Predicting MET exon 14 skipping mutation in pulmonary sarcomatoid carcinoma by whole-tumour texture analysis combined with clinical and conventional contrast-enhanced computed tomography features.

Background: Pulmonary sarcomatoid carcinoma (PSC) is a rare, highly malignant type of non-small cell lung cancer (NSCLC) with a poor prognosis. Targeted drugs for MET exon 14 (METex14) skipping mutation can have considerable clinical benefits. This study aimed to predict METex14 skipping mutation in PSC patients by whole-tumour texture analysis combined with clinical and conventional contrast-enhanced computed tomography (CECT) features. Methods: This retrospective study included 56 patients with PSC diagnosed by pathology. All patients underwent CECT before surgery or other treatment, and both targeted DNA- and RNA-based next-generation sequencing (NGS) were used to detect METex14 skipping mutation status. The patients were divided into two groups: METex14 skipping mutation and nonmutation groups. Overall, 1,316 texture features of the whole tumour were extracted. We also collected 12 clinical and 20 conventional CECT features. After dimensionality reduction and selection, predictive models were established by multivariate logistic regression analysis. Models were evaluated using the area under the curve (AUC), and the clinical utility of the model was assessed by decision curve analysis. Results: METex14 skipping mutation was detected in 17.9% of PSCs. Mutations were found more frequently in those (I) who had smaller long- or short-axis diameters (P=0.02, P=0.01); (II) who had lower T stages (I, II) (P=0.02); and (III) with pseudocapsular or annular enhancement (P=0.03). The combined model based on the conventional and texture models yielded the best performance in predicting METex14 skipping mutation with the highest AUC (0.89). The conventional and texture models also had good performance (AUC =0.83 conventional; =0.88 texture). Conclusions: Whole-tumour texture analysis combined with clinical and conventional CECT features may serve as a noninvasive tool to predict the METex14 skipping mutation status in PSC.

Incidence rate of occult lymph node metastasis in clinical T1-2N0M0 small cell lung cancer patients and radiomic prediction based on contrast-enhanced CT imaging: a multicenter study : Original research.

BACKGROUND: This study aimed to explore the incidence of occult lymph node metastasis (OLM) in clinical T1 - 2N0M0 (cT1 - 2N0M0) small cell lung cancer (SCLC) patients and develop machine learning prediction models using preoperative intratumoral and peritumoral contrast-enhanced CT-based radiomic data. METHODS: By conducting a retrospective analysis involving 242 eligible patients from 4 centeres, we determined the incidence of OLM in cT1 - 2N0M0 SCLC patients. For each lesion, two ROIs were defined using the gross tumour volume (GTV) and peritumoral volume 15 mm around the tumour (PTV). By extracting a comprehensive set of 1595 enhanced CT-based radiomic features individually from the GTV and PTV, five models were constucted and we rigorously evaluated the model performance using various metrics, including the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curve, and decision curve analysis (DCA). For enhanced clinical applicability, we formulated a nomogram that integrates clinical parameters and the rad_score (GTV and PTV). RESULTS: The initial investigation revealed a 33.9% OLM positivity rate in cT1 - 2N0M0 SCLC patients. Our combined model, which incorporates three radiomic features from the GTV and PTV, along with two clinical parameters (smoking status and shape), exhibited robust predictive capabilities. With a peak AUC value of 0.772 in the external validation cohort, the model outperformed the alternative models. The nomogram significantly enhanced diagnostic precision for radiologists and added substantial value to the clinical decision-making process for cT1 - 2N0M0 SCLC patients. CONCLUSIONS: The incidence of OLM in SCLC patients surpassed that in non-small cell lung cancer patients. The combined model demonstrated a notable generalization effect, effectively distinguishing between positive and negative OLMs in a noninvasive manner, thereby guiding individualized clinical decisions for patients with cT1 - 2N0M0 SCLC.

A comparison of machine learning methods for radiomics modeling in prediction of occult lymph node metastasis in clinical stage IA lung adenocarcinoma patients.

Background: Accurate prediction of occult lymph node metastasis (ONM) is an important basis for determining whether lymph node (LN) dissection is necessary in clinical stage IA lung adenocarcinoma patients. The aim of this study is to determine the best machine learning algorithm for radiomics modeling and to compare the performances of the radiomics model, the clinical-radilogical model and the combined model incorporate both radiomics features and clinical-radilogical features in preoperatively predicting ONM in clinical stage IA lung adenocarcinoma patients. Methods: Patients with clinical stage IA lung adenocarcinoma undergoing curative surgery from one institution were retrospectively recruited and assigned to training and test cohorts. Radiomics features were extracted from the preoperative computed tomography (CT) images of the primary tumor. Seven machine learning algorithms were used to construct radiomics models, and the model with the best performance, evaluated using the area under the curve (AUC), was selected. Univariate and multivariate logistic regression analyses were performed on the clinical-radiological features to identify statistically significant features and to develop a clinical model. The optimal radiomics and clinical models were integrated to build a combined model, and its predictive performance was assessed using receiver operating characteristic curves, Brier score, and decision curve analysis (DCA). Results: This study included 258 patients who underwent resection (training cohort, n=182; test cohort, n=76). Six radiomics features were identified. Among the seven machine learning algorithms, extreme gradient boosting (XGB) demonstrated the highest performance for radiomics modeling, with an AUC of 0.917. The combined model improved the AUC to 0.933 and achieved a Brier score of 0.092. DCA revealed that the combined model had optimal clinical efficacy. Conclusions: The superior performance of the combined model, based on XGB algorithm in predicting ONM in patients with clinical stage IA lung adenocarcinoma, might aid surgeons in deciding whether to conduct mediastinal LN dissection and contribute to improve patients' prognosis.

Assessing the Feasibility of Simplifying the Scanning Protocol for Spinal Metastases With Vertebral Compression Fractures Using Only the Dixon T2-Weighted Sequence.

OBJECTIVE: Conventional imaging protocols, including sagittal T1-weighted imaging (T1WI) and water-only T2-weighted imaging (T2WI), are time consuming when screening for spinal metastases with vertebral compression fractures (VCFs). In this study, we aimed to assess the accuracy of using only the Dixon T2-weighted sequence in the diagnosis of spinal metastases with VCFs to determine its suitability as a simplified protocol for this task. METHODS: This retrospective study included 27 patients diagnosed with spinal metastases and VCFs. Qualitative analysis was performed separately by two musculoskeletal radiologists, who independently performed diagnostic evaluations of each vertebra using both conventional and simplified protocols. McNemar's test was then used to compare the differences in diagnostic results, and Cohen's kappa coefficient was used to assess interobserver and interprotocol agreement. Diagnostic performance values for both protocols, including sensitivity, specificity, and area under the curve, were then determined based on the reference standard. Quantitative image analysis was performed randomly for 30 metastases on T1WI and fat-only T2WI to measure the signal intensity, signal-to-noise ratio, and contrast-to-noise ratio. RESULTS: The diagnosis of VCFs by both radiologists was in full agreement with the reference standard. The classification of spinal metastases and diagnostic performance values determined by both radiologists were not significantly different between the two protocols (all P > 0.05), and the consistency between observers and protocols was excellent (κ = 0.973-0.991). The contrast-to-noise ratio of fat-only T2WI was significantly higher than that of T1WI (P < 0.001). CONCLUSIONS: The Dixon T2-weighted sequence alone performed well in diagnosing spinal metastases with VCFs, performing no worse than the conventional protocol (T1WI and water-only T2WI). This suggests that the Dixon T2-weighted sequence alone can serve as a simplified protocol for the diagnosis of spinal metastases with VCFs, thereby avoiding the need for more intricate scanning procedures.

Case report: A 17-year-old male with primary pulmonary osteosarcoma.

Primary pulmonary osteosarcoma is one of the extraskeletal osteosarcomas originating from the lung with an extremely low incidence and highly invasive potential. Here we report a case of primary pulmonary osteosarcoma treated in our hospital with a literature review. The patient, a 17-year-old male, had a cough and hemoptysis for 20 days. Computed tomography (CT) and positron emission tomography (PET)/CT were performed in our hospital. According to pathological examination after surgery, the tumor was diagnosed as a high-grade sarcoma with remarkable osteogenesis and necrosis. Based on radiological and histological examinations, a diagnosis of primary pulmonary osteosarcoma originating was considered. The patient underwent surgery and adjuvant chemotherapy. This patient has been under consecutive follow-up for nearly 8 years, showing no signs of recurrence or distant metastasis. Primary pulmonary osteosarcoma is a rare lung malignancy that shows rapid progression, nonspecific symptoms and inapparent signs at an early stage. The diagnosis of primary pulmonary osteosarcoma highly relies on imaging and histological examinations, among which chest CT is the predominant method to check this disease.

Dual-energy CT-based radiomics in predicting EGFR mutation status non-invasively in lung adenocarcinoma.

Objectives: Patients with epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma (LUAD) can benefit from individualized targeted therapy. This study aims to develop, compare, analyse prediction models based on dual-energy spectral computed tomography (DESCT) and CT-based radiomic features to non-invasively predict EGFR mutation status in LUAD. Materials and methods: Patients with LUAD (n = 175), including 111 patients with and 64 patients without EGFR mutations, were enrolled in the current study. All patients were randomly divided into a training dataset (122 cases) and validation dataset (53 cases) at a ratio of 7:3. After extracting CT-based radiomic, DESCT and clinical features, we built seven prediction models and a nomogram of the best prediction. Receiver operating characteristic (ROC) curves and the mean area under the curve (AUC) values were used for comparisons among the models to obtain the best prediction model for predicting EGFR mutations. Results: The best distinguishing ability is the combined model incorporating radiomic, DESCT and clinical features for predicting the EGFR mutation status with an AUC of 0.86 (95 % CI: 0.79-0.92) in the training group and an AUC value of 0.83 (95 % CI: 0.73, 0.96) in the validation group. Conclusions: Our study provides a predictive nomogram non-invasively with a combination of CT-based radiomic, DESCT and clinical features, which can provide image-based biological information for targeted therapy of LUAD with EGFR mutations.

Predicting pathological complete response of neoadjuvant radiotherapy and targeted therapy for soft tissue sarcoma by whole-tumor texture analysis of multisequence MRI imaging.

OBJECTIVES: To construct effective prediction models for neoadjuvant radiotherapy (RT) and targeted therapy based on whole-tumor texture analysis of multisequence MRI for soft tissue sarcoma (STS) patients. METHODS: Thirty patients with STS of the extremities or trunk from a prospective phase II trial were enrolled for this analysis. All patients underwent pre- and post-neoadjuvant RT MRI examinations from which whole-tumor texture features were extracted, including T1-weighted with fat saturation and contrast enhancement (T1FSGd), T2-weighted with fat saturation (T2FS), and diffusion-weighted imaging (DWI) sequences and their corresponding apparent diffusion coefficient (ADC) maps. According to the postoperative pathological results, the patients were divided into pathological complete response (pCR) and non-pCR (N-pCR) groups. pCR was defined as less than 5% of residual tumor cells by postoperative pathology. Delta features were defined as the percentage change in a texture feature from pre- to post-neoadjuvant RT MRI. After data reduction and feature selection, logistic regression was used to build prediction models. ROC analysis was performed to assess the diagnostic performance. RESULTS: Five of 30 patients (16.7%) achieved pCR. The Delta_Model (AUC 0.92) had a better predictive ability than the Pre_Model (AUC 0.78) and Post_Model (AUC 0.76) and was better than AJCC staging (AUC 0.52) and RECIST 1.1 criteria (AUC 0.52). The Combined_Model (pre, post, and delta features) had the best predictive performance (AUC 0.95). CONCLUSION: Whole-tumor texture analysis of multisequence MRI can well predict pCR status after neoadjuvant RT and targeted therapy in STS patients, with better performance than RECIST 1.1 and AJCC staging. KEY POINTS: • MRI multisequence texture analysis could predict the efficacy of neoadjuvant RT and targeted therapy for STS patients. • Texture features showed incremental value beyond routine clinical factors. • The Combined_Model with features at multiple time points showed the best performance.

The Value of Deep Learning Image Reconstruction in Improving the Quality of Low-Dose Chest CT Images.

This study aimed to evaluate the value of the deep learning image reconstruction (DLIR) algorithm (GE Healthcare's TrueFidelity™) in improving the image quality of low-dose computed tomography (LDCT) of the chest. First, we retrospectively extracted raw data of chest LDCT from 50 patients and reconstructed them by using model-based adaptive statistical iterative reconstruction-Veo at 50% (ASIR-V 50%) and DLIR at medium and high strengths (DLIR-M and DLIR-H). Three sets of images were obtained. Next, two radiographers measured the mean CT value/image signal and standard deviation (SD) in Hounsfield units at the region of interest (ROI) and calculated the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Two radiologists subjectively evaluated the image quality using a 5-point Likert scale. The differences between the groups of data were analyzed through a repeated measures ANOVA or the Friedman test. Last, our result show that the three reconstructions did not differ significantly in signal (p > 0.05) but had significant differences in noise, SNR, and CNR (p < 0.001). The subjective scores significantly differed among the three reconstruction modalities in soft tissue (p < 0.001) but not in lung tissue (p > 0.05). DLIR-H had the best noise reduction ability and improved SNR and CNR without distorting the image texture, followed by DLIR-M and ASIR-V 50%. In summary, DLIR can provide a higher image quality at the same dose, enhancing the physicians' diagnostic confidence and improving the diagnostic efficacy of LDCT for lung cancer screening.

Efficacy evaluation of contrast-enhanced magnetic resonance imaging in differentiating glioma from metastatic tumor of the brain and exploration of its association with patients' neurological function.

Objective: To determine the efficacy of contrast-enhanced MRI in differentiating glioma (GL) from the metastatic tumor of the brain (MTB) and its association with patients' neurological function. Methods: A retrospective analysis was conducted on 49 cases of pathologically confirmed GL and 42 cases of MTB admitted between April 2019 and January 2022. All patients were examined by a set of MRI sequences that included T1WI, T2WI, FLAIR, and DWI. The values of fractional anisotropy (FA), apparent diffusion coefficient (ADC), and operation coefficient (Ktrans) were calculated by taking the tumor parenchyma area, cystic area, and peritumor edema area as the regions of interest (ROIs). And according to the Mini-mental state examination (MMSE) results, the contrast-enhanced MRI with patients' neurological dysfunction was observed. Results: The clinical symptoms and MRI findings of MTB and GL were basically the same, mainly showing neurological symptoms. The tumor parenchyma area and cystic area were mainly located in the tumor periphery and tumor central area, respectively, while the peritumor edema area was widely distributed, showing an irregular patchy edema zone. Contrast-enhanced scans suggested an obvious enhancement in the tumor parenchymal area, presenting with nodular and annular enhancement, but no enhancement in the tumor cystic and peritumor edema areas. There was no difference between GL and MTB in FA values of tumor cystic area and peritumor edema area (P > 0.05), but the FA value of the parenchyma area of GL was higher (P < 0.05). Besides, GL and MTB showed no difference in ADC and Ktrans values (P > 0.05), while the former presented lower ADC values and higher Ktrans values of the peritumor edema area than the latter (P < 0.05). In patients with GL and MTB, the FA and Ktrans values of all ROIs in those with neurological dysfunction were higher compared with those without neurological dysfunction, while the ADC values were lower (P < 0.05). Conclusion: Contrast-enhanced MRI of peritumor edema area can effectively distinguish GL from MTB, and improve the accuracy of early clinical screening, thus providing more reliable life security for patients.

With 3 Types of Respiratory Acquisition: 3.0 T Respiratory Triggered Acquisition Can Obtain Higher Quality DWI Images of the Upper Abdomen.

Objective: To compare the effects of 1.5 T and 3.0 T upper abdominal magnetic resonance diffusion-weighted imaging (DWI) under three acquisition techniques of breath holding, breath triggering, and free breathing, so as to provide a reference for the usage of upper abdominal DWI scanning. Methods: Twenty-one healthy subjects were selected from social volunteers and underwent routine magnetic resonance imaging (MRI) and DWI on 1.5 T and 3.0 T, respectively. DWI included three acquisition methods: breath triggering, breath holding, and free breathing, and b values were 100 and 800. The DWI image artifacts, image quality, apparent diffusion coefficient (ADC), and the signal-to-noise ratio (SNR) obtained through the three acquisition methods were compared. Results: The 1.5 T free-breathing DWI image quality was the best, while the 3.0 T had the best breath-triggered DWI image quality. The 3.0 T breath-triggered DWI image quality was better than the 1.5 T free-breathing DWI image (P=0.012), and the SNR of free-breathing DWI was the highest. Between the two field intensities, the SNR of the liver in the 3.0 T group was much lower than that in the 1.5 T group, and obvious differences were not observed in ADC values of normal liver, gallbladder, kidney, spleen, and pancreas. Conclusion: 3.0 T respiratory-triggered acquisition can obtain higher quality DWI images. But in the case of only 1.5 T field strength, free-breathing acquisition of DWI images should be selected.

Clinical and imaging features of desmoid tumors of the extremities.

BACKGROUND: Desmoid fibroma is a rare soft tissue tumor originating from the aponeurosis, fascia, and muscle, and it is also known as aponeurotic fibroma, invasive fibroma, or ligamentous fibroma. AIM: To investigate the clinical and imaging features of desmoid tumors of the extremities. METHODS: Thirteen patients with desmoid fibroma of the extremities admitted to our hospital from October 2016 to March 2021 were included. All patients underwent computed tomography (CT), magnetic resonance imaging (MRI), and pathological examination of the lesion. Data on the diameter and distribution of the lesion, the relationship between the lesion morphology and surrounding structures, MRI and CT findings, and pathological features were statistically analyzed. RESULTS: The lesion diameter ranged from 1.7 to 8.9 cm, with an average of 5.35 ± 2.39 cm. All lesions were located in the deep muscular space, with the left and right forearm each accounting for 23.08% of cases. Among the 13 patients with desmoid fibroma of the extremities, the lesions were "patchy" in 1 case, irregular in 10, and quasi-round in 2. The boundary between the lesion and surrounding soft tissue was blurred in 10 cases, and the focus infiltrated along the tissue space and invaded the adjacent structures. Furthermore, the edge of the lesion showed "beard-like" infiltration in 2 cases; bone resorption and damage were found in 8, and bending of the bone was present in 2; the boundary of the focus was clear in 1. According to the MRI examination, the lesions were larger than 5 cm (61.54%), round or fusiform in shape (84.62%), had an unclear boundary (76.92%), showed uniform signal (69.23%), inhomogeneous enhancement (84.62%), and "root" or "claw" infiltration (69.23%). Neurovascular tract invasion was present in 30.77% of cases. CT examination showed that the desmoid tumors had slightly a lower density (69.23%), higher enhancement (61.54%), and unclear boundary (84.62%); a CT value < 50 Hu was present in 53.85% of lesions, and the enhancement was uneven in 53.85% of cases. Microscopically, fibroblasts and myofibroblasts were arranged in strands and bundles, without obvious atypia but with occasional karyotyping; cells were surrounded by collagen tissue. There were disparities in the proportion of collagen tissue in different regions, with abundant collagen tissue and few tumor cells in some areas, similar to the structure of aponeuroses or ligaments, and tumor cells invading the surrounding tissues. CONCLUSION: Desmoid tumors of the extremities have certain imaging features on CT and MRI. The two imaging techniques can be combined to improve the diagnostic accuracy, achieve a comprehensive diagnosis of the disease in the clinical practice, and reduce the risk of missed diagnosis or misdiagnosis. In addition, their use can ensure timely diagnosis and treatment.