large well-de fi ned lobulated extra-axial left petroclival dura-based mass. The mass was seen extending along the prepon-tine, left cerebellopontine, and cerebellomedullary cisterns and inferiorly into the spinal canal through the foramen magnum ( ► Fig. 1A – D ). The mass was isointense on T1-weighted(T1)imagingandonT2-weighted(T2)imaging.There was no signi fi cant diffusion restriction in diffusion-weighted imaging or blooming in gradient images. The tumor displayed intense, homogeneous post-contrast enhancement. The mass showed broad base along the tentorium cerebelli and clivus. Laterally the lesion was seen widening and eroding the hypo-glossal canal and extending intotheleft parapharyngeal","PeriodicalId":51597,"journal":{"name":"Indian Journal of Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139150419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}