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BACKGROUND: In recent years, the traditional simulation-based medical teaching approach has faced challenges in meeting the requirements of practical emergency medicine education. This study utilized open-source tools and software to develop immersive panoramic videos using virtual reality technology for emergency medical teaching. It aims to investigate the efficacy of this novel teaching methodology. This transformation shifted the focus from physical simulation to virtual simulation in medical education, establishing a metaverse for emergency medical teaching. METHODS: In accordance with the curriculum guidelines, the instructors produced panoramic videos demonstrating procedures such as spinal injury management, humeral fracture with abdominal wall intestinal tube prolapse, head and chest composite injuries, cardiopulmonary resuscitation, and tracheal intubation. Using Unity software, a virtual training application for bronchoscopy was developed and integrated into the PICO4 VR all-in-one device to create a metaverse teaching environment. Fourth-year medical undergraduate students were allocated into either an experimental group (n = 26) or a control group (n = 30) based on student IDs. The experimental group received instruction through the metaverse immersive teaching method, while the control group followed the traditional simulation-based medical teaching approach. Both groups participated in theoretical and practical lessons as usual. Subsequently, all students underwent a four-station Objective Structured Clinical Examination (OSCE) to assess the effectiveness of the teaching methods based on their performance. Additionally, students in the experimental group provided subjective evaluations to assess their acceptance of the new teaching approach. RESULTS: Before the training commenced, there were no significant statistical differences in the first aid test scores between the experimental and control groups. Following the training, the experimental group outperformed the control group in the four-station OSCE examination, with all P-values being less than 0.05. The satisfaction rate among the experimental group regarding the new teaching method reached 88.46%, reflecting levels of satisfaction and extreme satisfaction. CONCLUSION: The open-source metaverse immersive teaching method has demonstrated a positive impact on enhancing the emergency skills of medical undergraduate students, with a high level of acceptance among students. In comparison to traditional simulated medical teaching methods, this approach requires less time and space, incurring lower costs, and is deemed worthy of wider adoption.
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Clinical Competence , Education, Medical, Undergraduate , Emergency Medicine , Virtual Reality , Humans , Education, Medical, Undergraduate/methods , Emergency Medicine/education , Students, Medical , Male , Female , Simulation Training , Educational Measurement , Curriculum , Young AdultABSTRACT
RATIONALE AND OBJECTIVES: We examined the effectiveness of computed tomography (CT)-based deep learning (DL) models in differentiating benign and malignant solid pulmonary nodules (SPNs) ≤ 8 mm. MATERIALS AND METHODS: The study patients (n = 719) were divided into internal training, internal validation, and external validation cohorts; all had small SPNs and had undergone preoperative chest CTs and surgical resection. We developed five DL models incorporating features of the nodule and five different peri-nodular regions with the Multiscale Dual Attention Network (MDANet) to differentiate benign and malignant SPNs. We selected the best-performing model, which was then compared to four conventional algorithms (VGG19, ResNet50, ResNeXt50, and DenseNet121). Furthermore, another five DL models were constructed using MDANet to distinguish benign tumors from inflammatory nodules and the one performed best was selected out. RESULTS: Model 4, which incorporated the nodule and 15 mm peri-nodular region, best differentiated benign and malignant SPNs. The model had an area under the curve (AUC), accuracy, recall, precision, and F1-score of 0.730, 0.724, 0.711, 0.705, and 0.707 in the external validation cohort. Model 4 also performed better than the other four conventional algorithms. Model 8, which incorporated the nodule and 10 mm peri-nodular region, was the best model for distinguishing benign tumors from inflammatory nodules. The model had an AUC, accuracy, recall, precision, and F1-score of 0.871, 0.938, 0.863, 0.904, and 0.882 in the external validation cohort. CONCLUSION: The study concludes that CT-based DL models built with MDANet can accurately discriminate among small benign and malignant SPNs, benign tumors and inflammatory nodules.
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OBJECTIVES: To develop an interactive, non-invasive artificial intelligence (AI) system for malignancy risk prediction in cystic renal lesions (CRLs). METHODS: In this retrospective, multicenter diagnostic study, we evaluated 715 patients. An interactive geodesic-based 3D segmentation model was created for CRLs segmentation. A CRLs classification model was developed using spatial encoder temporal decoder (SETD) architecture. The classification model combines a 3D-ResNet50 network for extracting spatial features and a gated recurrent unit (GRU) network for decoding temporal features from multi-phase CT images. We assessed the segmentation model using sensitivity (SEN), specificity (SPE), intersection over union (IOU), and dice similarity (Dice) metrics. The classification model's performance was evaluated using the area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA). RESULTS: From 2012 to 2023, we included 477 CRLs (median age, 57 [IQR: 48-65]; 173 men) in the training cohort, 226 CRLs (median age, 60 [IQR: 52-69]; 77 men) in the validation cohort, and 239 CRLs (median age, 59 [IQR: 53-69]; 95 men) in the testing cohort (external validation cohort 1, cohort 2, and cohort 3). The segmentation model and SETD classifier exhibited excellent performance in both validation (AUC = 0.973, ACC = 0.916, Dice = 0.847, IOU = 0.743, SEN = 0.840, SPE = 1.000) and testing datasets (AUC = 0.998, ACC = 0.988, Dice = 0.861, IOU = 0.762, SEN = 0.876, SPE = 1.000). CONCLUSION: The AI system demonstrated excellent benign-malignant discriminatory ability across both validation and testing datasets and illustrated improved clinical decision-making utility. CRITICAL RELEVANCE STATEMENT: In this era when incidental CRLs are prevalent, this interactive, non-invasive AI system will facilitate accurate diagnosis of CRLs, reducing excessive follow-up and overtreatment. KEY POINTS: The rising prevalence of CRLs necessitates better malignancy prediction strategies. The AI system demonstrated excellent diagnostic performance in identifying malignant CRL. The AI system illustrated improved clinical decision-making utility.
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Background: The risk of locoregional recurrence (LRR) and the long-term prognosis of breast-conserving surgery (BCS) after neoadjuvant chemotherapy (NAC) are still controversial. This study aimed to evaluate oncological outcomes for patients undergoing BCS after NAC and determine LRR and survival predictors. Methods: This study was a retrospective cohort study of patients with locally advanced breast cancer (LABC) who received NAC and underwent BCS or mastectomy from June 2011 to November 2020. LRR, disease-free survival (DFS), and overall survival (OS) were compared in patients undergoing BCS or mastectomy. Univariate and multivariate analyses were performed to determine LRR, DFS, and OS predictors. Results: A total of 585 patients were included, of whom 106 (18.1%) underwent BCS and 479 (81.9%) underwent a mastectomy. The LRR rate was 11.3% in the BCS group and 16.3% in the mastectomy group, revealing no significant difference(p = 0.200). In patients who underwent BCS, clinical lymph node status, histological grade and pathological complete response (pCR) were independent factors to predict LRR. There was no significant difference in DFS and OS between the BCS and the mastectomy groups. Multivariable analysis showed that lymph node status, histological grade, molecular subtypes, pCR and Miller&Payne (M&P) classification were independent predictors of DFS. Lymph node status, molecular subtypes and pCR were independent predictors of OS. BCS or mastectomy was not an independent predictor of DFS or OS. Conclusion: Compared with mastectomy, BCS after NAC may not increase the risk of local recurrence or mortality, BCS can be performed in selected patients with small tumor size and good response to NAC.
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PURPOSE: To investigate the computed tomography (CT) characteristics of air-containing space and its specific patterns in neoplastic and non-neoplastic ground glass nodules (GGNs) for clarifying their significance in differential diagnosis. MATERIALS AND METHODS: From January 2015 to October 2022, 1328 patients with 1,350 neoplastic GGNs and 462 patients with 465 non-neoplastic GGNs were retrospectively enrolled. Their clinical and CT data were analyzed and compared with emphasis on revealing the differences of air-containing space and its specific patterns (air bronchogram and bubble-like lucency [BLL]) between neoplastic and non-neoplastic GGNs and their significance in differentiating them. RESULTS: Compared with patients with non-neoplastic GGNs, female was more common (P < 0.001) and lesions were larger (P < 0.001) in those with neoplastic ones. Air bronchogram (30.1% vs. 17.2%), and BLL (13.0% vs. 2.6%) were all more frequent in neoplastic GGNs than in non-neoplastic ones (each P < 0.001), and the BLL had the highest specificity (93.6%) in differentiation. Among neoplastic GGNs, the BLL was more frequently detected in the larger (14.9 ± 6.0 mm vs. 11.4 ± 4.9 mm, P < 0.001) and part-solid (15.3% vs. 10.7%, P = 0.011) ones, and its incidence significantly increased along with the invasiveness (9.5-18.0%, P = 0.001), whereas no significant correlation was observed between the occurrence of BLL and lesion size, attenuation, or invasiveness. CONCLUSION: The air containing space and its specific patterns are of great value in differentiating GGNs, while BLL is a more specific and independent sign of neoplasms.
Subject(s)
Lung Neoplasms , Multiple Pulmonary Nodules , Humans , Female , Lung Neoplasms/diagnostic imaging , Lung Neoplasms/pathology , Retrospective Studies , Multiple Pulmonary Nodules/diagnostic imaging , Multiple Pulmonary Nodules/pathology , Tomography, X-Ray Computed/methods , Diagnosis, DifferentialABSTRACT
Background: The solid component of subsolid nodules (SSNs) is closely associated with the invasiveness of lung adenocarcinoma, and its accurate assessment is crucial for selecting treatment method. Therefore, this study aimed to evaluate the accuracy of solid component size within SSNs measured on multiplanar volume rendering (MPVR) and compare it with the dimensions of invasive components on pathology. Methods: A pilot study was conducted using a chest phantom to determine the optimal MPVR threshold for the solid component within SSN, and then clinical validation was carried out by retrospective inclusion of patients with pathologically confirmed solitary SSN from October 2020 to October 2021. The radiological tumor size on MPVR and solid component size on MPVR (RSSm) and on lung window (RSSl) were measured. The size of the tumor and invasion were measured on the pathological section, and the invasion, fibrosis, and inflammation within SSNs were also recorded. The measurement difference between computed tomography (CT) and pathology, inter-observer and inter-measurement agreement were analyzed. Receiver operating characteristic (ROC) analysis and Bland-Altman plot were performed to evaluate the diagnostic efficiency of MPVR. Results: A total of 142 patients (mean age, 54±11 years, 39 men) were retrospectively enrolled in the clinical study, with 26 adenocarcinomas in situ, 92 minimally invasive adenocarcinomas (MIAs), and 24 invasive adenocarcinomas (IAs). The RSSl was significantly smaller than pathological invasion size with fair inter-measurement agreement [intraclass correlation coefficient (ICC) =0.562, P<0.001] and moderate interobserver agreement (ICC =0.761, P<0.001). The RSSm was significantly larger than pathological invasion size with the excellent inter-measurement agreement (ICC =0.829, P<0.001) and excellent (ICC =0.952, P<0.001) interobserver agreement. ROC analysis showed that the cutoff value of RSSm for differentiating adenocarcinoma in situ from MIA and MIA from IA was 1.85 and 6.45 mm (sensitivity: 93.8% and 95.5%, specificity: 85.7% and 88.2%, 95% confidence internal: 0.914-0.993 and 0.900-0.983), respectively. The positive predictive value-and negative predictive value of MPVR in predicting invasiveness were 92.8% and 100%, respectively. Conclusions: Using MPVR to predict the invasive degree of SSN had high accuracy and good inter-observer agreement, which is superior to lung window measurements and helpful for clinical decision-making.
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To investigate the role and potential mechanism of serine/threonine kinase 36 (STK36) in docetaxel resistance-prostate cancer (PCa). The expression of STK36 in PCa and the correlation with clinicopathological characteristics of PCa patients were analyzed using the data from different databases and tissue microarrays. To investigate the role of STK36 on cell proliferation, invasion, and migration, STK36 was overexpressed and silenced in DU-145 and PC-3 cell lines. Cell counting kit-8 (CCK8) was used to test cell proliferation. Cell invasion and migration were detected by cell wound scratch assay and trans well, respectively. The expression profile of STK36, E-Cadherin, and Vimentin was analyzed by Western blot. Cell apoptosis was detected by the TUNEL assay. STK36 expression was upregulated in PCa tissue compared with adjacent benign PCa tissue; it was higher in patients with advanced stages compared with lower stages and was significantly correlated with decreased overall survival. Up-regulation of STK36 significantly promoted the proliferation, invasion, and migration of DU-145 and PC-3 cells and compensated for the suppression caused by docetaxel treatment in vitro. A striking apoptosis inhibition could be observed when dealing with docetaxel, although the apoptosis of DU-145 and PC-3 cells was not affected by the STK36 exclusive overexpression. Besides, E-Cadherin expression was restrained while the expression levels of vimentin were all enhanced. The knockdown of STK36 reversed the above process. STK36 up-regulation could accelerate the biological behavior and docetaxel resistance of PCa by epithelial-mesenchymal transition (EMT) activation. STK36 may be potentially used as a target in PCa resolvent with docetaxel.
Subject(s)
Prostatic Neoplasms , Male , Humans , Docetaxel/pharmacology , Vimentin/genetics , Prostatic Neoplasms/drug therapy , Prostatic Neoplasms/genetics , Cadherins/genetics , Epithelial-Mesenchymal Transition/genetics , Serine , Protein Serine-Threonine Kinases/geneticsABSTRACT
Purpose: To determine the value of intrapulmonary concomitant lesions in differentiating non-neoplastic and neoplastic ground-glass nodules (GGNs). Patients and Methods: From January 2014 to March 2022, 395 and 583 patients with confirmed non-neoplastic and neoplastic GGNs were retrospectively enrolled. Their clinical and chest CT data were evaluated. The CT features of target GGNs and intrapulmonary concomitant lesions in these two groups were analyzed and compared, and the role of intrapulmonary concomitant lesions in improving differentiation was evaluated. Results: The intrapulmonary concomitant lesions were more common in patients with non-neoplastic GGNs than in those with neoplastic ones (87.88% vs 82.18%, P = 0.015). Specifically, patients with non-neoplastic GGNs had a higher incidence of multiple solid nodules (SNs), patchy ground-glass opacity/consolidation, and fibrosis/calcification in any lung fields (each P < 0.05). Logistic regression analysis indicated that patients < 44 years old, diameter < 7.35 mm, irregular shape, and coarse margin or ill-defined boundary for target GGN, pleural thickening, and concomitant SNs in the same lobe and fibrosis or calcification in any lung field were independent indicators for predicting non-neoplastic GGNs. The AUC of the model for predicting non-neoplastic GGNs increased from 0.894 to 0.926 (sensitivity, 83.10%; specificity, 87.10%) after including the concomitant lesions in the patients' clinical characteristics and CT features of target GGNs (P < 0.0001). Conclusion: Besides the patients' clinical characteristics and CT features of target GGNs, the concomitant multiple SNs in the same lobe and fibrosis/calcification in any lung field should be considered in further differentiating non-neoplastic and neoplastic GGNs.
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OBJECTIVE: To investigate the dynamic changes during follow-up computed tomography (CT), histological subtypes, gene mutation status, and surgical prognosis for different morphological presentations of solitary lung adenocarcinomas (SLADC). MATERIALS AND METHODS: This retrospective study compared dynamic tumor changes and volume doubling time (VDT) in 228 patients with SLADC (morphological types I-IV) who had intermittent growth during follow-ups. The correlation between the morphological classification and histological subtypes, gene mutation status, and surgical prognosis was evaluated. RESULTS: Among the 228 patients, 66 (28.9%) were classified as type I, 123 (53.9%) as type II, 16 (7%) as type III, and 23 (10.1%) as type IV. Type I had the shortest VDT (254 days), followed by types IV (381 days) and III (501 days), and then type II (993 days) (p < 0.05 each). Type I had a greater proportion of solid/micropapillary-predominant pattern than type II, and the lepidic-predominant pattern was more common in type II and III than in type I (p < 0.05 each). Furthermore, type II and IV SLADCs were correlated with positive epidermal growth factor receptor mutation (p < 0.05 each). Lastly, the Kaplan-Meier curves showed that the disease-free survival was longest for patients with type II tumors, followed by those with type III and IV tumors, and then those with type I tumors (p < 0.001 each). CONCLUSION: A good understanding of the natural progression and pathological-molecular characteristics of different morphological SLADC types can help make accurate diagnoses, develop individual treatment strategies, and predict patient outcomes. CRITICAL RELEVANCE STATEMENT: A good understanding of the natural progression and pathological-molecular characteristics of different morphological solitary lung adenocarcinoma types can help make accurate diagnoses, develop individual treatment strategies, and predict patient outcomes. KEY POINTS: ⢠Type I-IV solitary lung adenocarcinomas exhibit varying natural progression on serial CT scans. ⢠Morphological classification of solitary lung adenocarcinomas predicts histological subtype, gene status, and surgical prognosis. ⢠This classification of solitary lung adenocarcinomas may help improve diagnostic, therapeutic, and prognosticating abilities.
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BACKGROUND: Pulmonary solid pleura-attached nodules (SPANs) are not very commonly detected and thus not well studied and understood. This study aimed to identify the clinical and CT characteristics for differentiating benign and malignant SPANs. RESULTS: From January 2017 to March 2023, a total of 295 patients with 300 SPANs (128 benign and 172 malignant) were retrospectively enrolled. Between benign and malignant SPANs, there were significant differences in patients' age, smoking history, clinical symptoms, CT features, nodule-pleura interface, adjacent pleural change, peripheral concomitant lesions, and lymph node enlargement. Multivariate analysis revealed that smoking history (odds ratio [OR], 2.016; 95% confidence interval [CI], 1.037-3.919; p = 0.039), abutting the mediastinal pleura (OR, 3.325; 95% CI, 1.235-8.949; p = 0.017), nodule diameter (> 15.6 mm) (OR, 2.266; 95% CI, 1.161-4.423; p = 0.016), lobulation (OR, 8.922; 95% CI, 4.567-17.431; p < 0.001), narrow basement to pleura (OR, 6.035; 95% CI, 2.847-12.795; p < 0.001), and simultaneous hilar and mediastinal lymph nodule enlargement (OR, 4.971; 95% CI, 1.526-16.198; p = 0.008) were independent predictors of malignant SPANs, and the area under the curve (AUC) of this model was 0.890 (sensitivity, 82.0%, specificity, 77.3%) (p < 0.001). CONCLUSION: In patients with a smoking history, SPANs abutting the mediastinal pleura, having larger size (> 15.6 mm in diameter), lobulation, narrow basement, or simultaneous hilar and mediastinal lymph nodule enlargement are more likely to be malignant. CRITICAL RELEVANCE STATEMENT: The benign and malignant SPANs have significant differences in clinical and CT features. Understanding the differences between benign and malignant SPANs is helpful for selecting the high-risk ones and avoiding unnecessary surgical resection. KEY POINTS: ⢠The solid pleura-attached nodules (SPANs) are closely related to the pleura. ⢠Relationship between nodule and pleura and pleural changes are important for differentiating SPANs. ⢠Benign SPANs frequently have broad pleural thickening or embed in thickened pleura. ⢠Smoking history and lesions abutting the mediastinal pleura are indicators of malignant SPANs. ⢠Malignant SPANs usually have larger diameters, lobulation signs, narrow basements, and lymphadenopathy.
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BACKGROUND: Some peripheral small cell lung cancers (pSCLCs) and benign lung tumors (pBLTs) have similar morphological features but different treatment and prognosis. PURPOSE: To determine the significance of marginal vessels in differentiating pSCLCs and pBLTs. MATERIAL AND METHODS: A total of 57 and 95 patients with pathological confirmed nodular (≤3â cm) pSCLC and pBLT with similar morphological features were enrolled in this study retrospectively. The patients' clinical characteristics and computed tomography (CT) features of tumors and marginal vessels (vessels connecting with tumors) were analyzed and compared. RESULTS: Compared with pBLTs, pSCLCs had a larger diameter (P = 0.001) but lower enhancement (P = 0.015) and fewer had calcification (P = 0.013). Compared with pBLTs, more lesions had proximal (70.2% vs. 22.1%) and distal (59.6% vs. 4.2%) marginal vessels in pSCLCs (each P < 0.0001). In addition, in pSCLCs, the numbers of proximal (1.3 ± 1.4 vs. 0.3 ± 0.6), distal (2.4 ± 3.1 vs. 0.1 ± 0.5), and total (3.6 ± 3.5 vs. 0.4 ± 1.0) marginal vessels were all more than those in pBLTs (each P < 0.001). Receiver operating characteristic curve analysis revealed the positive distal marginal vessel sign had the highest specificity (95.8%), and the number of total marginal vessels had the best performance in discriminating pSCLC from pBLT (cutoff value = 1.5, AUC = 0.80, 95% CI = 0.72-0.89, sensitivity = 70.2%, and specificity = 91.6%). CONCLUSION: For peripheral solid nodules similar to pBLTs but without any calcification, the possibility of pSCLC should be considered if they have multiple marginal vessels (≥2), especially the distal ones.
Subject(s)
Lung Neoplasms , Small Cell Lung Carcinoma , Humans , Retrospective Studies , Lung Neoplasms/diagnostic imaging , Lung Neoplasms/pathology , Tomography, X-Ray Computed/methods , Small Cell Lung Carcinoma/diagnostic imaging , ROC CurveABSTRACT
OBJECTIVE: To explore the effects of labor analgesia for primiparae with different stages of cervical dilation on parturition and neonates. MATERIALS AND METHODS: In the past three years, 530 cases of primiparae who had delivered in the Second People's Hospital of Hefei and were eligible for a vaginal trial of parturition were enrolled as the research subjects. Of these, 360 puerperae had labor analgesia, and the remaining 170 were taken as the control group. Those given labor analgesia were divided into three groups based on the different stages of cervical dilation at that time. There were 160 cases in Group I (cervical dilation <3 cm), 100 cases in Group II (cervical dilation of 3-4 cm), and 100 cases in Group III (cervical dilation of 4-6 cm). The labor and neonatal outcomes were compared among the four groups. RESULTS: The first, second, and total stages of labor in the three groups receiving labor analgesia were all longer than in the control group, and the differences were statistically significant (p < 0.05 in all). Group I had the longest duration of each stage and the total stage of labor. The differences in labor stages and the total stage of labor were not statistically significant between Group II and Group III (P > 0.05). In the three groups with labor analgesia, the usage rate of oxytocin was higher than in the control group, and the differences were statistically significant (P < 0.05). The differences in the incidence of postpartum hemorrhage, the incidence of postpartum urine retention, and the episiotomy rate were not statistically significant among the four groups (P > 0.05). The differences in the neonatal Apgar score were not statistically significant among the four groups (P > 0.05). CONCLUSION: Labor analgesia might prolong the stages of labor but does not affect the neonatal outcomes. It would be optimal to conduct labor analgesia when cervical dilation reaches 3-4 cm.
Subject(s)
Analgesia, Epidural , Analgesia, Obstetrical , Analgesia , Labor, Obstetric , Postpartum Hemorrhage , Female , Humans , Infant, Newborn , Pregnancy , Delivery, Obstetric , Parturition , Postpartum Hemorrhage/epidemiologyABSTRACT
IKKα has been shown to be responsible of multiple pro-tumorigenic functions and therapy resistance independent of canonical NF-κB, but its role in acquired chemotherapy resistance in breast cancer remains unclarified. In this study, we obtained pre-treatment biopsy and post-treatment mastectomy specimens from a retrospective cohort of triple-negative breast cancer (TNBC) patients treated with neoadjuvant chemotherapy(NAC) (n = 43). Immunohistochemical methods were used to detect the expression of IKKα before and after NAC, and the relationship between IKKα and the pathologic response to NAC was examined. In addition, we developed a new ADR-resistant MDA-MB-231 cell line(MDA-MB-231/ADR) and analyzed these cells for changes in IKKα expression, the role and mechanisms of the increased IKKα in promoting drug resistance were determined in vitro and in vivo. We demonstrated that the expression of IKKα in residual TNBC tissues after chemotherapy was significantly higher than that before chemotherapy, and was positively correlated with lower pathological reaction. IKKα expression was significantly higher in ADR-resistant TNBC cells than in ADR-sensitive cells, IKKα knockdown results in apoptotic cell death of chemoresistant cells upon drug treatment. Moreover, IKKα knockdown promotes chemotherapeutic drug-induced tumor cell death in an transplanted tumor mouse model. Functionally, we demonstrated that IKKα knockdown significantly upregulated the expression of cleaved caspase 3 and Bax and inhibited the expression of Bcl-2 upon ADR treatment. Our findings highlighted that IKKα exerts an important and previously unknown role in promoting chemoresistance in TNBC, combining IKKα inhibition with chemotherapy may be an effective strategy to improve treatment outcome in chemoresistant TNBC patients.
Subject(s)
Antineoplastic Agents , Triple Negative Breast Neoplasms , Humans , Animals , Mice , Triple Negative Breast Neoplasms/drug therapy , Triple Negative Breast Neoplasms/genetics , Triple Negative Breast Neoplasms/metabolism , I-kappa B Kinase/metabolism , Doxorubicin/pharmacology , Doxorubicin/therapeutic use , Retrospective Studies , Mastectomy , Apoptosis , Antineoplastic Agents/pharmacology , Cell Line, Tumor , Drug Resistance, NeoplasmABSTRACT
BACKGROUND. Pure ground-glass nodules (pGGNs) may represent a diverse range of histologic entities of varying aggressiveness. OBJECTIVE. The purpose of this study was to evaluate the use of the reticulation sign on thin-section CT images for predicting the invasiveness of pGGNs. METHODS. This retrospective study included 795 patients (mean age, 53.4 ± 11.1 [SD] years; 254 men, 541 women) with a total of 876 pGGNs on thin-section CT that underwent resection between January 2015 and April 2022. Two fellowship-trained thoracic radiologists independently reviewed unenhanced CT images to assess the pGGNs for a range of features, including diameter, attenuation, location, shape, air bronchogram, bubble lucency, vascular change, lobulation, spiculation, margins, pleural indentation, and the reticulation sign (defined as multiple small linear opacities resembling a mesh or a net); differences were resolved by consensus. The relationship between the reticulation sign and lesion invasiveness on pathologic assessment was evaluated. RESULTS. On pathologic assessment, the 876 pGGNs included 163 nonneoplastic and 713 neoplastic pGGNs (323 atypical adenomatous hyperplasias [AAHs] or adenocarcinomas in situ [AISs], 250 minimally invasive adenocarcinomas [MIAs], and 140 invasive adenocarcinomas [IACs]). Interobserver agreement for the reticulation sign, expressed as kappa, was 0.870. The reticulation sign was detected in 0.0% of nonneoplastic lesions, 0.0% of AAHs/AISs, 6.8% of MIAs, and 54.3% of IACs. The reticulation sign had sensitivity of 24.0% and specificity of 100.0% for a diagnosis of MIA or IAC and sensitivity of 54.3% and specificity of 97.7% for a diagnosis of IAC. In multivariable regression analyses including all of the assessed CT features, the reticulation sign was a significant independent predictor of IAC (OR, 3.64; p = .001) but was not a significant independent predictor of MIA or IAC. CONCLUSION. The reticulation sign, when observed in a pGGN on thin-section CT, has high specificity (albeit low sensitivity) for invasiveness and is an independent predictor of IAC. CLINICAL IMPACT. Those pGGNs that show the reticulation sign should be strongly suspected to represent IAC; this suspicion may guide risk assessments and follow-up recommendations.
Subject(s)
Adenocarcinoma in Situ , Adenocarcinoma , Lung Neoplasms , Precancerous Conditions , Male , Humans , Female , Adult , Middle Aged , Lung Neoplasms/pathology , Retrospective Studies , Tomography, X-Ray Computed/methods , Neoplasm Invasiveness/diagnostic imaging , Adenocarcinoma/pathology , Adenocarcinoma in Situ/pathology , Hyperplasia , Precancerous Conditions/pathologyABSTRACT
OBJECTIVES: Noncontrast computed tomography (NCCT) imaging markers are associated with early perihematomal edema (PHE) growth. The aim of this study was to compare the predictive value of different NCCT markers in predicting early PHE expansion. METHODS: ICH patients who underwent baseline CT scan within 6â¯h of symptoms onset and follow-up CT scan within 36â¯h between July 2011 and March 2017 were included in this study. The predictive value of hypodensity, satellite sign, heterogeneous density, irregular shape, blend sign, black hole sign, island sign and expansion-prone hematoma for early perihematomal edema expansion were assessed, separately. RESULTS: 214 patients were included in our final analysis. After adjusting for ICH characteristics, hypodensity, blend sign, island sign and expansion-prone hematoma are still predictors of early perihematomal edema expansion in multivariable logistics regression analysis (all Pâ¯<â¯0.05). The area under the receiver operating characteristic (ROC) curve of expansion-prone hematoma was significantly larger than the area under the ROC curve of hypodensity, blend sign and island sign in predicting PHE expansion (Pâ¯=â¯0.003, Pâ¯<â¯0.001 and Pâ¯=â¯0.002, respectively). CONCLUSION: Compared with single NCCT imaging markers, expansion-prone hematoma seems to be optimal predictor for early PHE expansion than any single NCCT imaging marker.
Subject(s)
Cerebral Hemorrhage , Tomography, X-Ray Computed , Humans , Cerebral Hemorrhage/complications , Cerebral Hemorrhage/diagnostic imaging , Hematoma/complications , Hematoma/diagnostic imaging , ROC Curve , Edema/diagnostic imaging , Edema/etiology , Retrospective StudiesABSTRACT
OBJECTIVES: To investigate the predictive value of noncontrast computed tomography (NCCT) models based on radiomics features and machine learning for early perihematomal edema (PHE) expansion in patients with spontaneous intracerebral hemorrhage (ICH). METHODS: We retrospectively reviewed NCCT data from 214 patients with spontaneous ICH. All radiomics features were extracted from volume of interest of hematomas on admission scans. A total of 8 machine learning methods were applied for constructing models in the training and the test set. Receiver operating characteristic analysis and the areas under the curve were used to evaluate the predictive value. RESULTS: A total of 23 features were finally selected to establish models of early PHE expansion after feature screening. Patients were randomly assigned into training (n = 171) and test (n = 43) sets. The accuracy, sensitivity, and specificity in the test set were 72.1%, 90.0%, and 66.7% for the support vector machine model; 79.1%, 70.0%, and 84.4% for the k-nearest neighbor model; 88.4%, 90.0%, and 87.9% for the logistic regression model; 74.4%, 90.0%, and 69.7% for the extra tree model; 74.4%, 90.0%, and 69.7% for the extreme gradient boosting model; 83.7%, 100%, and 78.8% for the multilayer perceptron (MLP) model; 72.1%, 100%, and 65.6% for the light gradient boosting machine model; and 60.5%, 90.0%, and 53.1% for the random forest model, respectively. CONCLUSIONS: The MLP model seemed to be the best model for prediction of PHE expansion in patients with ICH. NCCT models based on radiomics features and machine learning could predict early PHE expansion and improve the discrimination of identify spontaneous intracerebral hemorrhage patients at risk of early PHE expansion.
Subject(s)
Cerebral Hemorrhage , Tomography, X-Ray Computed , Humans , Retrospective Studies , Cerebral Hemorrhage/diagnostic imaging , Tomography, X-Ray Computed/methods , Edema , Machine LearningABSTRACT
Purpose: To investigate the influence factors for the various boundary manifestations of pulmonary non-neoplastic ground glass nodules (GGNs) on computed tomography (CT). Materials and Methods: From January 2015 to March 2022, a total of 280 patients with 318 non-neoplastic GGNs were enrolled. The correlations between degree of inflammatory cell infiltration and relative density (ΔCT) and the boundary manifestations of lesions were evaluated, respectively. Results: Nongranulomatous nodules (283, 89.0%) with fibrous tissue proliferation and/or inflammatory cells as the predominant pathological findings were the most common non-neoplastic GGNs, followed by granulomatous nodules (28, 8.8%). Among nongranulomatous GGNs, cases with more and less/no inflammatory cells were 15 (10.9%) and 122 (89.1%) in 137 well-defined ones with smooth margin, 16 (24.6%) and 49 (75.4%) in 65 well-defined ones with coarse margin, 43 (91.5%) and 4 (8.5%) in 47 ill-defined ones with higher ΔCT (>151HU), and 4 (11.8%) and 30 (88.2%) in 34 ill-defined ones with lower ΔCT (< 151HU). The proportion of cases with more inflammatory cells in well-defined nodules was similar to that in ill-defined ones with lower ΔCT (P = 0.587) but significantly lower than that in ill-defined ones with higher ΔCT (P < 0.001). Among the granulomatous nodules, ill-defined cases with higher ΔCT (16, 57.1%) were the most common, and they (7/8, 87.5%) frequently had changes during short-term follow-up. Conclusion: Nongranulomatous nodules are the most common non-neoplastic GGNs, their diverse boundary manifestations closely correlate with degree of inflammatory cell infiltration and density difference.
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Background: Transition of the CT values from nodule to peripheral normal lung is related to pathological changes and may be a potential indicator for differential diagnosis. This study investigated the significance of the standard deviation (SD) values in the lesion-lung boundary zone when differentiating between benign and neoplastic subsolid nodules (SSNs). Methods: From January 2012 to July 2021, a total of 229 neoplastic and 84 benign SSNs confirmed by pathological examination were retrospectively and nonconsecutively enrolled in this study. The diagnostic study was not registered with a clinical trial platform, and the study protocol was not published. Computed tomography (CT) values of the ground-glass component (CT1), adjacent normal lung tissue (CT2), and lesion-lung boundary zone (CT3) were measured consecutively. The SD of CT3 was recorded to assess density variability. The CT1, CT2, CT3, and SD values were compared between benign and neoplastic SSNs. Results: No significant differences in CT1 and CT2 were observed between benign and neoplastic SSNs (each P value >0.05). CT3 (-736.1±51.0 vs. -792.6±73.9; P<0.001) and its SD (135.6±29.6 vs. 83.6±20.6; P<0.001) in neoplastic SSNs were significantly higher than those in benign SSNs. Moreover, the SD increased with the invasiveness degree of neoplastic SSNs (r=0.657; P<0.001). The receiver operating characteristic (ROC) curve revealed that the area under the curve was 0.927 (95% CI: 0.896-0.959) when using the SD (cutoff value =106.98) as a factor to distinguish SSNs, which increased to 0.966 (95% CI: 0.934-0.985) when including nodules with a CT1 of ≥-715 Hounsfield units (HU) only (cutoff of SD 109.9, sensitivity 0.930, and specificity 0.914). Conclusions: The SD as an objective index is valuable for differentiating SSNs, especially for those with a CT1 of ≥-715 HU, which have a higher possibility of neoplasm if the SD is >109.9.
ABSTRACT
BACKGROUND: Radiomics has been used to predict pulmonary nodule (PN) malignancy. However, most of the studies focused on pulmonary ground-glass nodules. The use of computed tomography (CT) radiomics in pulmonary solid nodules, particularly sub-centimeter solid nodules, is rare. PURPOSE: This study aims to develop a radiomics model based on non-enhanced CT images that can distinguish between benign and malignant sub-centimeter pulmonary solid nodules (SPSNs, <1 cm). METHODS: The clinical and CT data of 180 SPSNs confirmed by pathology were analyzed retrospectively. All SPSNs were divided into two groups: training set (n = 144) and testing set (n = 36). From non-enhanced chest CT images, over 1000 radiomics features were extracted. Radiomics feature selection was performed using the analysis of variance and principal component analysis. The selected radiomics features were fed into a support vector machine (SVM) to develop a radiomics model. The clinical and CT characteristics were used to develop a clinical model. Associating non-enhanced CT radiomics features with clinical factors were used to develop a combined model using SVM. The performance was evaluated using the area under the receiver-operating characteristic curve (AUC). RESULTS: The radiomics model performed well in distinguishing between benign and malignant SPSNs, with an AUC of 0.913 (95% confidence interval [CI], 0.862-0.954) in the training set and an AUC of 0.877 (95% CI, 0.817-0.924) in the testing set. The combined model outperformed the clinical and radiomics models with an AUC of 0.940 (95% CI, 0.906-0.969) in the training set and an AUC of 0.903 (95% CI, 0.857-0.944) in the testing set. CONCLUSIONS: Radiomics features based on non-enhanced CT images can be used to differentiate SPSNs. The combined model, which included radiomics and clinical factors, had the best discrimination power between benign and malignant SPSNs.
Subject(s)
Lung Neoplasms , Multiple Pulmonary Nodules , Humans , Retrospective Studies , Lung Neoplasms/diagnostic imaging , Lung Neoplasms/pathology , Multiple Pulmonary Nodules/diagnostic imaging , Multiple Pulmonary Nodules/pathology , Tomography, X-Ray Computed/methods , Machine LearningABSTRACT
BACKGROUND: The rising prevalence of cystic renal lesions (CRLs) detected by computed tomography necessitates better identification of the malignant cystic renal neoplasms since a significant majority of CRLs are benign renal cysts. Using arterial phase CT scans combined with pathology diagnosis results, a fusion feature-based blending ensemble machine learning model was created to identify malignant renal neoplasms from cystic renal lesions (CRLs). Histopathology results were adopted as diagnosis standard. Pretrained 3D-ResNet50 network was selected for non-handcrafted features extraction and pyradiomics toolbox was selected for handcrafted features extraction. Tenfold cross validated least absolute shrinkage and selection operator regression methods were selected to identify the most discriminative candidate features in the development cohort. Feature's reproducibility was evaluated by intra-class correlation coefficients and inter-class correlation coefficients. Pearson correlation coefficients for normal distribution and Spearman's rank correlation coefficients for non-normal distribution were utilized to remove redundant features. After that, a blending ensemble machine learning model were developed in training cohort. Area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA) were employed to evaluate the performance of the final model in testing cohort. RESULTS: The fusion feature-based machine learning algorithm demonstrated excellent diagnostic performance in external validation dataset (AUC = 0.934, ACC = 0.905). Net benefits presented by DCA are higher than Bosniak-2019 version classification for stratifying patients with CRL to the appropriate surgery procedure. CONCLUSIONS: Fusion feature-based classifier accurately distinguished malignant and benign CRLs which outperformed the Bosniak-2019 version classification and illustrated improved clinical decision-making utility.