Preoperative Contrast-enhanced Computed Tomography Research Articles

Diagnostic accuracy of contrast-enhanced computed tomography in assessing bone invasion in patients with oral squamous cell carcinoma

ObjectivesThis study aimed to evaluate the diagnostic accuracy of contrast-enhanced computed tomography (CT) in detecting bone invasion in oral squamous cell carcinoma (OSCC) patients and to explore clinicopathological factors associated with its reliability.Materials and methods417 patients underwent preoperative contrast-enhanced CT followed by radical surgery. The presence or absence of bone invasion served as the outcome variable, with histopathologic examination of the resection specimen considered the gold standard. Statistical analyses, comprising correlation analyses and the determination of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were conducted.ResultsCT exhibited 76.85% sensitivity, 82.20% specificity, 47.14% PPV, and 89.67% NPV. False-positive and false-negative rates were 11.27% and 5.99%, respectively. Artifacts affected assessment in 44 patients, but not in those with bone invasion. Tumor size, depth of invasion (DOI), tumor localization at the upper jaw, lymphatic invasion, and perineural invasion correlated with incorrect identification of bone invasion (Chi-square, p < 0.05).ConclusionsDespite utilizing thin-section CT, notable false-positive and false-negative results persisted. Patients with T3 tumors, DOI ≥ 10 mm, or upper jaw tumors are at higher risk for misidentification of bone invasion. Combining multiple methods may enhance diagnostic accuracy, and the integration of artificial intelligence or tracking electrolyte disturbances by tumor depth profiling shows promise for further assessment of bone invasion before histopathology.Clinical relevanceSurgeons should consider these insights when planning tumor resection. Supplementary imaging may be warranted in cases with high risk factors for misidentification. Further methodological advancements are crucial for enhancing diagnostic precision.

Development and validation of a nomogram to predict Ki67 expression in hepatocellular carcinoma based on preoperative contrast-enhanced CT features and clinical factors

Development and validation of a nomogram to predict Ki67 expression in hepatocellular carcinoma based on preoperative contrast-enhanced CT features and clinical factors

A rare clinical case of malignant ectopic retrosternal goiter of a large size in a patient with aberrant right subclavian artery

Background. Ectopic goiter accounts for only 0.2 % to 1 % of all mediastinal goiters. The aberrant right subclavian artery (arteria lusoria) is an extremely rare anomaly of the major vessel, with a prevalence not exceeding 0.3 % in the general population. This paper presents the first case of malignant ectopic retrosternal goiter of a large size in combination with the aberrant right subclavian artery. Clinical case description. A 61-year-old male presented to the thoracic surgery department of P. Herzen Moscow oncology Research institute with a large posterior mediastinal mass measuring 108×106×79 mm and adjacent to the right lobe of the thyroid gland. The tumor displaced the trachea and esophagus. Open biopsy through a thoracotomy incision revealed thyroid gland cells without signs of atypia. A detailed examination revealed mediastinal vascular anomaly – retrotracheal position of the right subclavian artery. a complete longitudinal sternotomy, complemented by a right-sided thoracotomy at the 3rd intercostal space, served as the surgical approach. The operation lasted 320 minutes, with a blood loss of 3500 ml. The postoperative period was uneventful. The morphological examination of the surgical specimen revealed nodular hyperplasia of the ectopic thyroid tissue with the growth of well-differentiated follicular carcinoma without metastatic involvement of removed lymph nodes. Considering the identified follicular carcinoma, total thyroidectomy was performed. Conclusion. In surgical planning, it is crucial to assess the location of major mediastinal vessels and their relationship with the goiter. Therefore, all patients with mediastinal lesions should undergo preoperative contrast-enhanced ct. Retrosternal goiter predisposes to a higher rate of malignancy compared to cervical goiter. If the lower edge of the goiter is at the level of or below the aortic arch, cervical access should be supplemented with sternotomy. In cases of a massive thoracic component, additional thoracotomy is justified.

Radiological Cutoff Values for Diagnosis of Lymph Node Metastasis in Colorectal Cancer With Multilevel Analysis.

A cutoff value for lymph node diameter in colorectal cancer lymph node metastases has not been established. This prospective study aimed to investigate the direct association between swollen lymph nodes identified on preoperative computed tomography (CT) and pathological findings and proposed a cutoff value. We enrolled patients scheduled to undergo curative surgery with lymph node dissection for colorectal adenocarcinoma who underwent preoperative contrast-enhanced CT and had swollen lymph nodes ≥7 mm in diameter. Two gastrointestinal surgeons intraoperatively identified the target lymph nodes to assess the association between lymph node diameter and pathological findings. The diagnostic performance for lymph node metastasis was determined using multi-level logistic modelling. A total of 109 patients were enrolled, and 225 swollen lymph nodes were pathologically evaluated. Using a cutoff value of ≥9 mm for the short diameter, the positive and negative predictive values, sensitivity, and specificity were 100.0% (99.6%-100.0%), 99.9% (99.1%-100.0%), 62.0% (45.6%-76.0%), and 84.9% (67.0%-94.0%), respectively. The cutoff value for improving the positive predictive value for the preoperative lymph node metastasis diagnosis in colorectal cancer patients should be at least 9 mm in diameter.

Impact of aortic root geometry and degree of aortic calcification on outcomes of patients undergoing TAVI procedure.

Adequate differentiation of calcifications in contrast-enhanced CT scans remains difficult to assess TAVI parameters. The size of the aortic leaflets has not been taken into account so far in present studies. The aim of our study was to establish a new method for optimized quantification of the aortic valve calcification degree in contrast-enhanced CT scans for better preoperative prediction of postoperative paravalvular leak after TAVI. We retrospectively analyzed preoperative contrast-enhanced CT scans of patients who underwent TAVI in our institution between 2014 and 2017. Calcium volume was quantified by a method using contrast enhanced computer tomography (3mensio-Structural Heart-7.2 software) with different iodine contents for better discrimination of contrast agent from calcium and by an individually set Houndsfield Unit (HU) threshold with 50HU above the individually determined reference value. Calcium volume was correlated with surface area of each aortic cusp. Perioperative variables were analyzed. All patients (n = 150) with severe aortic stenosis were treated with TAVI implantation. Overall incidence of postoperative trace to moderate PVL was 37%. The amount of calcium correlated with the incidence of PVL. In a logistic regression analysis total volume of calcification (p = .032) as well as calcification of each aortic cusp (NC_p = .001; RC_p < .001; LC_p = .001) were independent predictors. Calcification degree as well as its correlation with the surface area of each aortic cusp significantly influence incidence of PVL. Our new method improves preoperative quantification of the calcification degree by use of contrast agents with different iodine contents and thereby helps to improve patients' outcomes.

Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study.

The accurate discrimination of uterine leiomyosarcomas and leiomyomas in a pre-operative setting remains a current challenge. To date, the diagnosis is made by a pathologist on the excised tumor. The aim of this study was to develop a machine learning algorithm using radiomic data extracted from contrast-enhanced computed tomography (CECT) images that could accurately distinguish leiomyosarcomas from leiomyomas. Pre-operative CECT images from patients submitted to surgery with a histological diagnosis of leiomyoma or leiomyosarcoma were used for the region of interest identification and radiomic feature extraction. Feature extraction was conducted using the PyRadiomics library, and three feature selection methods combined with the general linear model (GLM), random forest (RF), and support vector machine (SVM) classifiers were built, trained, and tested for the binary classification task (malignant vs. benign). In parallel, radiologists assessed the diagnosis with or without clinical data. A total of 30 patients with leiomyosarcoma (mean age 59 years) and 35 patients with leiomyoma (mean age 48 years) were included in the study, comprising 30 and 51 lesions, respectively. Out of nine machine learning models, the three feature selection methods combined with the GLM and RF classifiers showed good performances, with predicted area under the curve (AUC), sensitivity, and specificity ranging from 0.78 to 0.97, from 0.78 to 1.00, and from 0.67 to 0.93, respectively, when compared to the results obtained from experienced radiologists when blinded to the clinical profile (AUC = 0.73 95%CI = 0.62-0.84), as well as when the clinical data were consulted (AUC = 0.75 95%CI = 0.65-0.85). CECT images integrated with radiomics have great potential in differentiating uterine leiomyomas from leiomyosarcomas. Such a tool can be used to mitigate the risks of eventual surgical spread in the case of leiomyosarcoma and allow for safer fertility-sparing treatment in patients with benign uterine lesions.

Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study

BackgroundCombining conventional radiomics models with deep learning features can result in superior performance in predicting the prognosis of patients with tumors; however, this approach has never been evaluated for the prediction of metachronous distant metastasis (MDM) among patients with retroperitoneal leiomyosarcoma (RLS). Thus, the purpose of this study was to develop and validate a preoperative contrast-enhanced computed tomography (CECT)-based deep learning radiomics model for predicting the occurrence of MDM in patients with RLS undergoing complete surgical resection.MethodsA total of 179 patients who had undergone surgery for the treatment of histologically confirmed RLS were retrospectively recruited from two tertiary sarcoma centers. Semantic segmentation features derived from a convolutional neural network deep learning model as well as conventional hand-crafted radiomics features were extracted from preoperative three-phase CECT images to quantify the sarcoma phenotypes. A conventional radiomics signature (RS) and a deep learning radiomics signature (DLRS) that incorporated hand-crafted radiomics and deep learning features were developed to predict the risk of MDM. Additionally, a deep learning radiomics nomogram (DLRN) was established to evaluate the incremental prognostic significance of the DLRS in combination with clinico-radiological predictors.ResultsThe comparison of the area under the curve (AUC) values in the external validation set, as determined by the DeLong test, demonstrated that the integrated DLRN, DLRS, and RS models all exhibited superior predictive performance compared with that of the clinical model (AUC 0.786 [95% confidence interval 0.649–0.923] vs. 0.822 [0.692–0.952] vs. 0.733 [0.573–0.892] vs. 0.511 [0.359–0.662]; both P < 0.05). The decision curve analyses graphically indicated that utilizing the DLRN for risk stratification provided greater net benefits than those achieved using the DLRS, RS and clinical models. Good alignment with the calibration curve indicated that the DLRN also exhibited good performance.ConclusionsThe novel CECT-based DLRN developed in this study demonstrated promising performance in the preoperative prediction of the risk of MDM following curative resection in patients with RLS. The DLRN, which outperformed the other three models, could provide valuable information for predicting surgical efficacy and tailoring individualized treatment plans in this patient population.Trial registration: Not applicable.

The value of CT radiomics combined with deep transfer learning in predicting the nature of gallbladder polypoid lesions.

Computed tomography (CT) radiomics combined with deep transfer learning was used to identify cholesterol and adenomatous gallbladder polyps that have not been well evaluated before surgery. To investigate the potential of various machine learning models, incorporating radiomics and deep transfer learning, in predicting the nature of cholesterol and adenomatous gallbladder polyps. A retrospective analysis was conducted on clinical and imaging data from 100 patients with cholesterol or adenomatous polyps confirmed by surgery and pathology at our hospital between September 2015 and February 2023. Preoperative contrast-enhanced CT radiomics combined with deep learning features were utilized, and t-tests and least absolute shrinkage and selection operator (LASSO) cross-validation were employed for feature selection. Subsequently, 11 machine learning algorithms were utilized to construct prediction models, and the area under the ROC curve (AUC), accuracy, and F1 measure were used to assess model performance, which was validated in a validation group. The Logistic algorithm demonstrated the most effective prediction in identifying polyp properties based on 10 radiomics combined with deep learning features, achieving the highest AUC (0.85 in the validation group, 95% confidence interval = 0.68-1.0). In addition, the accuracy (0.83 in the validation group) and F1 measure (0.76 in the validation group) also indicated strong performance. The machine learning radiomics combined with deep learning model based on enhanced CT proves valuable in predicting the characteristics of cholesterol and adenomatous gallbladder polyps. This approach provides a more reliable basis for preoperative diagnosis and treatment of these conditions.

Utility of Combined Use of Imaging Features From Abdominopelvic CT and CA 125 to Identify Presence of CT Occult Peritoneal Metastases in Advanced Gastric Cancer.

The purpose of this study is to identify the presence of occult peritoneal metastasis (OPM) in patients with advanced gastric cancer (AGC) by using clinical characteristics and abdominopelvic computed tomography (CT) features. This retrospective study included 66 patients with OPM and 111 patients without peritoneal metastasis (non-PM [NPM]) who underwent preoperative contrast-enhanced CT between January 2020 and December 2021. Occult PMs means PMs that are missed by CT but later diagnosed by laparoscopy or laparotomy. Patients with NPM means patients have neither PM nor other distant metastases, indicating there is no evidence of distant metastases in patients with AGC. Patients' clinical characteristics and CT features such as tumor marker, Borrmann IV, enhancement patterns, and pelvic ascites were observed by 2 experienced radiologists. Computed tomography features and clinical characteristics were combined to construct an indicator for identifying the presence of OPM in patients with AGC based on a logistic regression model. Receiver operating characteristic curves and the area under the receiver operating characteristic curve (AUC) were generated to assess the diagnostic performance of the combined indicator. Four independent predictors (Borrmann IV, pelvic ascites, carbohydrate antigen 125, and normalized arterial CT value) differed significantly between OPM and NPM and performed outstandingly in distinguishing patients with OPM from those without PM (AUC = 0.643-0.696). The combined indicator showed a higher AUC value than the independent risk factors (0.820 vs 0.643-0.696). The combined indicator based on abdominopelvic CT features and carbohydrate antigen 125 may assist clinicians in identifying the presence of CT OPMs in patients with AGC.

Discrimination between benign and malignant gallbladder lesions on enhanced CT imaging using radiomics.

Gallbladder cancer is a rare but aggressive malignancy that is often diagnosed at an advanced stage and is associated with poor outcomes. To develop a radiomics model to discriminate between benign and malignant gallbladder lesions using enhanced computed tomography (CT) imaging. All patients had a preoperative contrast-enhanced CT scan, which was independently analyzed by two radiologists. Regions of interest were manually delineated on portal venous phase images, and radiomics features were extracted. Feature selection was performed using mRMR and LASSO methods. The patients were randomly divided into training and test groups at a ratio of 7:3. Clinical and radiomics parameters were identified in the training group, three models were constructed, and the models' prediction accuracy and ability were evaluated using AUC and calibration curves. In the training group, the AUCs of the clinical model and radiomics model were 0.914 and 0.968, and that of the nomogram model was 0.980, respectively. There were statistically significant differences in diagnostic accuracy between nomograms and radiomics features (P <0.05). There was no significant difference in diagnostic accuracy between the nomograms and clinical features (P >0.05) or between the clinical features and radiomics features (P >0.05). In the testing group, the AUC of the clinical model and radiomics model were 0.904 and 0.941, and that of the nomogram model was 0.948, respectively. There was no significant difference in diagnostic accuracy between the three groups (P >0.05). It was suggested that radiomics analysis using enhanced CT imaging can effectively discriminate between benign and malignant gallbladder lesions.

A SICE (Società Italiana di Chirurgia Endoscopica e Nuove Tecnologie) observational prospective multicenter study on anatomical variants of the superior mesenteric artery: intraoperative analysis during laparoscopic right hemicolectomy-CoDIG 2 database (ColonDx Italian Group).

Colorectal cancer, the third most common cancer worldwide, affects 40-45% of patients on the right side. Surgery, especially minimally invasive methods such as laparoscopic and robotic procedures, is the preferred treatment. However, these techniques present technical complications. The anatomical complexity and variations in vessel branching patterns pose challenges, particularly for less experienced surgeons. The CoDIG 2 is a nationwide observational study involving 76 specialized Italian general surgery departments focused on colorectal surgery. The centres were directed to maintain their standard surgical and clinical practices. The aim of this study was to analyse the intraoperative vascular anatomy of Italian patients who underwent laparoscopic right colectomy and explore the ligature techniques used by Italian surgeons. Surgeons reported information about vascularization of the right colon for 616 patients and about surgical anatomy of RCA for 368 patients. Fifty-three patients (10.8%) showed no RCA intraoperatively. The right colic artery (RCA) was categorized according to the Yada classification (types 1-4) during evaluation, and intraoperative assessments revealed that Yada type 1 was the most common type (55.2%), while radiologic evaluations revealed a higher prevalence of type 2. Furthermore, compared with the superior mesenteric vein (SMV), the RCA is more often located anteriorly according to intraoperative and contrast-enhancedCT examination; 59.9% were found in the anterior position during intraoperative examination, while 40.1% were found in the same position on preoperative contrast-enhanced CT. Vascularization of the right colon, including missing branches, additional branches, shared trunks, and retro-superior courses of the mesenteric vein, exhibited notable variations. To understand vascular variations, a preoperative radiological study is necessary; although there was no concordance between the intraoperative and radiological evaluations, this is a limitation of preinterventional radiological evaluation (PII) because it is always needed for oncological staging. This approach is especially critical for inexperienced surgeons to avoid potential complications, such as problematic bleeding.

Radiographic Predictors of Extranodal Extension on Preoperative Contrast-enhanced CT in Patients with Oropharyngeal Squamous Cell Carcinoma

Radiographic Predictors of Extranodal Extension on Preoperative Contrast-enhanced CT in Patients with Oropharyngeal Squamous Cell Carcinoma

Contrast Enhanced CT Versus MRI for Accurate Diagnosis of Wall-thickening Type Gallbladder Cancer

IntroductionDiagnosis of wall-thickening type gallbladder cancer (GBC) is challenging. Computed tomography (CT) and magnetic resonance imaging (MRI) are commonly utilized to evaluate gallbladder wall thickening. However, there is a lack of data comparing the performance of CT and MRI for the detection of wall-thickening type GBC. AimWe aim to compare the diagnostic accuracy of CT and MRI in diagnosis of wall-thickening type GBC. Materials and MethodsThis prospective study comprised consecutive patients suspected of wall-thickening type GBC who underwent preoperative contrast-enhanced CT and MRI. The final diagnosis was based on the histopathology of the resected gallbladder lesion. Two radiologists independently reviewed the characteristics of gallbladder wall thickening at CT and MRI. The association of CT and MRI findings with histological diagnosis and the interobserver agreement of CT and MRI findings were assessed. ResultsThirty-three patients (malignancy, 13 and benign, 20) were included. None of the CT findings were significantly associated with GBC. However, at MRI, heterogeneous enhancement, indistinct interface with the liver, and diffusion restriction were significantly associated with malignancy (p=0.006, <0.001, and 0.005, respectively), and intramural cysts were significantly associated with benign lesions (p=0.012). For all MRI findings, the interobserver agreement was substantial to perfect (kappa=0.697-1.000). At CT, the interobserver agreement was moderate to near perfect (k=0.631-1.000). ConclusionThese findings suggest that MRI may be preferred over CT in patients with suspected wall thickening type GBC. However, larger multicenter studies must confirm our findings.

The radiomics-clinical nomogram for predicting the response to initial superselective arterial embolization in renal angiomyolipoma, a preliminary study.

The aim of this study was to explore a radiomics-clinical model for predicting the response to initial superselective arterial embolization (SAE) in renal angiomyolipoma (RAML). A total of 78 patients with RAML were retrospectively enrolled. Clinical data were recorded and evaluated. Radiomic features were extracted from preoperative contrast-enhanced CT (CECT). Least absolute shrinkage and selection operator (LASSO) and intra- and inter-class correlation coefficients (ICCs) were used in feature selection. Logistic regression analysis was performed to develop the radiomics, clinical, and combined models where the fivefold cross-validation method was used. The predictive performance and calibration were evaluated by the receiver operating characteristic (ROC) curve and calibration curve. Decision curve analysis (DCA) was used to measure clinical usefulness. The tumor shrinkage rate was 29.7% in total, and both fat and angiomyogenic components were significantly reduced. In the radiomics model, 12 significant features were selected. In the clinical model, maximum diameter (p = 0.001), angiomyogenic tissue ratio (p = 0.032), aneurysms (p = 0.048), and post-SAE time (p = 0.002) were significantly associated with greater volume reduction after SAE. Because of the severe linear dependence between radiomics signature and some clinical parameters, the combined model eventually included Rad-score, aneurysm, and post-SAE time. The radiomics-clinical model showed better discrimination (mean AUC = 0.83) than the radiomics model (mean AUC = 0.60) and the clinical model (mean AUC = 0.82). Calibration curve and DCA showed the goodness of fit and clinical usefulness of the radiomics-clinical model. The radiomics-clinical model incorporating radiomics features and clinical parameters can potentially predict the positive response to initial SAE in RAML and provide support for clinical treatment decisions.

Prediction of transformation in the histopathological growth pattern of colorectal liver metastases after chemotherapy using CT-based radiomics.

Chemotherapy alters the prognostic biomarker histopathological growth pattern (HGP) phenotype in colorectal liver metastases (CRLMs) patients. We aimed to develop a CT-based radiomics model to predict the transformation of the HGP phenotype after chemotherapy. This study included 181 patients with 298 CRLMs who underwent preoperative contrast-enhanced CT followed by partial hepatectomy between January 2007 and July 2022 at two institutions. HGPs were categorized as pure desmoplastic HGP (pdHGP) or non-pdHGP. The samples were allocated to training, internal validation, and external validation cohorts comprising 153, 65, and 29 CRLMs, respectively. Radiomics analysis was performed on pre-enhanced, arterial phase, portal venous phase (PVP), and fused images. The model was used to predict prechemotherapy HGPs in 112 CRLMs, and HGP transformation was analysed by comparing these findings with postchemotherapy HGPs determined pathologically. The prevalence of pdHGP was 19.8% (23/116) and 45.8% (70/153) in chemonaïve and postchemotherapy patients, respectively (P < 0.001). The PVP radiomics signature showed good performance in distinguishing pdHGP from non-pdHGPs (AUCs of 0.906, 0.877, and 0.805 in the training, internal validation, and external validation cohorts, respectively). The prevalence of prechemotherapy pdHGP predicted by the radiomics model was 33.0% (37/112), and the prevalence of postchemotherapy pdHGP according to the pathological analysis was 47.3% (53/112; P = 0.029). The transformation of HGP was bidirectional, with 15.2% (17/112) of CRLMs transforming from prechemotherapy pdHGP to postchemotherapy non-pdHGP and 30.4% (34/112) transforming from prechemotherapy non-pdHGP to postchemotherapy pdHGP (P = 0.005). CT-based radiomics method can be used to effectively predict the HGP transformation in chemotherapy-treated CRLM patients, thereby providing a basis for treatment decisions.

A novel stratification scheme combined with internal arteries in CT imaging for guiding postoperative adjuvant transarterial chemoembolization in hepatocellular carcinoma: a retrospective cohort study.

Although postoperative adjuvant transarterial chemoembolization (PA-TACE) improves survival outcomes in a subset of patients with resected hepatocellular carcinoma (HCC), the lack of reliable biomarkers for patient selection remains a significant challenge. The present study aimed to evaluate whether computed tomography imaging can provide more value for predicting benefits from PA-TACE and to establish a new scheme for guiding PA-TACE benefits. In this retrospective study, patients with HCC who had undergone preoperative contrast-enhanced computed tomography and curative hepatectomy were evaluated. Inverse probability of treatment weight was performed to balance the difference of baseline characteristics. Cox models were used to test the interaction among PA-TACE, imaging features, and pathological indicators. An HCC imaging and pathological classification (HIPC) scheme incorporating these imaging and pathological indicators was established. This study included 1488 patients [median age, 52 years (IQR, 45-61 years); 1309 male]. Microvascular invasion (MVI) positive, and diameter >5cm tumors achieved a higher recurrence-free survival (RFS), and overall survival (OS) benefit, respectively, from PA-TACE than MVI negative, and diameter ≤5cm tumors. Patients with internal arteries (IA) positive benefited more than those with IA-negative in terms of RFS ( P =0.016) and OS ( P =0.018). PA-TACE achieved significant RFS and OS improvements in HIPC3 (IA present and diameter >5 cm, or two or three tumors) patients but not in HIPC1 (diameter ≤5cm, MVI negative) and HIPC2 (other single tumor) patients. Our scheme may decrease the number of patients receiving PA-TACE by ~36.5% compared to the previous suggestion. IA can provide more value for predicting the benefit of PA-TACE treatment. The proposed HIPC scheme can be used to stratify patients with and without survival benefits from PA-TACE.

Inflammatory myofibroblastic tumor directly invading the right first rib treated with oral steroids: a case report

BackgroundWe present a case of an inflammatory myofibroblastic tumor cured with a short period of steroid administration, a treatment previously unreported for such cases.Case presentationA 49-year-old man had a chief complaint of chest pain for more than 3 days. Computed tomography (CT) revealed a tumoral lesion suspected to have infiltrated into the right first rib and intercostal muscles, with changes in lung parenchymal density around the lesion. The maximal standardized uptake value on 18 F-fluorodeoxyglucose positron emission tomography/computed tomography was high (16.73), consistent with tumor presence. CT-guided biopsy revealed an inflammatory myofibroblastic tumor with no distant metastases. Surgery was indicated based on the disease course. However, he had received an oral steroid before the preoperative contrast-enhanced CT scan due to a history of bronchial asthma, and subsequent CT showed that the tumor shrank in size after administration; he has been recurrence-free for more than a year.ConclusionsSurgery is still the first choice for inflammatory myofibroblastic tumors, as the disease can metastasize and relapse; however, this condition can also be cured with a short period of steroid therapy.

Association of LI-RADS and Histopathologic Features with Survival in Patients with Solitary Resected Hepatocellular Carcinoma.

Background Both Liver Imaging Reporting and Data System (LI-RADS) and histopathologic features provide prognostic information in patients with hepatocellular carcinoma (HCC), but whether LI-RADS is independently associated with survival is uncertain. Purpose To assess the association of LI-RADS categories and features with survival outcomes in patients with solitary resected HCC. Materials and Methods This retrospective study included patients with solitary resected HCC from three institutions examined with preoperative contrast-enhanced CT and/or MRI between January 2008 and December 2019. Three independent readers evaluated the LI-RADS version 2018 categories and features. Histopathologic features including World Health Organization tumor grade, microvascular and macrovascular invasion, satellite nodules, and tumor capsule were recorded. Overall survival and disease-free survival were assessed with Cox regression models. Marginal effects of nontargetoid features on survival were estimated using propensity score matching. Results A total of 360 patients (median age, 64 years [IQR, 56-70 years]; 280 male patients) were included. At CT and MRI, the LI-RADS LR-M category was associated with increased risk of recurrence (CT: hazard ratio [HR] = 1.83 [95% CI: 1.26, 2.66], P = .001; MRI: HR = 2.22 [95% CI: 1.56, 3.16], P < .001) and death (CT: HR = 2.47 [95% CI: 1.72, 3.55], P < .001; MRI: HR = 1.80 [95% CI: 1.32, 2.46], P < .001) independently of histopathologic features. The presence of at least one nontargetoid feature was associated with an increased risk of recurrence (CT: HR = 1.80 [95% CI: 1.36, 2.38], P < .001; MRI: HR = 1.93 [95% CI: 1.81, 2.06], P < .001) and death (CT: HR = 1.51 [95% CI: 1.10, 2.07], P < .010) independently of histopathologic features. In matched samples, recurrence was associated with the presence of at least one nontargetoid feature at CT (HR = 2.06 [95% CI: 1.15, 3.66]; P = .02) or MRI (HR = 1.79 [95% CI: 1.01, 3.20]; P = .048). Conclusion In patients with solitary resected HCC, LR-M category and nontargetoid features were negatively associated with survival independently of histopathologic characteristics. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kartalis and Grigoriadis in this issue.

Noninvasive prediction of perineural invasion in intrahepatic cholangiocarcinoma by clinicoradiological features and computed tomography radiomics based on interpretable machine learning: a multicenter cohort study.

Perineural invasion (PNI) of intrahepatic cholangiocarcinoma (ICC) is a strong independent risk factor for tumour recurrence and long-term patient survival. However, there is a lack of noninvasive tools for accurately predicting the PNI status. The authors develop and validate a combined model incorporating radiomics signature and clinicoradiological features based on machine learning for predicting PNI in ICC, and used the Shapley Additive explanation (SHAP) to visualize the prediction process for clinical application. This retrospective and prospective study included 243 patients with pathologically diagnosed ICC (training, n =136; external validation, n =81; prospective, n =26, respectively) who underwent preoperative contrast-enhanced computed tomography between January 2012 and May 2023 at three institutions (three tertiary referral centres in Guangdong Province, China). The ElasticNet was applied to select radiomics features and construct signature derived from computed tomography images, and univariate and multivariate analyses by logistic regression were used to identify the significant clinical and radiological variables with PNI. A robust combined model incorporating radiomics signature and clinicoradiological features based on machine learning was developed and the SHAP was used to visualize the prediction process. A Kaplan-Meier survival analysis was performed to compare prognostic differences between PNI-positive and PNI-negative groups and was conducted to explore the prognostic information of the combined model. Among 243 patients (mean age, 61.2years ± 11.0 (SD); 152 men and 91 women), 108 (44.4%) were diagnosed as PNI-positive. The radiomics signature was constructed by seven radiomics features, with areas under the curves of 0.792, 0.748, and 0.729 in the training, external validation, and prospective cohorts, respectively. Three significant clinicoradiological features were selected and combined with radiomics signature to construct a combined model using machine learning. The eXtreme Gradient Boosting exhibited improved accuracy and robustness (areas under the curves of 0.884, 0.831, and 0.831, respectively). Survival analysis showed the construction combined model could be used to stratify relapse-free survival (hazard ratio, 1.933; 95% CI: 1.093-3.418; P =0.021). We developed and validated a robust combined model incorporating radiomics signature and clinicoradiological features based on machine learning to accurately identify the PNI statuses of ICC, and visualize the prediction process through SHAP for clinical application.

The prognostic significance of extramural venous invasion detected via contrast-enhanced computed tomography colonography in colon cancer.

167 Background: The precise effects of extramural venous invasion (EMVI) and Spiculation on colon cancer recurrence remain poorly understood. This study investigated the clinical significance of ctEMVI and ctSpiculation as diagnosed via preoperative contrast-enhanced CT colonography (CTC) using three-dimensional multiplanar reconstruction (3D MPR) images. Methods: Patients with stage I–Ⅲ colon cancer (T2 or deeper) diagnosed via CTC at the National Cancer Center Hospital in Japan between 2013 and 2018 were retrospectively evaluated for ctEMVI and ctSpiculation, diagnosed by two observers using 3D MPR images of slices 0.5 mm thick. The interobserver reliability was assessed with Cohen’s kappa statistic, and recurrence-free survival (RFS) was analyzed using Kaplan–Meier curves. Preoperative clinical factors were subjected to Cox regression analysis in relation to RFS. Results: Of 922 patients, 544 eligible cases were analyzed. There were 50 (9.2%) ctEMVI-positive cases and 494 (90.8%) ctEMVI-negative cases, along with 186 (34.2%) ctSpiculation-positive cases and 358 (65.8%) with negative findings. Kappa coefficients for ctEMVI and ctSpiculation were 0.830 and 0.718, respectively. Kaplan–Meier analysis and log-rank tests demonstrated lower RFS in ctEMVI- and ctSpiculation-positive cases (p&lt;0.001). In multivariable analysis, ctT4 (HR 2.95, 95% CI 1.73–5.02) and ctEMVI (2.97, 1.81–4.87) were independent factors associated with RFS. When stratifying TNM stages based on ctEMVI status, in N(+) (i.e., Stage III), ctEMVI-positive group had worse prognoses than negative group (5-year RFS rates; 52.0% vs. 78.5%, p=0.003). Similarly, in N(–) (i.e., Stage I and II), T3N0EMVI(+) cases had significantly worse prognoses than T3N0EMVI(–) and Stage I cases (50.6% vs. 89.3% and 90.1%) and were comparable to the T4N0 group (50.6% vs. 46.7%, p=0.844). Conclusions: Both ctEMVI and ctSpiculation are associated with RFS. ctEMVI is an independent predictor of recurrence and may be valuable in combination with preoperative TNM staging to identify patients at high risk for recurrence. [Table: see text]