Predicting recurrent glioblastoma clinical outcome to immune checkpoint inhibition and low-dose bevacizumab with tumor in situ fluid circulating tumor DNA analysis.

OBJECTIVE: Most recurrent glioblastoma (rGBM) patients do not benefit from immune checkpoint inhibition, emphasizing the necessity for response biomarkers. This study evaluates whether tumor in situ fluid (TISF) circulating tumor DNA (ctDNA) could serve as a biomarker for response to low-dose bevacizumab (Bev) plus anti-PD-1 therapy in rGBM patients, aiming to enhance systemic responses to immunotherapy. METHODS: In this phase II trial, 32 GBM patients with first recurrence after standard therapy were enrolled and then received tislelizumab plus low-dose Bev each cycle. TISF samples were analyzed for ctDNA using a 551-gene panel before each treatment. RESULTS: The median progression-free survival (mPFS) and overall survival (mOS) were 8.2 months (95% CI, 5.2-11.1) and 14.3 months (95% CI, 6.5-22.1), respectively. The 12-month OS was 43.8%, and the objective response rate was 56.3%. Patients with more than 20% reduction in the mutant allele fraction and tumor mutational burden after treatment were significantly associated with better prognosis compared to baseline TISF-ctDNA. Among detectable gene mutations, patients with MUC16 mutation, EGFR mutation & amplification, SRSF2 amplification, and H3F3B amplification were significantly associated with worse prognosis. CONCLUSIONS: Low-dose Bev plus anti-PD-1 therapy significantly improves OS in rGBM patients, offering guiding significance for future individualized treatment strategies. TISF-ctDNA can monitor rGBM patients' response to combination therapy and guide treatment. CLINICAL TRIAL REGISTRATION: This trial is registered with ClinicalTrials.gov, NCT05540275.

Tri-Compartmental Restriction Spectrum Imaging Based on 18F-FDG PET/MR for Identification of Primary Benign and Malignant Lung Lesions.

BACKGROUND: Restriction spectrum imaging (RSI), as an advanced quantitative diffusion-weighted magnetic resonance imaging technique, has the potential to distinguish primary benign and malignant lung lesions. OBJECTIVE: To explore how well the tri-compartmental RSI performs in distinguishing primary benign from malignant lung lesions compared with diffusion-weighted imaging (DWI), and to further explore whether positron emission tomography/magnetic resonance imaging (PET/MRI) can improve diagnostic efficacy. STUDY TYPE: Prospective. POPULATION: 137 patients, including 108 malignant and 29 benign lesions (85 males, 52 females; average age = 60.0 ± 10.0 years). FIELD STRENGTH/SEQUENCE: T2WI, T1WI, multi-b value DWI, MR-based attenuation correction, and PET imaging on a 3.0 T whole-body PET/MR system. ASSESSMENT: The apparent diffusion coefficient (ADC), RSI-derived parameters (restricted diffusion f 1 $$ {f}_1 $$ , hindered diffusion f 2 $$ {f}_2 $$ , and free diffusion f 3 $$ {f}_3 $$ ) and the maximum standardized uptake value (SUVmax) were calculated and analyzed for diagnostic efficacy individually or in combination. STATISTICAL TESTS: Student's t-test, Mann-Whitney U test, receiver operating characteristic (ROC) curves, Delong test, Spearman's correlation analysis. P < 0.05 was considered statistically significant. RESULTS: The f 1 $$ {f}_1 $$ , SUVmax were significantly higher, and f 3 $$ {f}_3 $$ , ADC were significantly lower in the malignant group [0.717 ± 0.131, 9.125 (5.753, 13.058), 0.194 ± 0.099, 1.240 (0.972, 1.407)] compared to the benign group [0.504 ± 0.236, 3.390 (1.673, 6.030), 0.398 ± 0.195, 1.485 ± 0.382]. The area under the ROC curve (AUC) values ranked from highest to lowest as follows: AUC (SUVmax) > AUC ( f 3 $$ {f}_3 $$ ) > AUC ( f 1 $$ {f}_1 $$ ) > AUC (ADC) > AUC ( f 2 $$ {f}_2 $$ ) (AUC = 0.819, 0.811, 0.770, 0.745, 0549). The AUC (AUC = 0.900) of the combined model of RSI with PET was significantly higher than that of either single-modality imaging. CONCLUSION: RSI-derived parameters ( f 1 $$ {f}_1 $$ , f 3 $$ {f}_3 $$ ) might help to distinguish primary benign and malignant lung lesions and the discriminatory utility of f 2 $$ {f}_2 $$ was not observed. The RSI exhibits comparable or potentially enhanced performance compared with DWI, and the combined RSI and PET model might improve diagnostic efficacy. TECHNICAL EFFICACY: Stage 2.

Radiomics analysis for prediction of lymph node metastasis after neoadjuvant chemotherapy based on pretreatment MRI in patients with locally advanced cervical cancer.

Background: This study aims to develop and validate a pretreatment MRI-based radiomics model to predict lymph node metastasis (LNM) following neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC). Methods: Patients with LACC who underwent NACT from two centers between 2013 and 2022 were enrolled retrospectively. Based on the lymph node (LN) status determined in the pathology reports after radical hysterectomy, patients were categorized as LN positive or negative. The patients from center 1 were assigned as the training set while those from center 2 formed the validation set. Radiomics features were extracted from pretreatment sagittal T2-weighted imaging (Sag-T2WI), axial diffusion-weighted imaging (Ax-DWI), and the delayed phase of dynamic contrast-enhanced sagittal T1-weighted imaging (Sag-T1C) for each patient. The K-best and least absolute shrinkage and selection operator (LASSO) methods were employed to reduce dimensionality, and the radiomics features strongly associated with LNM were selected and used to construct three single-sequence models. Furthermore, clinical variables were incorporated through multivariate regression analysis and fused with the selected radiomics features to construct the clinical-radiomics combined model. The diagnostic performance of the models was assessed using receiver operating characteristic (ROC) curve analysis. The clinical utility of the models was evaluated by the area under the ROC curve (AUC) and decision curve analysis (DCA). Results: A total of 282 patients were included, comprising 171 patients in the training set, and 111 patients in the validation set. Compared to the Sag-T2WI model (AUC, 95%CI, training set, 0.797, 0.722-0.782; validation set, 0.648, 0.521-0.776) and the Sag-T1C model (AUC, 95%CI, training set, 0.802, 0.723-0.882; validation set, 0.630, 0.505-0.756), the Ax-DWI model exhibited the highest diagnostic performance with AUCs of 0.855 (95%CI, 0.791-0.919) in training set, and 0.753 (95%CI, 0.638-0.867) in validation set, respectively. The combined model, integrating selected features from three sequences and FIGO stage, surpassed predictive ability compared to the single-sequence models, with AUC of 0.889 (95%CI, 0.833-0.945) and 0.859 (95%CI, 0.781-0.936) in the training and validation sets, respectively. Conclusions: The pretreatment MRI-based radiomics model, integrating radiomics features from three sequences and clinical variables, exhibited superior performance in predicting LNM following NACT in patients with LACC.

Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning.

Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging cytological images from 57,220 cases at four tertiary hospitals, we developed a deep-learning method for tumor origin differentiation using cytological histology (TORCH) that can identify malignancy and predict tumor origin in both hydrothorax and ascites. We examined its performance on three internal (n = 12,799) and two external (n = 14,538) testing sets. In both internal and external testing sets, TORCH achieved area under the receiver operating curve values ranging from 0.953 to 0.991 for cancer diagnosis and 0.953 to 0.979 for tumor origin localization. TORCH accurately predicted primary tumor origins, with a top-1 accuracy of 82.6% and top-3 accuracy of 98.9%. Compared with results derived from pathologists, TORCH showed better prediction efficacy (1.677 versus 1.265, P < 0.001), enhancing junior pathologists' diagnostic scores significantly (1.326 versus 1.101, P < 0.001). Patients with CUP whose initial treatment protocol was concordant with TORCH-predicted origins had better overall survival than those who were administrated discordant treatment (27 versus 17 months, P = 0.006). Our study underscores the potential of TORCH as a valuable ancillary tool in clinical practice, although further validation in randomized trials is warranted.

Laser capture microdissection transcriptome (LCM RNA-seq) reveals BcDFR is a key gene in anthocyanin synthesis of non-heading Chinese cabbage.

BACKGROUND: Purple non-heading Chinese cabbage [Brassica campestris (syn. Brassica rapa) ssp. chinensis] has become popular because of its richness in anthocyanin. However, anthocyanin only accumulates in the upper epidermis of leaves. Further studies are needed to investigate the molecular mechanisms underlying the specific accumulation of it. RESULTS: In this study, we used the laser capture frozen section method (LCM) to divide purple (ZBC) and green (LBC) non-heading Chinese cabbage leaves into upper and lower epidermis parts (Pup represents the purple upper epidermis, Plow represents the purple lower epidermis, Gup represents the green upper epidermis, Glow represents the green lower epidermis). Through transcriptome sequencing, we found that the DIHYDROFLAVONOL 4-REDUCTASE-encoding gene BcDFR, is strongly expressed in Pup but hardly in others (Plow, Gup, Glow). Further, a deletion and insertion in the promoter of BcDFR in LBC were found, which may interfere with BcDFR expression. Subsequent analysis of gene structure and conserved structural domains showed that BcDFR is highly conserved in Brassica species. The predicted protein-protein interaction network of BcDFR suggests that it interacts with almost all functional proteins in the anthocyanin biosynthesis pathway. Finally, the results of the tobacco transient expression also demonstrated that BcDFR promotes the synthesis and accumulation of anthocyanin. CONCLUSIONS: BcDFR is specifically highly expressed on the upper epidermis of purple non-heading Chinese cabbage leaves and regulates anthocyanin biosynthesis and accumulation. Our study provides new insights into the functional analysis and transcriptional regulatory network of anthocyanin-related genes in purple non-heading Chinese cabbage.

Clinical study on the prediction of ALN metastasis based on intratumoral and peritumoral DCE-MRI radiomics and clinico-radiological characteristics in breast cancer.

Objective: To investigate the value of predicting axillary lymph node (ALN) metastasis based on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinico-radiological characteristics in breast cancer. Methods: A total of 473 breast cancer patients who underwent preoperative DCE-MRI from Jan 2017 to Dec 2020 were enrolled. These patients were randomly divided into training (n=378) and testing sets (n=95) at 8:2 ratio. Intratumoral regions (ITRs) of interest were manually delineated, and peritumoral regions of 3 mm (3 mmPTRs) were automatically obtained by morphologically dilating the ITR. Radiomics features were extracted, and ALN metastasis-related radiomics features were selected by the Mann-Whitney U test, Z score normalization, variance thresholding, K-best algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. Clinico-radiological risk factors were selected by logistic regression and were also used to construct predictive models combined with radiomics features. Then, 5 models were constructed, including ITR, 3 mmPTR, ITR+3 mmPTR, clinico-radiological and combined (ITR+3 mmPTR+ clinico-radiological) models. The performance of models was assessed by sensitivity, specificity, accuracy, F1 score and area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves and decision curve analysis (DCA). Results: A total of 2264 radiomics features were extracted from each region of interest (ROI), 3 and 10 radiomics features were selected for the ITR and 3 mmPTR, respectively. 5 clinico-radiological risk factors were selected, including lesion size, human epidermal growth factor receptor 2 (HER2) expression, vascular cancer thrombus status, MR-reported ALN status, and time-signal intensity curve (TIC) type. In the testing set, the combined model showed the highest AUC (0.839), specificity (74.2%), accuracy (75.8%) and F1 Score (69.3%) among the 5 models. DCA showed that it had the greatest net clinical benefit compared to the other models. Conclusion: The intra- and peritumoral radiomics models based on DCE-MRI could be used to predict ALN metastasis in breast cancer, especially for the combined model with clinico-radiological characteristics showing promising clinical application value.

Radiomics analysis of intratumoral and different peritumoral regions from multiparametric MRI for evaluating HER2 status of breast cancer: A comparative study.

Purpose: To investigate the potential of radiomics signatures (RSs) from intratumoral and peritumoral regions on multiparametric magnetic resonance imaging (MRI) to noninvasively evaluate HER2 status in breast cancer. Method: In this retrospective study, 992 patients with pathologically confirmed breast cancers who underwent preoperative MRI were enrolled. The breast cancer lesions were segmented manually, and the intratumor region of interest (ROIIntra) was dilated by 2, 4, 6 and 8 mm (ROIPeri2mm, ROIPeri4mm, ROIPeri6mm, and ROIPeri8mm, respectively). Quantitative radiomics features were extracted from dynamic contrast-enhanced T1-weighted imaging (DCE-T1), fat-saturated T2-weighted imaging (T2) and diffusion-weighted imaging (DWI). A three-step procedure was performed for feature selection, and RSs were constructed using a support vector machine (SVM) to predict HER2 status. Result: The best single-area RSs for predicting HER2 status were DCE_Peri4mm-RS, T2_Peri4mm-RS, and DWI_Peri4mm-RS, yielding areas under the curve (AUCs) of 0.716 (95% confidence interval (CI), 0.648-0.778), 0.706 (95% CI, 0.637-0.768), and 0.719 (95% CI, 0.651-0.780), respectively, in the test set. The optimal RSs combining intratumoral and peritumoral regions for evaluating HER2 status were DCE-T1_Intra + DCE_Peri4mm-RS, T2_Intra + T2_Peri6mm-RS and DWI_Intra + DWI_Peri4mm-RS, with AUCs of 0.752 (95% CI, 0.686-0.810), 0.754 (95% CI, 0.688-0.812) and 0.725 (95% CI, 0.657-0.786), respectively, in the test set. Combining three sequences in the ROIIntra, ROIPeri2mm, ROIPeri4mm, ROIPeri6mm and ROIPeri8mm areas, the optimal RS was DCE-T1_Peri4mm + T2_Peri4mm + DWI_Peri4mm-RS, achieving an AUC of 0.795 (95% CI, 0.733-0.849) in the test set. Conclusion: This study systematically explored the influence of the intratumoral region, different peritumoral sizes and their combination in radiomics analysis for predicting HER2 status in breast cancer based on multiparametric MRI and found the optimal RS.

Amide proton transfer-weighted imaging and stretch-exponential model DWI based 18F-FDG PET/MRI for differentiation of benign and malignant solitary pulmonary lesions.

OBJECTIVES: To differentiate benign and malignant solitary pulmonary lesions (SPLs) by amide proton transfer-weighted imaging (APTWI), mono-exponential model DWI (MEM-DWI), stretched exponential model DWI (SEM-DWI), and 18F-FDG PET-derived parameters. METHODS: A total of 120 SPLs patients underwent chest 18F-FDG PET/MRI were enrolled, including 84 in the training set (28 benign and 56 malignant) and 36 in the test set (13 benign and 23 malignant). MTRasym(3.5 ppm), ADC, DDC, α, SUVmax, MTV, and TLG were compared. The area under receiver-operator characteristic curve (AUC) was used to assess diagnostic efficacy. The Logistic regression analysis was used to identify independent predictors and establish prediction model. RESULTS: SUVmax, MTV, TLG, α, and MTRasym(3.5 ppm) values were significantly lower and ADC, DDC values were significantly higher in benign SPLs than malignant SPLs (all P < 0.01). SUVmax, ADC, and MTRasym(3.5 ppm) were independent predictors. Within the training set, the prediction model based on these independent predictors demonstrated optimal diagnostic efficacy (AUC, 0.976; sensitivity, 94.64%; specificity, 92.86%), surpassing any single parameter with statistical significance. Similarly, within the test set, the prediction model exhibited optimal diagnostic efficacy. The calibration curves and DCA revealed that the prediction model not only had good consistency but was also able to provide a significant benefit to the related patients, both in the training and test sets. CONCLUSION: The SUVmax, ADC, and MTRasym(3.5 ppm) were independent predictors for differentiation of benign and malignant SPLs, and the prediction model based on them had an optimal diagnostic efficacy.

Synthesis of gadolinium-enhanced glioma images on multisequence magnetic resonance images using contrastive learning.

BACKGROUND: Gadolinium-based contrast agents are commonly used in brain magnetic resonance imaging (MRI), however, they cannot be used by patients with allergic reactions or poor renal function. For long-term follow-up patients, gadolinium deposition in the body can cause nephrogenic systemic fibrosis and other potential risks. PURPOSE: Developing a new method of enhanced image synthesis based on the advantages of multisequence MRI has important clinical value for these patients. In this paper, an end-to-end synthesis model structure similarity index measure (SSIM)-based Dual Constrastive Learning with Attention (SDACL) based on contrastive learning is proposed to synthesize contrast-enhanced T1 (T1ce) using three unenhanced MRI images of T1, T2, and Flair in patients with glioma. METHODS: The model uses the attention-dilation generator to enlarge the receptive field by expanding the residual blocks and to strengthen the feature representation and context learning of multisequence MRI. To enhance the detail and texture performance of the imaged tumor area, a comprehensive loss function combining patch-level contrast loss and structural similarity loss is created, which can effectively suppress noise and ensure the consistency of synthesized images and real images. RESULTS: The normalized root-mean-square error (NRMSE), peak signal-to-noise ratio (PSNR), and SSIM of the model on the independent test set are 0.307  ± $\pm$  0.12, 23.337  ± $\pm$  3.21, and 0.881  ± $\pm$  0.05, respectively. CONCLUSIONS: Results show this method can be used for the multisequence synthesis of T1ce images, which can provide valuable information for clinical diagnosis.

The role of dynamic, static, and delayed total-body PET imaging in the detection and differential diagnosis of oncological lesions.

OBJECTIVES: Commercialized total-body PET scanners can provide high-quality images due to its ultra-high sensitivity. We compared the dynamic, regular static, and delayed 18F-fluorodeoxyglucose (FDG) scans to detect lesions in oncologic patients on a total-body PET/CT scanner. MATERIALS & METHODS: In all, 45 patients were scanned continuously for the first 60 min, followed by a delayed acquisition. FDG metabolic rate was calculated from dynamic data using full compartmental modeling, whereas regular static and delayed SUV images were obtained approximately 60- and 145-min post-injection, respectively. The retention index was computed from static and delayed measures for all lesions. Pearson's correlation and Kruskal-Wallis tests were used to compare parameters. RESULTS: The number of lesions was largely identical between the three protocols, except MRFDG and delayed images on total-body PET only detected 4 and 2 more lesions, respectively (85 total). FDG metabolic rate (MRFDG) image-derived contrast-to-noise ratio and target-to-background ratio were significantly higher than those from static standardized uptake value (SUV) images (P < 0.01), but this is not the case for the delayed images (P > 0.05). Dynamic protocol did not significantly differentiate between benign and malignant lesions just like regular SUV, delayed SUV, and retention index. CONCLUSION: The potential quantitative advantages of dynamic imaging may not improve lesion detection and differential diagnosis significantly on a total-body PET/CT scanner. The same conclusion applied to delayed imaging. This suggested the added benefits of complex imaging protocols must be weighed against the complex implementation in the future. CLINICAL RELEVANCE: Total-body PET/CT was known to significantly improve the PET image quality due to its ultra-high sensitivity. However, whether the dynamic and delay imaging on total-body scanner could show additional clinical benefits is largely unknown. Head-to-head comparison between two protocols is relevant to oncological management.

The prognostic value of radiological and pathological lymph node status in patients with cervical cancer who underwent neoadjuvant chemotherapy and followed hysterectomy.

To investigate the prognostic value of lymph node status in patients with cervical cancer (CC) patients who underwent neoadjuvant chemotherapy (NACT) and followed hysterectomy. Patients in two referral centers were retrospectively analyzed. The baseline tumor size and radiological lymph node status (LNr) were evaluated on pre-NACT MRI. Tumor histology, differentiation and pathological lymph node status (LNp) were obtained from post-operative specimen. The log-rank test was used to compare survival between patient groups. Cox proportional hazards regression models were employed to estimate the hazard ratio (HR) of various factors with progression-free survival (PFS) and overall survival (OS). A total of 266 patients were included. Patients with 2018 FIGO IIIC showed worse PFS compared to those with FIGO IB-IIB (p < 0.001). The response rate in patients with LNp(-) was 64.1% (134/209), significantly higher than that of 45.6% (26/57) in patients with LNp( +) (p = 0.011). Multivariate Cox analysis identified the main independent predictors of PFS as LNp( +) (HR = 3.777; 95% CI 1.715-8.319), non-SCC (HR = 2.956; 95% CI 1.297-6.736), poor differentiation (HR = 2.370; 95% CI 1.130-4.970) and adjuvant radiation (HR = 3.266; 95% CI 1.183-9.019). The interaction between LNr and LNp regarding PFS were significant both for univariate and multivariate (P = 0.000171 and 1.5357e-7 respectively). In patients with LNr( +), a significant difference in PFS was observed between patients with LNp(-) and LNp( +) (p = 0.0027). CC patients with FIGO 2018 stage IIIC who underwent NACT and followed hysterectomy had worse PFS compared to those with IB-IIB. LNp( +), non-SCC, poor differentiation and adjuvant radiation were independent risk factors for PFS. The adverse prognostic value of LNp( +) was more significant in patients with LNr( +).

Prognostic Value of 18F-FDG PET/CT Radiomics in Extranodal Nasal-Type NK/T Cell Lymphoma.

OBJECTIVE: To investigate the prognostic utility of radiomics features extracted from 18F-fluorodeoxyglucose (FDG) PET/CT combined with clinical factors and metabolic parameters in predicting progression-free survival (PFS) and overall survival (OS) in individuals diagnosed with extranodal nasal-type NK/T cell lymphoma (ENKTCL). MATERIALS AND METHODS: A total of 126 adults with ENKTCL who underwent 18F-FDG PET/CT examination before treatment were retrospectively included and randomly divided into training (n = 88) and validation cohorts (n = 38) at a ratio of 7:3. Least absolute shrinkage and selection operation Cox regression analysis was used to select the best radiomics features and calculate each patient's radiomics scores (RadPFS and RadOS). Kaplan-Meier curve and Log-rank test were used to compare survival between patient groups risk-stratified by the radiomics scores. Various models to predict PFS and OS were constructed, including clinical, metabolic, clinical + metabolic, and clinical + metabolic + radiomics models. The discriminative ability of each model was evaluated using Harrell's C index. The performance of each model in predicting PFS and OS for 1-, 3-, and 5-years was evaluated using the time-dependent receiver operating characteristic (ROC) curve. RESULTS: Kaplan-Meier curve analysis demonstrated that the radiomics scores effectively identified high- and low-risk patients (all P < 0.05). Multivariable Cox analysis showed that the Ann Arbor stage, maximum standardized uptake value (SUVmax), and RadPFS were independent risk factors associated with PFS. Further, ß2-microglobulin, Eastern Cooperative Oncology Group performance status score, SUVmax, and RadOS were independent risk factors for OS. The clinical + metabolic + radiomics model exhibited the greatest discriminative ability for both PFS (Harrell's C-index: 0.805 in the validation cohort) and OS (Harrell's C-index: 0.833 in the validation cohort). The time-dependent ROC analysis indicated that the clinical + metabolic + radiomics model had the best predictive performance. CONCLUSION: The PET/CT-based clinical + metabolic + radiomics model can enhance prognostication among patients with ENKTCL and may be a non-invasive and efficient risk stratification tool for clinical practice.

Performance of node reporting and data system (node-RADS): a preliminary study in cervical cancer.

BACKGROUND: Node Reporting and Data System (Node-RADS) was proposed and can be applied to lymph nodes (LNs) across all anatomical sites. This study aimed to investigate the diagnostic performance of Node-RADS in cervical cancer patients. METHODS: A total of 81 cervical cancer patients treated with radical hysterectomy and LN dissection were retrospectively enrolled. Node-RADS evaluations were performed by two radiologists on preoperative MRI scans for all patients, both at the LN level and patient level. Chi-square and Fisher's exact tests were employed to evaluate the distribution differences in size and configuration between patients with and without LN metastasis (LNM) in various regions. The receiver operating characteristic (ROC) and the area under the curve (AUC) were used to explore the diagnostic performance of the Node-RADS score for LNM. RESULTS: The rates of LNM in the para-aortic, common iliac, internal iliac, external iliac, and inguinal regions were 7.4%, 9.3%, 19.8%, 21.0%, and 2.5%, respectively. At the patient level, as the NODE-RADS score increased, the rate of LNM also increased, with rates of 26.1%, 29.2%, 42.9%, 80.0%, and 90.9% for Node-RADS scores 1, 2, 3, 4, and 5, respectively. At the patient level, the AUCs for Node-RADS scores > 1, >2, > 3, and > 4 were 0.632, 0.752, 0.763, and 0.726, respectively. Both at the patient level and LN level, a Node-RADS score > 3 could be considered the optimal cut-off value with the best AUC and accuracy. CONCLUSIONS: Node-RADS is effective in predicting LNM for scores 4 to 5. However, the proportions of LNM were more than 25% at the patient level for scores 1 and 2, which does not align with the expected very low and low probability of LNM for these scores.

An [18F]FDG PET/3D-ultrashort echo time MRI-based radiomics model established by machine learning facilitates preoperative assessment of lymph node status in non-small cell lung cancer.

OBJECTIVES: To develop an [18F]FDG PET/3D-UTE model based on clinical factors, three-dimensional ultrashort echo time (3D-UTE), and PET radiomics features via machine learning for the assessment of lymph node (LN) status in non-small cell lung cancer (NSCLC). METHODS: A total of 145 NSCLC patients (training, 101 cases; test, 44 cases) underwent whole-body [18F]FDG PET/CT and chest [18F]FDG PET/MRI were enrolled. Preoperative clinical factors and 3D-UTE, CT, and PET radiomics features were analyzed. The Mann-Whitney U test, LASSO regression, and SelectKBest were used for feature extraction. Five machine learning algorithms were used to establish prediction models, which were evaluated by the area under receiver-operator characteristic (ROC), DeLong test, calibration curves, and decision curve analysis (DCA). RESULTS: A prediction model based on random forest, consisting of four clinical factors, six 3D-UTE, and six PET radiomics features, was used as the final model for PET/3D-UTE. The AUCs of this model were 0.912 and 0.791 in the training and test sets, respectively, which not only showed different degrees of improvement over individual models such as clinical, 3D-UTE, and PET (AUC-training = 0.838, 0.834, and 0.828, AUC-test = 0.756, 0.745, and 0.768, respectively) but also achieved the similar diagnostic efficacy as the optimal PET/CT model (AUC-training = 0.890, AUC-test = 0.793). The calibration curves and DCA indicated good consistency (C-index, 0.912) and clinical utility of this model, respectively. CONCLUSION: The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using machine learning methods could noninvasively assess the LN status of NSCLC. CLINICAL RELEVANCE STATEMENT: A machine learning model of 18F-fluorodeoxyglucose positron emission tomography/ three-dimensional ultrashort echo time could noninvasively assess the lymph node status of non-small cell lung cancer, which provides a novel method with less radiation burden for clinical practice. KEY POINTS: • The 3D-UTE radiomics model using the PLS-DA classifier was significantly associated with LN status in NSCLC and has similar diagnostic performance as the clinical, CT, and PET models. • The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using the RF classifier could noninvasively assess the LN status of NSCLC and showed improved diagnostic performance compared to the clinical, 3D-UTE, and PET models. • In the assessment of LN status in NSCLC, the [18F]FDG PET/3D-UTE model has similar diagnostic efficacy as the [18F]FDG PET/CT model that incorporates clinical factors and CT and PET radiomics features.

Blood-labyrinth barrier breakdown in Meniere's disease.

OBJECTIVE: We compared the signal intensity ratio (SIR) of the cochlear basal turn between Meniere's disease and healthy controls to investigate potential damage of the blood-labyrinth barrier in Meniere's disease. METHODS: Thirty patients diagnosed with unilateral definite Meniere's disease and 24 healthy controls were enrolled. 3D-FLAIR scan was conducted to assess the grades of endolymphatic hydrops in Meniere's patients while measuring the SIR of cochlear basal turns in both groups. The differences of bilateral SIR between Meniere's disease and healthy control were compared, and the correlation between the SIR on affected ear in Meniere's disease and the grades of cochlear and vestibular hydrops were analyzed. RESULTS: SIR of affected ear in Meniere's disease exhibited significant increase compared to that of unaffected ear. No significant difference was observed in SIR between the two ears in the healthy control. Furthermore, the SIR of unaffected side in Meniere's disease was higher than that of both ears in healthy controls. The SIR in affected ear of Meniere's disease exhibited positive correlation with hydrops in both cochlea and vestibula. CONCLUSION: The permeability of blood-labyrinth barrier is increased in Meniere's disease, in combination with the typical criteria of Meniere's disease it may be a good biological marker. Destruction of blood-labyrinth barrier may be one of the causes of endolymphatic hydrops in Meniere's disease.

Amide Proton Transfer-Weighted Imaging and Multiple Models Intravoxel Incoherent Motion-Based 18 F-FDG PET/MRI for Predicting Progression-Free Survival in Non-Small Cell Lung Cancer.

BACKGROUND: Amide proton transfer-weighted imaging (APTWI) and multiple models intravoxel incoherent motion (IVIM) based 18 F-FDG PET/MR could reflect the microscopic information of the tumor from multiple perspectives. However, its value in the prognostic assessment of non-small cell lung cancer (NSCLC) still needs to be further explored. PURPOSE: To determine whether pretreatment APTWI, mono-, bi-, and stretched-exponential model IVIM, and 18 F-FDG PET-derived parameters of the primary lesion may be associated with progression-free survival (PFS) in NSCLC. STUDY TYPE: Prospective. POPULATION: Seventy-seven patients (mean age, 62 years, range, 20-81 years) with 37 men and 40 women were included. FIELD STRENGTH/SEQUENCE: 3.0 T 18 F-FDG PET/MRI, single shot echo planar imaging sequences for IVIM and fast spin-echo sequences with magnetization transfer pulses for APTWI. ASSESSMENT: Patient clinical characteristics (age, sex, smoke, subtype, TNM stage, and surgery), PFS (chest CT every 3 months, median follow-up was 18 months, range, 4-27 months), and APTWI (MTRasym(3.5 ppm)), IVIM (ADCstand , D, D*, f, DDC, and α), and 18 F-FDG PET (SUVmax , MTV, and TLG) parameters were recorded. STATISTICAL TESTS: Proportional hazards model, concordance index, calibration curve, decision curve analysis (DCA), and Log-rank test. A P value <0.05 was considered statistically significant. RESULTS: Histological subtype, TNM stage, MTV, D*, and MTRasym(3.5 ppm) were all independent predictors of PFS. A prediction model based on these predictors was developed with a C-index of 0.895 (95% CI: 0.839-0.951), which was significantly superior to each of the above predictors alone (C-index = 0.629, 0.707, 0.692, 0.678, and 0.558, respectively). The calibration curve and DCA indicated good consistency and clinical utility of the prediction model, respectively. Log-rank test results showed a significant difference in PFS between the high- and low-risk groups. DATA CONCLUSION: APTWI and multiple models IVIM based 18 F-FDG PET/MRI can be used for PFS assessment in NSCLC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Quantitative parameters of static imaging and fast kinetics imaging in 18F-FDG total-body PET/CT for the assessment of histological feature of pulmonary lesions.

Background: To investigate the value of quantitative parameters related to static imaging and fast kinetics imaging of total-body (TB) 2-[18F]-fluoro-2-deoxy-D-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) in differentiating benign from malignant pulmonary lesions and squamous cell carcinoma (SCC) from adenocarcinoma (AC) and to analyze the correlation of each parameter with the Ki-67 index. Methods: A total of 108 patients with pulmonary lesions from July 2021 to May 2022 in the Henan Provincial People's Hospital, China, were consecutively recruited for TB 18F-FDG PET/CT in this prospective study. Static imaging parameters maximum standardized uptake value (SUVmax) and fast kinetics imaging parameters transport constant (K1), rate constants (k2), time delay (td), and fractional blood volume (vb) were calculated and compared. The area under the receiver operating characteristic (ROC) curve (AUC), Delong test, Logistic regression analyses, and Pearson correlation were used to assess diagnostic efficacy, find independent predictors and analyse correlations respectively. Results: Malignant lesions had higher SUVmax and K1 and lower vb than benign lesions, and SCC had higher SUVmax and K1 and lower td and vb than AC (all P<0.05). For the differentiation of benign and malignant lesions, SUVmax, K1, and vb were independent predictors, and AUC (SUVmax + K1+ vb) =0.909 (95% CI: 0.839-0.956), AUC (SUVmax) =0.883 (95% CI: 0.807-0.937), AUC (K1) =0.810 (95% CI: 0.723-0.879), and AUC (vb) =0.746 (95% CI: 0.653-0.825), where AUC (SUVmax + K1+ vb) was significantly different from AUC (K1), AUC (vb) (Z=3.006, 3.965, all P<0.05). For the differentiation of SCC and AC, SUVmax, K1, td, and vb were independent predictors, and AUC (SUVmax + K1+ td + vb) =0.946 (95% CI: 0.840-0.991), AUC (SUVmax) =0.818 (95% CI: 0.680-0.914), AUC (K1) =0.770 (95% CI: 0.626-0.879), AUC (vb) =0.737 (95% CI: 0.590-0.853), and AUC (td) =0.669 (95% CI: 0.510-0.791), where AUC (SUVmax + K1+ td + vb) was significantly different from AUC (SUVmax), AUC (K1), AUC (vb), and AUC (td) (Z=2.269, 2.821, 2.848, and 3.276, all P<0.05). SUVmax and K1 were moderately and mildly positively correlated with the Ki-67 index (r=0.541, 0.452, all P<0.05), respectively. Conclusions: Quantitative parameters of static imaging and fast kinetics imaging in 18F-FDG total-body PET/CT can be used to differentiate benign from malignant pulmonary lesions and SCC from AC and to assess Ki-67 expression.

Genomic alterations of oligodendrogliomas at distant recurrence.

BACKGROUND: Oligodendroglioma is known for its relatively better prognosis and responsiveness to radiotherapy and chemotherapy. However, little is known about the evolution of genetic changes as oligodendroglioma progresses. METHODS: In this study, we evaluated gene evolution invivo during tumor progression based on deep whole-genome sequencing data (ctDNA). We analyzed longitudinal genomic data from six patients during tumor evolution, of which five patients developed distant recurrence. RESULTS: Whole-exome sequencing demonstrated that the rate of shared mutations between the primary and recurrent samples was relatively low. In two cases, even well-known major driver mutations in CIC and FUBP1 that were detected in primary tumors were not detected in the relapse samples. Among these cases, two patients had a conversion from the IDH mutation in the originating state to the IDH1 wild state during the process of gene evolution under chemotherapy treatment, indicating that the cell phenotype and genetic characteristics of oligodendroglioma may change during tumor evolution. Two patients received long-term temozolomide (TMZ) treatment before the operation, and we found that recurrence tumors harbored mutations in the PI3K/AKT and Sonic hedgehog (SHh) signaling pathways. Hypermutation occurred with mutations in MMR genes in one patient, contributing to the rapid progression of the tumor. CONCLUSION: Oligodendroglioma displayed great spatial and temporal heterogeneity during tumor evolution. The PI3K/AKT and SHh signaling pathways may play an important role in promoting treatment resistance and distant relapse during oligodendroglioma evolution. In addition, there was a tendency to increase the degree of tumor malignancy during evolution. Distant recurrence may be a later event duringoligodendroglioma progression. CLINICALTRIALS: gov, Identifier: NCT05512325.

Preoperative prediction of lymphovascular invasion of colorectal cancer by radiomics based on 18F-FDG PET-CT and clinical factors.

Purpose: The purpose of this study was to investigate the value of a clinical radiomics model based on Positron emission tomography-computed tomography (PET-CT) radiomics features combined with clinical predictors of Lymphovascular invasion (LVI) in predicting preoperative LVI in patients with colorectal cancer (CRC). Methods: A total of 95 CRC patients who underwent preoperative 18F-fluorodeoxyglucose (FDG) PET-CT examination were retrospectively enrolled. Univariate and multivariate logistic regression analyses were used to analyse clinical factors and PET metabolic data in the LVI-positive and LVI-negative groups to identify independent predictors of LVI. We constructed four prediction models based on radiomics features and clinical data to predict LVI status. The predictive efficacy of different models was evaluated according to the receiver operating characteristic curve. Then, the nomogram of the best model was constructed, and its performance was evaluated using calibration and clinical decision curves. Results: Mean standardized uptake value (SUVmean), maximum tumour diameter and lymph node metastasis were independent predictors of LVI in CRC patients (P < 0.05). The clinical radiomics model obtained the best prediction performance, with an Area Under Curve (AUC) of 0.922 (95%CI 0.820-0.977) and 0.918 (95%CI 0.782-0.982) in the training and validation cohorts, respectively. A nomogram based on the clinical radiomics model was constructed, and the calibration curve fitted well (P > 0.05). Conclusion: The clinical radiomics prediction model constructed in this study has high value in the preoperative individualized prediction of LVI in CRC patients.

Molecular and clonal evolution in vivo reveal a common pathway of distant relapse gliomas.

The evolutionary trajectories of genomic alterations underlying distant recurrence in glioma remain largely unknown. To elucidate glioma evolution, we analyzed the evolutionary trajectories of matched pairs of primary tumors and relapse tumors or tumor in situ fluid (TISF) based on deep whole-genome sequencing data (ctDNA). We found that MMR gene mutations occurred in the late stage in IDH-mutant glioma during gene evolution, which activates multiple signaling pathways and significantly increases distant recurrence potential. The proneural subtype characterized by PDGFRA amplification was likely prone to hypermutation and distant recurrence following treatment. The classical and mesenchymal subtypes tended to progress locally through subclonal reconstruction, trunk genes transformation, and convergence evolution. EGFR and NOTCH signaling pathways and CDNK2A mutation play an important role in promoting tumor local progression. Glioma subtypes displayed distinct preferred evolutionary patterns. ClinicalTrials.gov, NCT05512325.