Non-intestinal-type adenocarcinoma of the nasal cavity and paranasal sinuses: an analysis of clinical characteristics and prognosis.

BACKGROUND: Non­intestinal adenocarcinoma of the nasal cavity and paranasal sinuses (non­ITAC) is a heterogeneous tumour that has rarely been reported in previous studies. We compared and analysed the symptoms, radiographic and pathological features, treatment methods, and prognosis of patients with low-grade (G1) and high-grade (G3) tumours. METHODS: This was a retrospective study included 22 patients with pathologically confirmed non-ITAC of the nasal cavity and paranasal sinuses who were treated between January 2008 and December 2021 at a single centre. Of these, 11 patients had G1 tumours, and 11 patients had G3 tumours. Clinicopathological features, treatment methods, and survival outcomes were analysed. RESULTS: The median follow-up period was 48.5 months. Nasal congestion was the most common initial symptom, and the nasal cavity was the most frequently involved site. For G1 tumours, the main treatment was simple surgery, 1 and 3­year overall survival (OS) rates were 100 and 88.9%, while the 1 and 3­year local control (LC) rates were 100 and 100%, respectively. For G3 tumours, the main treatments were surgery combined with radiotherapy and/or chemotherapy,1 and 3­year OS rates were 72.7 and 72.7%, while the 1 and 3­year LC rates were 100 and 90.91%, respectively. G3 tumours was associated with significantly shorter overall survival than G1 tumours (P = 0.035). Patients with stage III-IV showed shorter overall survival compared to stage I-II patients (P = 0.035). CONCLUSIONS: Non-ITAC of the nasal cavity and paranasal sinuses may frequently occur in the nasal cavity. The main treatment modality is surgery, supplemented by radiotherapy and chemotherapy. Pathological grade and tumour stage were poor prognostic factors for the disease.

Extracellular volume-based scoring system for tracking tumor progression in pancreatic cancer patients receiving intraoperative radiotherapy.

OBJECTIVES: To investigate the value of extracellular volume (ECV) derived from portal-venous phase (PVP) in predicting prognosis in locally advanced pancreatic cancer (LAPC) patients receiving intraoperative radiotherapy (IORT) with initial stable disease (SD) and to construct a risk-scoring system based on ECV and clinical-radiological features. MATERIALS AND METHODS: One hundred and three patients with LAPC who received IORT demonstrating SD were enrolled and underwent multiphasic contrast-enhanced CT (CECT) before and after IORT. ECV maps were generated from unenhanced and PVP CT images. Clinical and CT imaging features were analyzed. The independent predictors of progression-free survival (PFS) determined by multivariate Cox regression model were used to construct the risk-scoring system. Time-dependent receiver operating characteristic (ROC) curve analysis and the Kaplan-Meier method were used to evaluate the predictive performance of the scoring system. RESULTS: Multivariable analysis revealed that ECV, rim-enhancement, peripancreatic fat infiltration, and carbohydrate antigen 19-9 (CA19-9) response were significant predictors of PFS (all p < 0.05). Time-dependent ROC of the risk-scoring system showed a satisfactory predictive performance for disease progression with area under the curve (AUC) all above 0.70. High-risk patients (risk score ≥ 2) progress significantly faster than low-risk patients (risk score < 2) (p < 0.001). CONCLUSION: ECV derived from PVP of conventional CECT was an independent predictor for progression in LAPC patients assessed as SD after IORT. The scoring system integrating ECV, radiological features, and CA19-9 response can be used as a practical tool for stratifying prognosis in these patients, assisting clinicians in developing an appropriate treatment approach. CRITICAL RELEVANCE STATEMENT: The scoring system integrating ECV fraction, radiological features, and CA19-9 response can track tumor progression in patients with LAPC receiving IORT, aiding clinicians in choosing individual treatment strategies and improving their prognosis. KEY POINTS: Predicting the progression of LAPC in patients receiving IORT is important. Our ECV-based scoring system can risk stratifying patients with initial SD. Appropriate prognostication can assist clinicians in developing appropriate treatment approaches.

Bladder MRI with deep learning-based reconstruction: a prospective evaluation of muscle invasiveness using VI-RADS.

PURPOSE: To investigate the influence of deep learning reconstruction (DLR) on bladder MRI, specifically examination time, image quality, and diagnostic performance of vesical imaging reporting and data system (VI-RADS) within a prospective clinical cohort. METHODS: Seventy participants with bladder cancer who underwent MRI between August 2022 and February 2023 with a protocol containing standard T2-weighted imaging (T2WIS), standard diffusion-weighted imaging (DWIS), fast T2WI with DLR (T2WIDL), and fast DWI with DLR (DWIDL) were enrolled in this prospective study. Imaging quality was evaluated by measuring signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and qualitative image quality scoring. Additionally, the apparent diffusion coefficient (ADC) of bladder lesions derived from DWIS and DWIDL was measured and VI-RADS scoring was performed. Paired t-test or paired Wilcoxon signed-rank test were performed to compare image quality score, SNR, CNR, and ADC between standard sequences and fast sequences with DLR. The diagnostic performance for VI-RADS was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS: Compared to T2WIS and DWIS, T2WIDL and DWIDL reduced the acquisition time from 5:57 min to 3:13 min and showed significantly higher SNR, CNR, qualitative image quality score of overall image quality, image sharpness, and lesion conspicuity. There were no significant differences in ADC and AUC of VI-RADS between standard sequences and fast sequences with DLR. CONCLUSIONS: The application of DLR to T2WI and DWI reduced examination time and significantly improved image quality, maintaining ADC and the diagnostic performance of VI-RADS for evaluating muscle invasion in bladder cancer.

MRI evaluation of vesical imaging reporting and data system for bladder cancer after neoadjuvant chemotherapy.

BACKGROUND: The Vesical Imaging-Reporting and Data System (VI-RADS) has demonstrated effectiveness in predicting muscle invasion in bladder cancer before treatment. The urgent need currently is to evaluate the muscle invasion status after neoadjuvant chemotherapy (NAC) for bladder cancer. This study aims to ascertain the accuracy of VI-RADS in detecting muscle invasion post-NAC treatment and assess its diagnostic performance across readers with varying experience levels. METHODS: In this retrospective study, patients with muscle-invasive bladder cancer who underwent magnetic resonance imaging (MRI) after NAC from September 2015 to September 2018 were included. VI-RADS scores were independently assessed by five radiologists, consisting of three experienced in bladder MRI and two inexperienced radiologists. Comparison of VI-RADS scores was made with postoperative histopathological diagnosis. Receiver operating characteristic curve analysis (ROC) was used for evaluating diagnostic performance, calculating sensitivity, specificity, and area under ROC (AUC)). Interobserver agreement was assessed using the weighted kappa statistic. RESULTS: The final analysis included 46 patients (mean age: 61 years ± 9 [standard deviation]; age range: 39-70 years; 42 men). The pooled AUC for predicting muscle invasion was 0.945 (95% confidence interval (CI): 0.893-0.977) for experienced readers, and 0.910 (95% CI: 0.831-0.959) for inexperienced readers, and 0.932 (95% CI: 0.892-0.961) for all readers. At an optimal cut-off value ≥ 4, pooled sensitivity and specificity were 74.1% (range: 66.0-80.9%) and 94.1% (range: 88.6-97.7%) for experienced readers, and 63.9% (range: 59.6-68.1%) and 86.4% (range: 84.1-88.6%) for inexperienced readers. Interobserver agreement ranged from substantial to excellent between all readers (k = 0.79-0.92). CONCLUSIONS: VI-RADS accurately assesses muscle invasion in bladder cancer patients after NAC and exhibits good diagnostic performance across readers with different experience levels.

Comparison of 68Ga-FAPI-04 PET/CT with 18F-FDG PET/CT for diagnosis and staging of gastric and colorectal cancer.

OBJECTIVE: The objective of this study is to evaluate the effectiveness of 68Ga-FAPI-04 PET/computed tomography (CT) for the diagnosis of primary and metastatic gastric cancer and colorectal cancer lesions as compared with 18F-FDG PET/CT. MATERIALS AND METHODS: Fifty-nine patients who underwent both 18F-FDG and 68Ga-FAPI-04 for initial staging or restaging were enrolled. Histopathological findings and clinical imaging follow-up were used as the reference standard. The diagnostic performance and TNM staging of the two tracers were calculated and compared. The maximum standardized uptake value (SUVmax), tumour-to-mediastinal blood pool ratio (TBR) (lesions SUVmax/ascending aorta SUVmean), and tumour-to-normal liver parenchyma ratio (TLR) (lesions SUVmax/liver SUVmean) of primary and metastatic lesions between two imaging modalities were measured and compared using the Wilcoxon signed-rank test and paired t-test. RESULTS: The two imaging agents are comparable for the detection of primary tumors. The sensitivity of 68Ga-FAPI-04 PET/CT was higher than that of 18F-FDG PET/CT for detecting lymph node metastases, peritoneal metastases, liver metastases, and bone metastases. In the patient-based analysis, the TLR for all lesions was significantly higher with 68Ga-FAPI-04 PET/CT than with 18F-FDG PET/CT (all P < 0.05). The accuracy (92.2 vs. 70.3%, P = 0.002) and sensitivity of 68Ga-FAPI-04 were significantly higher than that of 18F-FDG (78.6 vs. 71.4%, P = 0.011) in determining the lymph node status. 68Ga-FAPI-04 has a higher accuracy in staging (P = 0.041), which is mainly due to the ability of distant metastases detection. CONCLUSION: 68Ga-FAPI-04 PET/CT may be superior in evaluating the diagnostic efficiency and staging accuracy of gastric and colorectal cancer.

Prognostic performance of MRI LI-RADS version 2018 features and clinical-pathological factors in alpha-fetoprotein-negative hepatocellular carcinoma.

PURPOSE: To evaluate the role of the magnetic resonance imaging (MRI) Liver Imaging Reporting and Data System (LI-RADS) version 2018 features and clinical-pathological factors for predicting the prognosis of alpha-fetoprotein (AFP)-negative (≤ 20 ng/ml) hepatocellular carcinoma (HCC) patients, and to compare with other traditional staging systems. METHODS: We retrospectively enrolled 169 patients with AFP-negative HCC who received preoperative MRI and hepatectomy between January 2015 and August 2020 (derivation dataset:validation dataset = 118:51). A prognostic model was constructed using the risk factors identified via Cox regression analysis. Predictive performance and discrimination capability were evaluated and compared with those of two traditional staging systems. RESULTS: Six risk factors, namely the LI-RADS category, blood products in mass, microvascular invasion, tumor size, cirrhosis, and albumin-bilirubin grade, were associated with recurrence-free survival. The prognostic model constructed using these factors achieved C-index of 0.705 and 0.674 in the derivation and validation datasets, respectively. Furthermore, the model performed better in predicting patient prognosis than traditional staging systems. The model effectively stratified patients with AFP-negative HCC into high- and low-risk groups with significantly different outcomes (p < 0.05). CONCLUSION: A prognostic model integrating the LI-RADS category, blood products in mass, microvascular invasion, tumor size, cirrhosis, and albumin-bilirubin grade may serve as a valuable tool for refining risk stratification in patients with AFP-negative HCC.

Molecular imprinting-based triple-emission ratiometric fluorescence sensor with aggregation-induced emission effect for visual detection of doxycycline.

The development of high-performance sensors for doxycycline (DOX) detection is necessary because its residue accumulation will cause serious harm to human health and the environment. Here, a novel tri-emission ratiometric fluorescence sensor was proposed by using "post-mixing" strategy of different emissions fluorescence molecularly imprinted polymers with salicylamide as dummy template (DMIPs). BSA was chosen as assistant functional monomer, and also acted as sensitizers for the aggregation-induced emission (AIE) effect of DOX. The blue-emitting carbon dots and the red-emitting CdTe quantum dots were separately introduced into DMIPs as the response signals. Upon DOX recognition within 2 min, blue and red fluorescence of the tri-emission DMIPs sensor were quenched while green fluorescence of DOX was enhanced, resulting in a wide range of color variations observed over bluish violet-rosered-light pink-orange-yellow-green with a detection limit of 0.061 µM. The sensor possessed highly selective recognition and was successfully applied to detect DOX in complicated real samples. Moreover, with the fluorescent color collection and data processing, the smartphone-assisted visual detection of the sensors showed satisfied sensitivity with low detection limit. This work provides great potential applications for rapid and visual detection of antibiotics in complex substrates.

circNOX4 activates an inflammatory fibroblast niche to promote tumor growth and metastasis in NSCLC via FAP/IL-6 axis.

BACKGROUND: Cancer-associated fibroblasts (CAFs) orchestrate a supportive niche that fuels cancer metastatic development in non-small cell lung cancer (NSCLC). Due to the heterogeneity and plasticity of CAFs, manipulating the activated phenotype of fibroblasts is a promising strategy for cancer therapy. However, the underlying mechanisms of fibroblast activation and phenotype switching that drive metastasis remain elusive. METHODS: The clinical implications of fibroblast activation protein (FAP)-positive CAFs (FAP+CAFs) were evaluated based on tumor specimens from NSCLC patients and bioinformatic analysis of online databases. CAF-specific circular RNAs (circRNAs) were screened by circRNA microarrays of primary human CAFs and matched normal fibroblasts (NFs). Survival analyses were performed to assess the prognostic value of circNOX4 in NSCLC clinical samples. The biological effects of circNOX4 were investigated by gain- and loss-of-function experiments in vitro and in vivo. Fluorescence in situ hybridization, luciferase reporter assays, RNA immunoprecipitation, and miRNA rescue experiments were conducted to elucidate the underlying mechanisms of fibroblast activation. Cytokine antibody array, transwell coculture system, and enzyme-linked immunosorbent assay (ELISA) were performed to investigate the downstream effectors that promote cancer metastasis. RESULTS: FAP+CAFs were significantly enriched in metastatic cancer samples, and their higher abundance was correlated with the worse overall survival in NSCLC patients. A novel CAF-specific circRNA, circNOX4 (hsa_circ_0023988), evoked the phenotypic transition from NFs into CAFs and promoted the migration and invasion of NSCLC in vitro and in vivo. Clinically, circNOX4 correlated with the poor prognosis of advanced NSCLC patients. Mechanistically, circNOX4 upregulated FAP by sponging miR-329-5p, which led to fibroblast activation. Furthermore, the circNOX4/miR-329-5p/FAP axis activated an inflammatory fibroblast niche by preferentially inducing interleukin-6 (IL-6) and eventually promoting NSCLC progression. Disruption of the intercellular circNOX4/IL-6 axis significantly suppressed tumor growth and metastatic colonization in vivo. CONCLUSIONS: Our study reveals a role of the circRNA-induced fibroblast niche in tumor metastasis and highlights that targeting the circNOX4/FAP/IL-6 axis is a promising strategy for the intervention of NSCLC metastasis.

Computed Tomography-Based Radiomics to Predict FOXM1 Expression and Overall Survival in Patients with Clear Cell Renal Cell Carcinoma.

RATIONALE AND OBJECTIVES: To establish a computed tomography (CT)-based radiomics model to predict Fork head box M1(FOXM1) expression levels and develop a combined model for prognostic prediction in patients with clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS: A total of 529 patients were utilized to assess the prognostic significance of FOXM1 expression and were subsequently categorized into low and high FOXM1 expression groups. 184 patients with CT images were randomly divided into training and validation cohorts. Radiomics signature (Rad-score) for predicting FOXM1 expression level was developed in the training cohort. The predictive performance was evaluated using receiver operating characteristic (ROC) curves. A clinical model based on clinical factors and a combined model incorporating clinical factors and Rad-score were developed to predict ccRCC prognosis using Cox regression analyses. The concordance index(C-index) was employed to assess and compare the predictive capabilities of the Rad-score, TNM stage, clinical model, and combined model. The likelihood ratio test was used to compare the models' performance. RESULTS: The Rad-score demonstrated high predictive accuracy for high FOXM1 expression with areas under the ROC curves of 0.713 and 0.711 in the training and validation cohorts. In the training cohort, the C-indexes for the Rad-score, TNM Stage, clinical model, and combined model were 0.657, 0.711, 0.737, and 0.741, respectively. Correspondingly, in the validation cohort, the C-indexes were 0.670, 0.712, 0.736, and 0.745. The combined model had the highest C-index, significantly outperforming the other models. CONCLUSION: The Rad-score accurately predicts FOXM1 expression levels and is an independent prognostic factor for ccRCC.

Noninvasive Assessment of HER2 Expression Status in Gastric Cancer Using 18F-FDG Positron Emission Tomography/Computed Tomography-Based Radiomics: A Pilot Study.

Purpose: Immunohistochemistry (IHC) is the main method to detect human epidermal growth factor receptor 2 (HER2) expression levels. However, IHC is invasive and cannot reflect HER2 expression status in real time. The aim of this study was to construct and verify three types of radiomics models based on 18F-fuorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging and to evaluate the predictive ability of these radiomics models for the expression status of HER2 in patients with gastric cancer (GC). Patients and Methods: A total of 118 patients with GC were enrolled in this study. 18F-FDG PET/CT imaging was performed prior to surgery. The LIFEx software package was applied to extract PET and CT radiomics features. The minimum absolute contraction and selection operator (least absolute shrinkage and selection operator [LASSO]) algorithm was used to select the best radiomics features. Three machine learning methods, logistic regression (LR), support vector machine (SVM), and random forest (RF) models, were constructed and verified. The Synthetic Minority Oversampling Technique (SMOTE) was applied to address data imbalance. Results: In the training and test sets, the area under the curve (AUC) values of the LR, SVM, and RF models were 0.809, 0.761, 0.861 and 0.628, 0.993, 0.717, respectively, and the Brier scores were 0.118, 0.214, and 0.143, respectively. Among the three models, the LR and RF models exhibited extremely good prediction performance. The AUC values of the three models significantly improved after SMOTE balanced the data. Conclusions: 18F-FDG PET/CT-based radiomics models, especially LR and RF models, demonstrate good performance in predicting HER2 expression status in patients with GC and can be used to preselect patients who may benefit from HER2-targeted therapy.

The Value of LI-RADS and Radiomic Features From MRI for Predicting Microvascular Invasion in Hepatocellular Carcinoma within 5 cm.

RATIONALE AND OBJECTIVES: To explore and compare the performance of LI-RADS® and radiomics from multiparametric MRI in predicting microvascular invasion (MVI) preoperatively in patients with solitary hepatocellular carcinoma (HCC)< 5 cm. METHODS: We enrolled 143 patients with pathologically proven HCC and randomly stratified them into training (n = 100) and internal validation (n = 43) cohorts. Besides, 53 patients were enrolled to constitute an independent test cohort. Clinical factors and imaging features, including LI-RADS and three other features (non-smooth margin, incomplete capsule, and two-trait predictor of venous invasion), were reviewed and analyzed. Radiomic features from four MRI sequences were extracted. The independent clinic-imaging (clinical) and radiomics model for MVI-prediction were constructed by logistic regression and AdaBoost respectively. And the clinic-radiomics combined model was further constructed by logistic regression. We assessed the model discrimination, calibration, and clinical usefulness by using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision-curve analysis respectively. RESULTS: Incomplete tumor capsule, corona enhancement, and radiomic features were related to MVI in solitary HCC＜5 cm. The clinical model achieved AUC of 0.694/0.661 (training/internal validation). The single-sequence-based radiomic model's AUCs were 0.753-0.843/0.698-0.767 (training/internal validation). The combination model exhibited superior diagnostic performance to the clinical model (AUC: 0.895/0.848 [training/ internal validation]) and yielded an AUC of 0.858 in an independent test cohort. CONCLUSION: Incomplete tumor capsule and corona enhancement on preoperative MRI were significantly related to MVI in solitary HCC＜5 cm. Multiple-sequence radiomic features potentially improve MVI-prediction-model performance, which could potentially help determining HCC's appropriate therapy.

The GRAPHS-CRAFITY score: a novel efficacy predictive tool for unresectable hepatocellular carcinoma treated with immunotherapy.

OBJECTIVES: To investigate MR features associated with prognosis of unresectable HCC receiving immunotherapy and establish a MR feature-based scoring system to predict efficacy of immunotherapy. METHODS: This retrospective study included patients with unresectable HCC who received immunotherapy at 2 hospitals between August 2018 and February 2022. The last follow-up was October 2022. Clinical variables and MR features were assessed using univariate and multivariate Cox regression analyses. A new scoring system was constructed based on independent risk factors and the CRAFITY score consisting of AFP (≥ 100 ng/ml) and CRP (≥ 1 mg/dl). And the predictive performance of CRAFITY core and new score were compared by receiver-operating-characteristics curves (ROCs), area under ROCs (AUCs), and calibration curves. RESULTS: A total of 166 patients (55.6 ± 10.4 years) were included in training cohort and 77 patients (55.4 ± 10.7 years) were included in validation cohort. There were significant differences in BCLC stage, max size, macrovascular invasion, intratumoral artery, and enhancing capsule between the 2 groups. Based on independent risk factors (gross GRowtH type, intratumoral fAt, enhancing tumor caPsule, Sex and CRAFITY score), a novel efficacy predictive tool named the GRAPHS-CRAFITY score was developed to predict OS. The OS was significantly different among the 3 groups according to GRAPHS-CRAFITY score (p value < 0.001). The GRAPHS-CRAFITY score could predict tumor response and disease control (p value < 0.001, p value < 0.001). CONCLUSIONS: The GRAPHS-CRAFITY score is a reliable and easily applicable tool to predict the efficacy of unresectable HCC receiving immunotherapy.

Preoperative prediction of disease-free survival in pancreatic ductal adenocarcinoma patients after R0 resection using contrast-enhanced CT and CA19-9.

OBJECTIVES: To investigate the efficiency of a combination of preoperative contrast-enhanced computed tomography (CECT) and carbohydrate antigen 19-9 (CA19-9) in predicting disease-free survival (DFS) after R0 resection of pancreatic ductal adenocarcinoma (PDAC). METHODS: A total of 138 PDAC patients who underwent curative R0 resection were retrospectively enrolled and allocated chronologically to training (n = 91, January 2014-July 2019) and validation cohorts (n = 47, August 2019-December 2020). Using univariable and multivariable Cox regression analyses, we constructed a preoperative clinicoradiographic model based on the combination of CECT features and serum CA19-9 concentrations, and validated it in the validation cohort. The prognostic performance was evaluated and compared with that of postoperative clinicopathological and tumor-node-metastasis (TNM) models. Kaplan-Meier analysis was conducted to verify the preoperative prognostic stratification performance of the proposed model. RESULTS: The preoperative clinicoradiographic model included five independent prognostic factors (tumor diameter on CECT > 4 cm, extrapancreatic organ infiltration, CECT-reported lymph node metastasis, peripheral enhancement, and preoperative CA19-9 levels > 180 U/mL). It better predicted DFS than did the postoperative clinicopathological (C-index, 0.802 vs. 0.787; p < 0.05) and TNM (C-index, 0.802 vs. 0.711; p < 0.001) models in the validation cohort. Low-risk patients had significantly better DFS than patients at the high-risk, defined by the model preoperatively (p < 0.001, training cohort; p < 0.01, validation cohort). CONCLUSIONS: The clinicoradiographic model, integrating preoperative CECT features and serum CA19-9 levels, helped preoperatively predict postsurgical DFS for PDAC and could facilitate clinical decision-making. CLINICAL RELEVANCE STATEMENT: We constructed a simple model integrating clinical and radiological features for the prediction of disease-free survival after curative R0 resection in patients with pancreatic ductal adenocarcinoma; this novel model may facilitate preoperative identification of patients at high risk of recurrence and metastasis that may benefit from neoadjuvant treatments. KEY POINTS: • Existing clinicopathological predictors for prognosis in pancreatic ductal adenocarcinoma (PDAC) patients who underwent R0 resection can only be ascertained postoperatively and do not allow preoperative prediction. • We constructed a clinicoradiographic model, using preoperative contrast-enhanced computed tomography (CECT) features and preoperative carbohydrate antigen 19-9 (CA19-9) levels, and presented it as a nomogram. • The presented model can predict disease-free survival (DFS) in patients with PDAC better than can postoperative clinicopathological or tumor-node-metastasis (TNM) models.

Reclassification of therapeutic response of unresectable hepatocellular carcinoma to anti-angiogenic therapy and immunotherapy using alpha RECIST.

OBJECTIVES: To assess the therapeutic response of HCC to antiangiogenic therapy plus immunotherapy by integrating RECIST 1.1 and alpha-fetoprotein (AFP) response at the 6th week to predict overall survival (OS). METHODS: This retrospective study included 150 and 214 patients with HCC who received combination therapy in training and validation cohorts. The medical images and AFP levels obtained at baseline and 6th week were collected. AFP response stratification: partial response (PR): AFP% ≥ 75% decline; stable disease (SD): AFP% < 75% decline and ≤ 10% elevation; progressive disease (PD): AFP% > 10% elevation. The alpha-RECIST was: PR: RECIST 1.1-PR or AFP-PR; PD: AFP-PD or RECIST 1.1-PD and does not satisfy AFP-PR; SD: neither PR nor PD. OS was compared using Kaplan-Meier curves. The predictive ability of various criteria was evaluated using the concordance index and time-dependent area under the receiver-operating characteristic curve. RESULTS: RECIST 1.1 achieved significant OS stratification (p = 0.020) for AFP < 20 ng/mL. For AFP ≥ 20 ng/mL, alpha-RECIST showed better performance than RECIST 1.1, mRECIST, and AFP response according to C-index (0.73 vs 0.66 vs 0.68 vs 0.69). The National Cancer Center (NCC) strategy utilized RECIST 1.1 for AFP < 20 ng/mL and alpha-RECIST for AFP ≥ 20 ng/mL and showed better performance than RECIST 1.1, mRECIST and AFP response according to C-index (0.73 vs 0.67 vs 0.69 vs 0.64). The performances of alpha-RECIST and NCC Strategy were confirmed in the validation cohort (C-index = 0.77 and 0.74). CONCLUSIONS: The alpha-RECIST and NCC Strategy achieved better survival stratification in patients with HCC under combination therapy in the AFP ≥ 20 ng/mL group and the whole cohort compared to the RECIST 1.1, mRECIST, and AFP response. CLINICAL TRANSLATIONAL RELEVANCE: The alpha-RECIST and National Cancer Center strategy are optimal methods for determining therapeutic response to a combination of anti-angiogenic therapy plus immunotherapy and facilitating accurate prognostic stratification for HCC in the AFP ≥ 20 ng/mL group and the whole cohort, which may help oncologists for early identification of responders and progression at 6 weeks and clinical decision-making. KEY POINTS: • RECIST 1.1 is indicated for patients with baseline alpha-fetoprotein (AFP) < 20 ng/mL. • For patients with baseline AFP ≥ 20 ng/mL, integrating RECIST 1.1 and AFP response (alpha-RECIST) may aid in the early identification of survival benefits and progression definition prior to the administration of additional efficacious drugs. • The National Cancer Center strategy is an optimal stratified strategy for determining therapeutic response to a combination of anti-angiogenic therapy and immunotherapy for HCC based on baseline AFP levels.

Feasibility of One-Day PET/CT Scanning Protocol with 68Ga-DOTA-FAPI-04 and 18F-FDG for the Detection of Ovarian Cancer Recurrence and Metastasis.

Objective: The objective of this study was to investigate the feasibility of 1-d 68Ga-DOTA-FAPI-04 and 18F-FDG (2-deoxy-2[18F]fluoro-d-glucose) positron emission tomography/computed tomography (PET/CT) for detecting ovarian cancer recurrence and metastasis. Materials and Methods: Fifty-two patients who underwent 18F-FDG and 68Ga-DOTA-FAPI-04 PET/CT were divided into 1- and 2-d groups. Image acquisition, injection time, and total waiting time were compared. For the 68Ga-DOTA-FAPI-04 PET/CT scans, low-dose CT scans and low injection dosages were employed, and total radiation dose was assessed for both protocols. The comparative analysis included assessment of patient-based detection rates and lesion-based diagnostic efficacy. Results: The total waiting time was significantly shorter in the 1-d group than in the 2-d group (p = 0.000). The radiation doses stemming from internal radiation and external radiation between the groups showed no differences (p = 0.151 vs. 0.716). In the patient-based analysis, the detection rates for local recurrence, peritoneal, lymph node, and other metastases were not significantly different in both protocols (p ∈ [0.351, 1.000]). For the lesion-based analysis, no differences were noted in terms of sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (p ∈ [0.371, 1.000]). Conclusions: The 1-d PET/CT protocol reduced waiting time and exhibited equivalent detectability compared with the 2-d protocol, suggesting its clinical value.

Analysis of preoperative computed tomography radiomics and clinical factors for predicting postsurgical recurrence of papillary thyroid carcinoma.

BACKGROUND: Postsurgical recurrence is of great concern for papillary thyroid carcinoma (PTC). We aim to investigate the value of computed tomography (CT)-based radiomics features and conventional clinical factors in predicting the recurrence of PTC. METHODS: Two-hundred and eighty patients with PTC were retrospectively enrolled and divided into training and validation cohorts at a 6:4 ratio. Recurrence was defined as cytology/pathology-proven disease or morphological evidence of lesions on imaging examinations within 5 years after surgery. Radiomics features were extracted from manually segmented tumor on CT images and were then selected using four different feature selection methods sequentially. Multivariate logistic regression analysis was conducted to identify clinical features associated with recurrence. Radiomics, clinical, and combined models were constructed separately using logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), and neural network (NN), respectively. Receiver operating characteristic analysis was performed to evaluate the model performance in predicting recurrence. A nomogram was established based on all relevant features, with its reliability and reproducibility verified using calibration curves and decision curve analysis (DCA). RESULTS: Eighty-nine patients with PTC experienced recurrence. A total of 1218 radiomics features were extracted from each segmentation. Five radiomics and six clinical features were related to recurrence. Among the 4 radiomics models, the LR-based and SVM-based radiomics models outperformed the NN-based radiomics model (P = 0.032 and 0.026, respectively). Among the 4 clinical models, only the difference between the area under the curve (AUC) of the LR-based and NN-based clinical model was statistically significant (P = 0.035). The combined models had higher AUCs than the corresponding radiomics and clinical models based on the same classifier, although most differences were not statistically significant. In the validation cohort, the combined models based on the LR, SVM, KNN, and NN classifiers had AUCs of 0.746, 0.754, 0.669, and 0.711, respectively. However, the AUCs of these combined models had no significant differences (all P > 0.05). Calibration curves and DCA indicated that the nomogram have potential clinical utility. CONCLUSIONS: The combined model may have potential for better prediction of PTC recurrence than radiomics and clinical models alone. Further testing with larger cohort may help reach statistical significance.

Association Between MRI Radiomics and Intratumoral Tertiary Lymphoid Structures in Intrahepatic Cholangiocarcinoma and Its Prognostic Significance.

BACKGROUND: Tertiary lymphoid structures (TLSs) have prognostic value in intrahepatic cholangiocarcinoma (ICC) patients. Noninvasive tool to preoperatively evaluate TLSs is still lacking. PURPOSE: To explore the association between TLSs status of ICC and preoperative MRI radiomics analysis. STUDY TYPE: Retrospective. SUBJECTS: One hundred and ninety-two patients with ICC, divided into training (T = 105), internal validation groups (V1 = 46), and external validation group (V2 = 41). SEQUENCE: Coronal and axial single-shot fast spin-echo T2-weighted, diffusion-weighted imaging, T1-weighted, and T1WI fat-suppressed spoiled gradient-recall echo LAVA sequence at 3.0 T. ASSESSMENT: The VOIs were drawn manually within the visible borders of the tumors using ITK-SNAP version 3.8.0 software in the axial T2WI, DWI, and portal vein phase sequences. Radiomics features were subjected to least absolute shrinkage and selection operator regression to select the associated features of TLSs and construct the radiomics model. Univariate and multivariate analyses were used to identify the clinical radiological variables associated with TLSs. The performances were evaluated by the area under the receiver operator characteristic curve (AUC). STATISTICAL TESTS: Logistic regression analysis, ROC and AUC, Hosmer-Lemeshow test, Kaplan-Meier method with the log-rank test, calibration curves, and decision curve analysis. P < 0.05 was considered statistically significant. RESULTS: The AUCs of arterial phase diffuse hyperenhancement were 0.59 (95% confidence interval [CI], 0.50-0.67), 0.52 (95% CI, 0.43-0.61), and 0.66 (95% CI, 0.52-0.80) in the T, V1, and V2 cohorts. The AUCs of Rad-score were 0.85 (95% CI, 0.77-0.92), 0.81 (95% CI, 0.67-0.94), and 0.84 (95% CI, 0.71-0.96) in the T, V1, and V2 cohorts, respectively. In cohort T, low-risk group showed significantly better median recurrence-free survival (RFS) than that of the high-risk group, which was also confirmed in cohort V1 and V2. DATA CONCLUSION: A preoperative MRI radiomics signature is associated with the intratumoral TLSs status of ICC patients and correlate significantly with RFS. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.