Preoperative Imaging Features Research Articles

Determining Factors Which Limit Resectability in Advanced Hypopharyngeal Malignancy.

Preoperative radiological findings of hypopharyngeal cancers are used to determine suitability for surgical resection. We sought to examine preoperative imaging characteristics to determine how well imaging findings predicted surgical resectability. A retrospective case-control study of patients undergoing a pharyngolaryngectomy in a tertiary referral center over a 2-year period was completed. Demographic details, previous treatment, subsite, TNM staging, imaging characteristics, and operative characteristics were collected. A total of 78 patients met initial inclusion criteria, of which 71 patients ultimately underwent successful surgical resection (91.1%). Preoperative images identified suspicion of prevertebral fascia invasion in 24 (30.7%) cases and carotid artery involvement in 14 (17.9%) cases. In cases of suspicion of prevertebral fascia invasion (24), 19 cases (79.2%) were resectable, and in those with carotid artery involvement (14), 11 (78.6%) were resectable. Concern for prevertebral fascia invasion on radiology led to a higher likelihood of a close margin (42% vs. 17%) in those without concerning features (p = 0.088). The present study demonstrated a high rate of resectability of hypopharyngeal and upper esophageal cancers despite imaging findings suspicious for factors that could limit resectability. In patients with advanced hypopharyngeal, especially in the salvage setting, surgery should be considered.

Construction and validation of a diagnostic model for high-risk papillary thyroid microcarcinoma.

The purpose of this study was to construct a diagnostic model by exploring the potential predictors of high-risk Papillary Thyroid Microcarcinoma (PTMC) and verifying its reliability. A retrospective analysis of PTMC patients who underwent surgical treatment from 2004 to 2015 in the SEER database (training set) and the clinical pathological ultrasound information of PTMC patients at the Sichuan Provincial People's Hospital from 2020 to 2022 (external validation set) was conducted. In the training set, univariate and multivariate logistic regression analyses were used to screen independent predictive factors for high-risk PTMC patients in pathology. A nomogram diagnostic model was further constructed. Additionally, ROC curves and calibration curves were drawn to evaluate the efficiency of the model. In the external validation set, the diagnostic model was indirectly evaluated based on preoperative ultrasound imaging features to explore the feasibility and reliability of diagnosing high-risk PTMC through preoperative ultrasound imaging features. A total of 1628 patients were included in the training set, and 530 patients were included in the test set. The independent risk factors for pathological high-risk PTMC were sex, age, tumor maximum diameter, tumor invasive, and cervical lymph nodes (P<0.05). The C-index of the nomogram constructed based on these five factors was 0.947, with an optimal sensitivity of 96.7% and a specificity of 86.0%. The calibration curve showed that the model had high consistency. The area under the curve (AUC) value of the ROC curve for high-risk PTMC predicted by the risk score based on ultrasound features was 0.824 [95% CI (0.789, 0.860)], which was highly consistent with the risk score based on pathological features (κ= 0.758, P<0.05). Indirect evaluation of a high-risk PTMC diagnostic model based on preoperative ultrasound imaging features had high predictive efficiency and potential value for clinical application.

A Nomogram Based on Features of Ultrasonography and Contrast-Enhanced CT to Predict Vessels Encapsulating Tumor Clusters Pattern of Hepatocellular Carcinoma

ObjectiveThis study aimed to establish a clinical prediction model for vessels encapsulating tumor clusters (VETC) based on preoperative ultrasonography (US) and contrast-enhanced computed tomography (CECT) imaging in patients with hepatocellular carcinoma (HCC). MethodsData were retrospectively collected from 215 patients who underwent hepatectomy for solitary HCC lesions. They were divided into training and validation cohorts at a ratio of 6:4. Preoperative imaging features were extracted (seven from US and nine from CECT imaging) to explore their relationship with VETC. A VETC prediction model was constructed and graphically depicted as a nomogram. Its performance was evaluated via the receiver operating characteristic (ROC) curve, the calibration curve, and decision curve analysis (DCA). ResultsThe VETC incidence for all the lesions was 37.7%. The final variables included in the nomogram were “peritumoral enhancement in CECT”, “alpha-fetoprotein level > 200 ng/Ml,” “halo in US,” “capsule enhancement in CECT,” and “posterior acoustic enhancement in US.” The area under the curve (AUC) values for the training and validation cohorts were 0.824 and 0.725, respectively. The Hosmer–Lemeshow fit test showed no statistical difference (p = 0.369 and p = 0.067 for the training and validation cohorts, respectively). DCA demonstrated that our nomogram provided clinical benefits to a wide range of patients. According to the nomogram score, the VETC-positive and -negative groups demonstrated significant differences in both the training (p < 0.001) and validation (p = 0.001) cohorts. ConclusionOur prediction model based on US and CECT imaging features can accurately predict VETC in HCC.

Machine learning-assisted diagnosis of parotid tumor by using contrast-enhanced CT imaging features

PurposeThis study aims to develop a machine learning diagnostic model for parotid gland tumors based on preoperative contrast-enhanced CT imaging features to assist in clinical decision-making. Materials and methodsClinical data and contrast-enhanced CT images of 144 patients with parotid gland tumors from the Peking University School of Stomatology Hospital, collected from January 2019 to December 2022, were gathered. The 3D slicer software was utilized to accurately annotate the tumor regions, followed by exploring the correlation between multiple preoperative contrast-enhanced CT imaging features and the benign or malignant nature of the tumor, as well as the type of benign tumor. A prediction model was constructed using the k-nearest neighbors (KNN) algorithm. ResultsThrough feature selection, four key features—morphology, adjacent structure invasion, boundary, and suspicious cervical lymph node metastasis—were identified as crucial in preoperative discrimination between benign and malignant tumors. The KNN prediction model achieved an accuracy rate of 94.44 %. Additionally, six features including arterial phase CT value, age, delayed phase CT value, pre-contrast CT value, venous phase CT value, and gender, were also significant in the classification of benign tumors, with a KNN prediction model accuracy of 95.24 %. ConclusionThe machine learning model based on preoperative contrast-enhanced CT imaging features can effectively discriminate between benign and malignant parotid gland tumors and classify benign tumors, providing valuable reference information for clinicians.

Behavioral and brain morphological changes before and after hemispherotomy.

Hemispherotomy is an effective surgery for treating refractory epilepsy from diffuse unihemispheric lesions. To date, postsurgery neuroplastic changes supporting behavioral recovery after left or right hemispherotomy remain unclear. In the present study, we systematically investigated changes in gray matter volume (GMV) before and after surgery and further analyzed their relationships with behavioral scores in two large groups of pediatric patients with left and right hemispherotomy (29 left and 28 right). To control for the dramatic developmental effect during this stage, age-adjusted GMV within unaffected brain regions was derived voxel by voxel using a normative modeling approach with an age-matched reference cohort of 2115 healthy children. Widespread GMV increases in the contralateral cerebrum and ipsilateral cerebellum and GMV decreases in the contralateral cerebellum were consistently observed in both patient groups, but only the left hemispherotomy patients showed GMV decreases in the contralateral cingulate gyrus. Intriguingly, the GMV decrease in the contralateral cerebellum was significantly correlated with improvement in behavioral scores in the right but not the left hemispherotomy patients. Importantly, the preoperative voxelwise GMV features can be used to significantly predict postoperative behavioral scores in both patient groups. These findings indicate an important role of the contralateral cerebellum in the behavioral recovery following right hemispherotomy and highlight the predictive potential of preoperative imaging features in postoperative behavioral performance.

Adjunctive aspiration technique for the surgical management of deep orbital cavernous hemangioma.

To propose a needle aspiration technique for the surgical removal of orbital cavernous hemangioma. In this retrospective case series, we enrolled 13 patients with orbital cavernous hemangioma, who underwent excisional surgery assisted with needle aspiration technique from June 2013 to April 2022. Preoperative symptoms, clinical examination, and imaging features were recorded. Surgical outcomes, including the improvement of visual acuity, proptosis, and ocular motility, were assessed. Postoperative complications were also reported. There were 11 female and two male patients, with a mean age of 50.2 ± 8.0 years (range: 38-61 years). The most common symptom was proptosis (12 cases, 92%), followed by blurred vision (6 cases, 46%). The diameter of the lesions was between 1.8 and 3.2 cm on preoperative imaging. The surgical approaches included sub-brow orbitotomy in 11 patients and the inferior transconjunctival approach in two cases. All the tumors were removed successfully after needle aspiration of 1-3 cc of intralesional blood to reduce the tumor size. Preoperative proptosis, blurred vision, and diplopia improved after the surgery in all cases. There were no serious complications or recurrence of orbital hemangioma. The study presented an effective application of the needle aspiration technique in the surgical management of orbital cavernous hemangioma. Such an innovative method can bring significant benefits, especially for those with large cavernous hemangioma within the deep orbital region.

Preoperative CT features for characterization of vessels that encapsulate tumor clusters in hepatocellular carcinoma

PurposeTo explore the capability of preoperative CT imaging features, in combination with clinical indicators, for predicting vessels that encapsulate tumor clusters (VETC) pattern and prognosis in hepatocellular carcinoma (HCC). Materials and methodsFrom January 2015 to May 2022, patients with HCC who underwent curative resection and preoperative enhanced CT were retrospectively included. Clinical indicators and imaging featuresassociated with the VETC pattern were determined by logistic regression analyses. The early recurrence (ER) rate was determined using the Kaplan-Meier survival curve. Factors associated with ER after surgical resection were identified by Cox regression analyses. ResultA total of 243 patients with HCCwere evaluated. The total bilirubin > 17.1 μmol/L (odds ratio [OR] 3.43, 95 % Confidence Interval [CI] 1.70, 6.91, p = 0.001), serum α-fetoprotein > 100 ng/mL (OR 2.41, 95 % CI 1.25, 4.67, p = 0.009), intratumor artery (IA) (OR 2.00, 95 % CI 1.04, 3.86,p = 0.039) and arterial peritumoral enhancement (OR 2.60, 95 % CI 1.13, 5.96, p = 0.025) were independent risk factors for VETC+–HCC. The VETC+status andCT feature ofIA were associated with an increased risk of recurrence, with a shorter median RFS, compared to those without these factors (p < 0.001 and p = 0.019, respectively). In multivariable Cox regression analysis, the VETC+(hazard ratio [HR] 2.60, 95 % CI 1.66, 4.09, p < 0.001), morphological patterns of confluent multinodular growth (HR 1.79, 95 % CI 1.10, 2.91,p = 0.019), the number of the tumors (≥2) (HR 2.69, 95 % CI 1.56, 4.65, p < 0.001), and the IA (HR 1.73, 95 % CI 1.12, 2.66, p = 0.013) were independent predictors of ER in patients with HCC after surgical resection. ConclusionPreoperative CT features combined with clinical indicators could predict VETC pattern, and the CT features, along with VETC status, were of prognostic significance for early postoperative recurrence in patients with HCC. Clinical Relevance StatementPreoperative CT features combined with clinical indicators could predict VETC pattern, and the CT features, along with VETC status, were of prognostic significance for early recurrence in patients with HCC after surgical resection.

Impact of pericardial calcification on early postoperative outcomes after pericardiectomy: a retrospective observational study

BackgroundOwing to the lack of understanding of the clinical significance of pericardial calcification during pericardiectomy, whether pericardial calcification should be considered when determining the optimal timing for pericardiectomy is debatable. We aimed to investigate the effect of pericardial calcification on early postoperative outcomes in patients who underwent pericardiectomy for constrictive pericarditis.MethodsAltogether, 44 patients who underwent pericardiectomy for constrictive pericarditis were enrolled. After excluding three patients who underwent concurrent surgeries, a total of 41 patients were categorized into two groups based on the presence of pericardial calcification as determined by preoperative computed tomography and pathological examination. Preoperative clinical and imaging characteristics, intraoperative data, and early postoperative outcomes were compared between the two groups. A multivariable analysis was performed to identify the factors associated with postoperative complications.ResultsThe group with and without PC comprised 21 and 20 patients, respectively. No significant differences were observed in 30-day mortality (n = 1 [5%]) in the group with pericardial calcification and no mortality in the group without pericardial calcification (p > 0.999). Other early postoperative outcome variables did not demonstrate any significant differences between the two groups. However, the use of cardiopulmonary bypass was associated with postoperative complications (p < 0.009, odds ratio: 63.5, 95% confidence interval: 5.13–3400).ConclusionsPericardial calcification did not significantly affect the postoperative outcomes after pericardiectomy. Further comprehensive studies, including those with larger sample sizes and longitudinal designs, are necessary to determine whether pericardial calcification can significantly influence the timing of surgical intervention.

Multimodal Deep Learning-based Radiomics Approach for Predicting Surgical Outcomes in Patients with Cervical Ossification of the Posterior Longitudinal Ligament.

A retrospective analysis. This research sought to develop a predictive model for surgical outcomes in patients with cervical ossification of the posterior longitudinal ligament (OPLL) using deep learning and machine learning (ML) techniques. Determining surgical outcomes assists surgeons in communicating prognosis to patients and setting their expectations. Deep learning and ML are computational models that identify patterns from large datasets and make predictions. Of the 482 patients, 288 patients were included in the analysis. A minimal clinically important difference (MCID) was defined as gain in Japanese Orthopaedic Association (JOA) score of 2.5 points or more. The predictive model for MCID achievement at 1 year post-surgery was constructed using patient background, clinical symptoms, and preoperative imaging features (x-ray, CT, MRI) analyzed via LightGBM and deep learning with RadImagenet. The median preoperative JOA score was 11.0 (IQR: 9.0-12.0), which significantly improved to 14.0 (IQR: 12.0-15.0) at 1 year after surgery (P < 0.001, Wilcoxon signed-rank test). The average improvement rate of the JOA score was 44.7%, and 60.1% of patients achieved the MCID. Our model exhibited an area under the receiver operating characteristic curve of 0.81 and the accuracy of 71.9% in predicting MCID at 1 year. Preoperative JOA score and certain preoperative imaging features were identified as the most significant factors in the predictive models. A predictive ML and deep learning model for surgical outcomes in OPLL patients is feasible, suggesting promising applications in spinal surgery. 4.

Risk factors for poor neurological recovery after anterior cervical discectomy and fusion: imaging characteristics

BackgroundPoor neurological recovery in patients after anterior cervical discectomy and fusion has been frequently reported; however, no study has analyzed the preoperative imaging characteristics of patients to investigate the factors affecting surgical prognosis. The purpose of this study was to investigate the factors that affect the preoperative imaging characteristics of patients and their influence on poor neurologic recovery after anterior cervical discectomy and fusion.MethodsWe retrospectively analyzed the clinical data of 89 patients who met the criteria for anterior cervical discectomy and fusion for the treatment of single-level cervical spondylotic myelopathy and evaluated the patients’ neurological recovery based on the recovery rate of the Japanese Orthopaedic Association (JOA) scores at the time of the final follow-up visit. Patients were categorized into the “good” and “poor” groups based on the JOA recovery rates of ≥ 50% and < 50%, respectively. Clinical information (age, gender, body mass index, duration of symptoms, preoperative JOA score, and JOA score at the final follow-up) and imaging characteristics (cervical kyphosis, cervical instability, ossification of the posterior longitudinal ligament (OPLL), calcification of herniated intervertebral discs, increased signal intensity (ISI) of the spinal cord on T2-weighted imaging (T2WI), and degree of degeneration of the discs adjacent to the fused levels (cranial and caudal) were collected from the patients. Univariate and binary logistic regression analyses were performed to identify risk factors for poor neurologic recovery.ResultsThe mean age of the patients was 52.56 ± 11.18 years, and the mean follow-up was 26.89 ± 11.14 months. Twenty patients (22.5%) had poor neurological recovery. Univariate analysis showed that significant predictors of poor neurological recovery were age (p = 0.019), concomitant OPLL (p = 0.019), concomitant calcification of herniated intervertebral discs (p = 0.019), ISI of the spinal cord on T2WI (p <0.05), a high grade of degeneration of the discs of the cranial neighboring levels (p <0.05), and a high grade of discs of the caudal neighboring levels (p <0.05). Binary logistic regression analysis showed that ISI of the spinal cord on T2WI (p = 0.001 OR = 24.947) and high degree of degeneration of adjacent discs on the cranial side (p = 0.040 OR = 6.260) were independent risk factors for poor neurological prognosis.ConclusionISI of the spinal cord on T2WI and high degree of cranial adjacent disc degeneration are independent risk factors for poor neurological recovery after anterior cervical discectomy and fusion. A comprehensive analysis of the patients’ preoperative imaging characteristics can help in the development of surgical protocols and the management of patients’ surgical expectations.

Establishment of nomogram prediction model of contrast-enhanced ultrasound and Gd-EOB-DTPA-enhanced MRI for vessels encapsulating tumor clusters pattern of hepatocellular carcinoma.

To establish clinical prediction models of vessels encapsulating tumor clusters (VETC) pattern using preoperative contrast-enhanced ultrasound (CEUS) and gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid magnetic resonance imaging (EOB-MRI) in patients with hepatocellular carcinoma (HCC). A total of 111 resected HCC lesions from 101 patients were included. Preoperative imaging features of CEUS and EOB-MRI, postoperative recurrence, and survival information were collected from medical records. The best subset regression and multivariable Cox regression were used to select variables to establish the prediction model. The VETC-positive group had a statistically lower survival rate than the VETC-negative group. The selected variables were peritumoral enhancement in the arterial phase (AP), hepatobiliary phase (HBP) on EOB-MRI, intratumoral branching enhancement in the AP of CEUS, intratumoral hypoenhancement in the portal phase of CEUS, incomplete capsule, and tumor size. A nomogram was developed. High and low nomogram scores with a cutoff value of 168 points showed different recurrence-free survival rates and overall survival rates. The area under the curve (AUC) and accuracy were 0.804 and 0.820, respectively, indicating good discrimination. Decision curve analysis showed a good clinical net benefit (threshold probability > 5%), while the Hosmer-Lemeshow test yielded excellent calibration (P = 0.6759). The AUC of the nomogram model combining EOB-MRI and CEUS was higher than that of the models with EOB-MRI factors only (0.767) and CEUS factors only (0.7). The nomogram verified by bootstrapping showed AUC and calibration curves similar to those of the nomogram model. The Prediction model based on CEUS and EOB-MRI is effective for preoperative noninvasive diagnosis of VETC.

A nomogram combining CT-based radiomic features with clinical features for the differentiation of benign and malignant cystic pulmonary nodules

BackgroundCurrently, the differentiation between benign and malignant cystic pulmonary nodules poses a significant challenge for clinicians. The objective of this retrospective study was to construct a predictive model for determining the likelihood of malignancy in patients with cystic pulmonary nodules.MethodsThe current study involved 129 patients diagnosed with cystic pulmonary nodules between January 2017 and June 2023 at the Neijiang First People’s Hospital. The study gathered the clinical data, preoperative imaging features of chest CT, and postoperative histopathological results for both cohorts. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors, from which a prediction model and nomogram were developed. In addition, The model's performance was assessed through receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA).ResultsA cohort of 129 patients presenting with cystic pulmonary nodules, consisting of 92 malignant and 37 benign lesions, was examined. Logistic data analysis identified a cystic airspace with a mural nodule, spiculation, mural morphology, and the number of cystic cavities as significant independent predictors for discriminating between benign and malignant cystic lung nodules. The nomogram prediction model demonstrated a high level of predictive accuracy, as evidenced by an area under the ROC curve (AUC) of 0.874 (95% CI: 0.804–0.944). Furthermore, the calibration curve of the model displayed satisfactory calibration. DCA proved that the prediction model was useful for clinical application.ConclusionIn summary, the risk prediction model for benign and malignant cystic pulmonary nodules has the potential to assist clinicians in the diagnosis of such nodules and enhance clinical decision-making processes.

A predictive model using MRI and clinicopathologic features for breast cancer recurrence in young women treated with upfront surgery.

To identify preoperative breast MR imaging and clinicopathological variables related to recurrence and develop a risk prediction model for recurrence in young women with breast cancer treated with upfront surgery. This retrospective study analyzed 438 consecutive women with breast cancer aged 35 years or younger between January 2007 and December 2016. Breast MR images before surgery were independently reviewed by breast radiologists blinded to patient outcomes. The clinicopathological data including patient demographics, clinical features, and tumor characteristics were reviewed. Univariate and multivariate logistic regression analyses were used to identify the independent factors associated with recurrence. The risk prediction model for recurrence was developed, and the discrimination and calibration abilities were assessed. Of 438 patients, 95 (21.7%) developed recurrence after a median follow-up of 65 months. Tumor size at MR imaging (HR = 1.158, p = 0.006), multifocal or multicentric disease (HR = 1.676, p = 0.017), and peritumoral edema on T2WI (HR = 2.166, p = 0.001) were identified as independent predictors of recurrence, while adjuvant endocrine therapy (HR = 0.624, p = 0.035) was inversely associated with recurrence. The prediction model showed good discrimination ability in predicting 5-year recurrence (C index, 0.707 in the development cohort; 0.686 in the validation cohort) and overall recurrence (C index, 0.699 in the development cohort; 0.678 in the validation cohort). The calibration plot demonstrated an excellent correlation (concordance correlation coefficient, 0.903). A prediction model based on breast MR imaging and clinicopathological features showed good discrimination to predict recurrence in young women with breast cancer treated with upfront surgery, which could contribute to individualized risk stratification. Our prediction model, incorporating preoperative breast MR imaging and clinicopathological features, predicts recurrence in young women with breast cancer undergoing upfront surgery, facilitating personalized risk stratification and informing tailored management strategies. Younger women with breast cancer have worse outcomes than those diagnosed at more typical ages. The described prediction model showed good discrimination performance in predicting 5-year and overall recurrence. Incorporating better risk stratification tools in this population may help improve outcomes.

The diagnostic potential role of thioredoxin reductase and TXNRD1 in early lung adenocarcinoma: A cohort study

BackgroundLung adenocarcinoma (LUAD) is the primary form of lung cancer, yet the reliable biomarkers for early diagnosis remain insufficient. Thioredoxin reductase (TrxR) is strongly linked to the occurrence, development, and drug resistance of lung cancer, making it a potential biomarker. However, further research is required to assess its diagnostic value in LUAD. MethodsA retrospective analysis was performed on patients who underwent pulmonary nodule resection at our center from 2018 to 2022. Clinical data, including preoperative TrxR levels, imaging, and laboratory characteristics, were identified as study variables. Two prediction models were constructed using multiple logistic regression, and their prediction performance was evaluated comprehensively. Besides, bioinformatics analyses of TrxR coding genes including differential expression, functional enrichment, immune infiltration, drug sensitivity, and single-cell landscape were performed based on TCGA database, which were subsequently validated by Human Protein Atlas. ResultsA total of 506 eligible patients (72 benign lesions, 77 AISs, 185 MIAs and 172 IACs) were identified in the clinical cohort. Two TrxR-based models were developed, which were able to distinguish between benign and malignant pulmonary nodules, as well as pathological subtypes of LUAD, respectively. The models exhibited good predictive ability with all AUC values ranging from 0.7 to 0.9. Based on calibration curves and clinical decision analysis, the nomogram models showed high reliability. Functional analysis indicated that TXNRD1 primarily participated in cell cycle and lipid metabolism. Immune infiltration analysis showed that TXNRD1 has a strong association with immune cells and could impact immunotherapy. Then, we identified small molecular compounds that inhibit TXNRD1 and confirmed TXNRD1 expression by single-cell landscape and immunohistochemistry. ConclusionThis study validated the diagnostic value of TrxR and TXNRD1 in clinical cohorts and transcriptional data, respectively. TrxR and TXNRD1 could be used in the risk diagnosis of early LUAD and facilitate personalized treatment strategies.

A nomogram for preoperative prediction of vessels encapsulating tumor clusters (VETC) pattern and prognosis of hepatocellular carcinoma

BackgroundVessels encapsulating tumor clusters (VETC) pattern of hepatocellular carcinoma (HCC) are associated with unfavorable prognosis. This study aimed to establish a nomogram model to predict VETC patterns based on preoperative CT imaging features. Patients and methodsPatients who underwent surgical resection between January 1, 2016 and August 31, 2022 were retrospectively included. Predictors associated with VETC pattern were determined by using logistic regression analyses, and a nomogram model was constructed. Prognostic factors associated with recurrence-free survival (RFS) after surgical resection were identified by using Cox regression analyses. ResultsA total of 84 patients were included for CT analysis. All patients underwent radical surgical resection. AST/ALT >1.07(odds ratio [OR], 4.91; 95 ​% CI: 1.11, 21.68; P ​< ​0.05), intratumoral necrosis (OR, 4.99; 95 ​% CI: 1.25, 19.99; P ​< ​0.05) and enhancing capsule (OR, 3.32; 95 ​% CI: 1.27, 8.94; P ​< ​0.05) were independent predictors of VETC pattern. These features were used for the construction of nomogram model, which showed comparable prediction performance, with AUC value of 0.767 (95%CI [0.662, 0.852]). CK19 status (Hazard ratio [HR], 2.02; 95 ​% CI: 1.06, 3.86; P ​< ​0.05), the number of tumors (HR, 3.31; 95 ​% CI: 1.47, 7.45; P ​< ​0.05) and VETC pattern (HR, 2.52; 95 ​% CI: 1.31, 4.86; P ​< ​0.05) were independent predictors of postoperative RFS. ConclusionA nomogram model based on preoperative CT imaging features could be used for the characterization of VETC pattern, and has prognostic significance for postoperative RFS in patients with HCC.

Factors Influencing Treatment Success in Cholesteatoma Management: A Cross-Sectional Study.

Background/Objectives: Cholesteatoma presents significant management challenges in otolaryngology. This study aimed to delineate the influence of demographic and clinical characteristics, preoperative imaging, and surgical approaches on treatment success in cholesteatoma management. Methods: A cross-sectional analytical study was conducted at the Otolaryngology Department of the University Hospital from January 2021 to December 2022. It included 68 patients diagnosed with cholesteatoma, focusing on three objectives: assessing the impact of demographic and clinical characteristics on treatment outcomes, evaluating the predictive value of preoperative imaging findings, and analyzing the influence of surgical factors. Results: The study population predominantly consisted of male (56%) and Saudi (81%) patients, with an average age of 45 years. Logistic regression revealed that older age (OR: 1.05), male gender (OR: 0.63), and non-Saudi Arab ethnicity (OR: 2.14) significantly impacted treatment outcomes. Clinical characteristics such as severe disease severity (OR: 3.00) and longer symptom duration (OR: 0.96) also influenced treatment success. In preoperative imaging, labyrinthine fistula (Regression Coefficient: 0.63) and epidural extension (Coefficient: 0.55) emerged as key predictors. The surgical factors that significantly affected the outcomes included the extent of surgery (Complete Removal OR: 3.32) and the use of endoscopic approaches (OR: 1.42). Conclusions: This study highlights that patient demographics, clinical profiles, specific preoperative imaging features, and surgical strategies multifactorially determine cholesteatoma treatment success. These findings suggest the necessity for a tailored approach in cholesteatoma management, reinforcing the importance of individualized treatment plans based on comprehensive preoperative assessments.

Investigating the predictive factors of thoracic aortic invasion and surgical outcomes in patients with primary lung cancer: A retrospective study.

This study aimed to investigate predictors of thoracic aortic invasion in lung cancer patients using preoperative clinical and imaging characteristics and elucidate surgical outcomes in cases of aortic invasion. Of the 4751 lung cancer patients who underwent surgery at our hospital, we included 126 (6.8%) who underwent left-sided surgery and in whom tumor appeared to be in contact with the thoracic aorta on preoperative imaging. The patients were divided into two groups: group A, 23 patients (18%) who underwent combined aortic resection (+); group B, 103 patients (82%) who did not undergo combined aortic resection (-). The percentage of aortic invasion for tumor diameter <3 cm, 3-4 cm, 4-5 cm, 5-7 cm, and >7 cm was 0%, 13%, 23%, 16%, and 35%, respectively. The percentages of aortic invasion were 27%, 16%, and 0% for tumor localization in the upper division, S6, and S10, respectively. Multivariate analysis revealed that aortic depression due to tumor or loss of fatty tissue between tumor and mediastinum in the chest CT significantly predicted aortic invasion (odds ratio = 23.83, 16.66). Group A demonstrated significantly more blood loss, longer operative time, prolonged hospital stay, and increased percentage of recurrent nerve palsy (13%) compared to group B. The 1-, 3-, and 5-year survival rates for patients in group A were 53.4%, 24.3%, and 24.3%, respectively. If the chest CT of a patient demonstrates aortic depression due to tumor or loss of fatty tissue between tumor and mediastinum, aortic complications should be considered when planning surgery.

368 Preoperative Soft Tissue Imaging Features Elucidated by Artificial Intelligence May Best Predict Radiographic Proximal Junction Kyphosis Following Adult Spinal Deformity Surgery

INTRODUCTION: Proximal junction kyphosis (PJK) following adult spinal deformity surgery (ASD) remains difficult to predict. In a cohort of patients undergoing ASD surgery, we used artificial intelligence to compare three models of preoperatively predicting PJK using: 1) traditional radiographic measurements and demographics 2) raw preoperative scoliosis x-rays, and 3) raw preoperative thoracic T1 MRIs. METHODS: A single-institution retrospective cohort study was undertaken for patients undergoing ASD surgery from 2009-21. PJK was defined as a sagittal Cobb angle of upper-instrumented vertebra (UIV) and UIV+2&gt;10° and a postoperative change in UIV/UIV+2&gt;10°. For Model-1, A support vector machine was used to predict PJK within 2 years postoperatively using clinical and traditional sagittal/coronal radiographic variables and intended levels of instrumentation. Next, for Model-2, a convolutional neural network (CNN) was trained on raw preoperative lateral/posterior-anterior scoliosis x-rays. Finally, for Model-3, a CNN was trained on raw thoracic T1 MRIs. RESULTS: A total of 187 patients underwent ASD surgery with at least 2-year follow-up and 89 (47.6%) developed radiographic PJK within 2 years. Model-1: Using clinical variables and traditional radiographic measurements, the support vector machine predicted PJK with a sensitivity:57.2% and specificity:56.3%. Model-2: a CNN with raw scoliosis x-rays predicted PJK with sensitivity:68.2% and specificity:58.3%. Model-3: a CNN with raw thoracic MRIs predicted PJK with sensitivity:73.1% and specificity:79.5%. Finally, an attention map outlined the imaging features used by Model-3 to predict PJK. Soft tissue features predominated all true positive PJK predictions. CONCLUSIONS: The use of raw MRIs in an artificial intelligence model improved the accuracy of PJK prediction compared to raw scoliosis X-rays and traditional clinical/radiographic measurements. The improved predictive ability using MRI may indicate that PJK is best predicted by soft-tissue degeneration and muscle atrophy.

Magnetic resonance elastography as a preoperative assessment for predicting intrahepatic recurrence in patients with hepatocellular carcinoma

PurposeMagnetic resonance elastography (MRE) is a noninvasive tool for diagnosing hepatic fibrosis with high accuracy. We investigated the preoperative clinical and imaging predictors of intrahepatic recurrence after curative resection of hepatocellular carcinoma (HCC), and evaluated MRE as a predictor of intrahepatic recurrence. MethodsWe retrospectively evaluated 80 patients who underwent preoperative contrast-enhanced magnetic resonance imaging (MRI) with two-dimensional MRE and curative resection for treatment-naïve HCC between May 2019 and December 2021. Liver stiffness (LS) was measured on the elastograms, and the optimal cutoff of LS for predicting intrahepatic recurrence was obtained using receiver operating characteristic (ROC) analysis. An LS above this cutoff was defined as MRE-recurrence. Preoperative imaging features of the tumor were assessed on MRI, including features in the Liver Imaging Reporting and Data System and microvascular invasion (MVI). Recurrence-free survival (RFS) rates were estimated using the Kaplan-Meier method, and differences were compared using the log-rank test. Using a Cox proportional hazards model, we conducted a multivariable analysis to investigate the factors affecting recurrence-free survival. ResultsDuring a median follow-up period of 32 months (range, 4–52 months), thirteen patients (16.3%) developed intrahepatic recurrence. ROC analysis determined an LS cutoff of ≥4.35 kPa to define MRE-recurrence. The 4-year RFS rate was significantly higher in patients without MRE-recurrence than in those with MRE-recurrence (93.4% vs. 48.9%; p = 0.001). In multivariable analysis, MRE-recurrence (Hazard ratio [HR], 5.9; 95% confidence interval [CI], 1.5–23.1) and MVI (HR, 3.4; 95% CI, 1.0–11.3) were independent predictors of intrahepatic recurrence. ConclusionsPatients without MRE-recurrence had significantly higher RFS rates than those with MRE-recurrence. MRE-recurrence and MVI were independent predictors of intrahepatic recurrence in patients after curative resection for HCC.

Renal cell carcinoma and venous tumor thrombus: predicting sarcomatoid dedifferentiation through preoperative IVIM-based MR imaging.

To evaluate the value of preoperative intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and conventional MRI indicators in identifying sarcomatoid dedifferentiation in renal cell carcinoma (RCC) and tumor thrombus. From September 2016 to April 2023, consecutive patients with RCC and tumor thrombus who received routine MRI examination and IVIM-DWI before radical resection were enrolled prospectively. Kaplan-Meier method with log-rank test was used to calculate and compare the survival probability. The preoperative imaging features were analyzed. Univariate and multivariable logistic regression analyses were employed to identify independent predictors of sarcomatoid dedifferentiation. The predictive ability was evaluated by receiver operating characteristic (ROC) curves. Twenty-two patients (15.3%) of the 144 patients in the training set (median age, 58.0years [IQR, 52.0-65.0years]; 108 men) and 11 patients (22.4%) of the 49 patients in the test set (median age, 58.0years [IQR, 53.0-63.0years]; 38 men) had sarcomatoid dedifferentiated tumors. Patients with sarcomatoid-differentiated tumors had poor progress-free survival in the training set and test set (P < 0.001 and P = 0.007). f value (P = 0.011), mN stage (P = 0.007), and necrosis (P = 0.041) were independent predictors for predicting sarcomatoid dedifferentiation in the training set. The model combining conventional MRI features and f value had AUCs of 0.832 (95% CI 0.755-0.909) and 0.825 (95% CI 0.702-0.948) in predicting sarcomatoid dedifferentiation in the training set and test set. It is feasible to preoperatively identify sarcomatoid dedifferentiation based on IVIM-DWI and conventional MR imaging indicators.