N6-methyladenosine-modified circPLPP4 sustains cisplatin resistance in ovarian cancer cells via PIK3R1 upregulation.

BACKGROUND: Cisplatin (CDDP) is the first-line chemotherapeutic strategy to treat patients with ovarian cancer (OC). The development of CDDP resistance remains an unsurmountable obstacle in OC treatment and frequently induces tumor recurrence. Circular RNAs (circRNAs) are noncoding RNAs with important functions in cancer progression. Whether circRNAs function in CDDP resistance of OC is unclear. METHODS: Platinum-resistant circRNAs were screened via circRNA deep sequencing and examined using in situ hybridization (ISH) in OC. The role of circPLPP4 in CDDP resistance was assessed by clone formation and Annexin V assays in vitro, and by OC patient-derived xenografts and intraperitoneal tumor models in vivo. The mechanism underlying circPLPP4-mediated activation of miR-136/PIK3R1 signaling was examined by luciferase reporter assay, RNA pull-down, RIP, MeRIP and ISH. RESULTS: circPLPP4 was remarkably upregulated in platinum resistant OC. circPLPP4 overexpression significantly enhanced, whereas circPLPP4 silencing reduced, OC cell chemoresistance. Mechanistically, circPLPP4 acts as a microRNA sponge to sequester miR-136, thus competitively upregulating PIK3R1 expression and conferring CDDP resistance. The increased circPLPP4 level in CDDP-resistant cells was caused by increased RNA stability, mediated by increased N6-methyladenosine (m6A) modification of circPLPP4. In vivo delivery of an antisense oligonucleotide targeting circPLPP4 significantly enhanced CDDP efficacy in a tumor model. CONCLUSIONS: Our study reveals a plausible mechanism by which the m6A -induced circPLPP4/ miR-136/ PIK3R1 axis mediated CDDP resistance in OC, suggesting that circPLPP4 may serve as a promising therapeutic target against CDDP resistant OC. A circPLPP4-targeted drug in combination with CDDP might represent a rational regimen in OC.

High b-Value and Ultra-High b-Value Diffusion Weighted MRI in Stroke.

PURPOSE: To explore the application value of high-b-value and ultra-high b-value DWI in noninvasive evaluation of ischemic infarctions. STUDY TYPE: Prospective. SUBJECTS: Sixty-four patients with clinically diagnosed ischemic lesions based on symptoms and DWI. FIELD STRENGTH/SEQUENCE: 3.0 T/T2-weighted fast spin-echo, fluid-attenuated inversion recovery, pre-contrast T1-weighted magnetization prepared rapid gradient echo sequence, multi-b-value trace DWI and q-space sampling sequences. ASSESSMENT: Lesions were segmented on standard b-value DWI (SB-DWI, 1000 s/mm2), high b-value DWI (HB-DWI, 4000 s/mm2) and ultra-high b-value DWI (UB-DWI, 10,000 s/mm2), and cumulative segmented areas were the final abnormality volumes. Normal white matter (WM) areas were obtained after binarization of segmented brain. In 47 patients, fractional anisotropy (FA) and apparent diffusion coefficients (ADCs) at b values of 1000, 4000, and 10,000 s/mm2 were extracted from symmetrical WM masks and lesion masks of contralateral WM (CWM) and lesion-side WM (LWM). STATISTICAL TESTS: Wilcoxon matched-pairs signed-rank test and Pearson correlation analysis. Two-tailed P-values <0.05 were considered statistically significant. RESULTS: Various signals of HB-/UB-DWI (hypo-, iso- or hyper-intensity) were observed in strokes compared with SB-DWI, and some areas with iso-intensity of SB-DWI manifested with hyper-intensity on HB-/UB-DWI. Abnormality volumes from SB-DWI were significantly smaller than those from HB-DWI and UB-DWI (10.32 ± 16.45 cm3, vs. 12.25 ± 19.71 cm3 and 11.83 ± 19.41 cm3), while no significant difference exist in volume between HB-DWI and UB-DWI (P = 0.32). In CWM, FA significantly correlated with ADC4000 and ADC10,000 (maximum r = -0.51 and -0.64), but did not significantly correlate with ADC1000 (maximum r = -0.20, P = 0.17). ADC1000 or ADC4000 of LWM not significant correlated with FA of CWM (maximum r = -0.28, P = 0.06), while ADC10,000 of LWM significantly correlated with FA of CWM (maximum r = -0.46). DATA CONCLUSION: HB- and UB-DWI have potential to be supplementary tools for the noninvasive evaluation of stroke lesions in clinics. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Prediction of microvascular invasion in hepatocellular carcinoma patients with MRI radiomics based on susceptibility weighted imaging and T2-weighted imaging.

BACKGROUND: The accurate identification of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is of great clinical importance. PURPOSE: To develop a radiomics nomogram based on susceptibility-weighted imaging (SWI) and T2-weighted imaging (T2WI) for predicting MVI in early-stage (Barcelona Clinic Liver Cancer stages 0 and A) HCC patients. MATERIALS AND METHODS: A prospective cohort of 189 participants with HCC was included for model training and testing, and an additional 34 participants were enrolled for external validation. ITK-SNAP was used to manually segment the tumour, and PyRadiomics was used to extract radiomic features from the SWI and T2W images. Variance filtering, student's t test, least absolute shrinkage and selection operator regression and random forest (RF) were applied to select meaningful features. Four machine learning classifiers, including K-nearest neighbour, RF, logistic regression and support vector machine-based models, were established. Independent clinical and radiological risk factors were also determined to establish a clinical model. The best radiomics and clinical models were further evaluated in the validation set. In addition, a nomogram was constructed from the radiomic model and independent clinical factors. Diagnostic efficacy was evaluated by receiver operating characteristic curve analysis with fivefold cross-validation. RESULTS: AFP levels greater than 400 ng/mL [odds ratio (OR) 2.50; 95% confidence interval (CI) 1.239-5.047], tumour diameter greater than 5 cm (OR 2.39; 95% CI 1.178-4.839), and absence of pseudocapsule (OR 2.053; 95% CI 1.007-4.202) were found to be independent risk factors for MVI. The areas under the curve (AUCs) of the best radiomic model were 1.000 and 0.882 in the training and testing cohorts, respectively, while those of the clinical model were 0.688 and 0.6691. In the validation set, the radiomic model achieved better diagnostic performance (AUC = 0.888) than the clinical model (AUC = 0.602). The combination of clinical factors and the radiomic model yielded a nomogram with the best diagnostic performance (AUC = 0.948). CONCLUSION: SWI and T2WI-derived radiomic features are valuable for noninvasively and accurately identifying MVI in early-stage HCC. Furthermore, the integration of radiomics and clinical factors yielded a predictive nomogram with satisfactory diagnostic performance and potential clinical benefits.

Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort study.

BACKGROUND: Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms. METHODS: In this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics. RESULTS: The RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02-0.06, P < 0.001) and achieved AUCs of 0.98, 0.94, and 0.92 for 1-, 2-, and 3-year RFS, respectively. In the validation and testing cohorts, the RDeepNet model could also clarify patients into high- and low-risk groups, and demonstrated AUCs of 0.91 and 0.94 for 3-year RFS, respectively. Radiomic features displayed differential expression between the two risk groups. Furthermore, the generalizability of RDeepNet model was confirmed across different molecular subtypes and patient populations with different therapy regimens (All P < 0.001). The study also identified variations in radiomic features among patients with diverse therapeutic responses and after neoadjuvant chemotherapy. Importantly, a significant correlation between radiomics and long non-coding RNAs (lncRNAs) was discovered. A key lncRNA was found to be noninvasively quantified by a deep learning-based radiomics prediction model with AUCs of 0.79 in the training cohort and 0.77 in the testing cohort. CONCLUSIONS: This study demonstrates that machine learning radiomics of MRI can effectively predict RFS after surgery in patients with breast cancer, and highlights the feasibility of non-invasive quantification of lncRNAs using radiomics, which indicates the potential of radiomics in guiding treatment decisions.

An MRI-based scoring system for pretreatment risk stratification in locally advanced rectal cancer.

BACKGROUND: Accurately assessing the risk of recurrence in patients with locally advanced rectal cancer (LARC) before treatment is important for the development of treatment strategies. The purpose of this study is to develop an MRI-based scoring system to predict the risk of recurrence in patients with LARC. METHODS: This was a multicenter observational study that enrolled participants who underwent neoadjuvant chemoradiotherapy. To evaluate the risk of recurrence in these patients, we developed the mrDEC scoring system and assessed inter-reader agreement. Additionally, we plotted Kaplan-Meier curves to compare the 3-year disease-free survival (DFS) and 5-year overall survival (OS) rates among patients with different mrDEC scores. RESULTS: A total of 1287 patients with LARC were included in this study. We observed substantial inter-reader agreement for mrDEC. Based on the mrDEC scores ranging from 0 to 3, the patients were categorized into four groups. The 3-year DFS rates for the groups were 91.0%, 79.5%, 65.5%, and 44.0% (P < 0.0001), respectively, and the 5-year OS rates were 92.9%, 87.1%, 74.8%, and 44.5%, respectively (P < 0.0001). CONCLUSIONS: The mrDEC scoring system proved to be an effective tool for predicting the prognosis of patients with LARC and can assist clinicians in clinical decision-making.

Deep Learning for Fully Automated Prediction of Overall Survival in Patients Undergoing Resection for Pancreatic Cancer: A Retrospective Multicenter Study.

OBJECTIVE: To develop an imaging-derived biomarker for prediction of overall survival (OS) of pancreatic cancer by analyzing preoperative multiphase contrast-enhanced computed topography (CECT) using deep learning. BACKGROUND: Exploiting prognostic biomarkers for guiding neoadjuvant and adjuvant treatment decisions may potentially improve outcomes in patients with resectable pancreatic cancer. METHODS: This multicenter, retrospective study included 1516 patients with resected pancreatic ductal adenocarcinoma (PDAC) from 5 centers located in China. The discovery cohort (n=763), which included preoperative multiphase CECT scans and OS data from 2 centers, was used to construct a fully automated imaging-derived prognostic biomarker-DeepCT-PDAC-by training scalable deep segmentation and prognostic models (via self-learning) to comprehensively model the tumor-anatomy spatial relations and their appearance dynamics in multiphase CECT for OS prediction. The marker was independently tested using internal (n=574) and external validation cohorts (n=179, 3 centers) to evaluate its performance, robustness, and clinical usefulness. RESULTS: Preoperatively, DeepCT-PDAC was the strongest predictor of OS in both internal and external validation cohorts [hazard ratio (HR) for high versus low risk 2.03, 95% confidence interval (CI): 1.50-2.75; HR: 2.47, CI: 1.35-4.53] in a multivariable analysis. Postoperatively, DeepCT-PDAC remained significant in both cohorts (HR: 2.49, CI: 1.89-3.28; HR: 2.15, CI: 1.14-4.05) after adjustment for potential confounders. For margin-negative patients, adjuvant chemoradiotherapy was associated with improved OS in the subgroup with DeepCT-PDAC low risk (HR: 0.35, CI: 0.19-0.64), but did not affect OS in the subgroup with high risk. CONCLUSIONS: Deep learning-based CT imaging-derived biomarker enabled the objective and unbiased OS prediction for patients with resectable PDAC. This marker is applicable across hospitals, imaging protocols, and treatments, and has the potential to tailor neoadjuvant and adjuvant treatments at the individual level.

Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study.

BACKGROUND: Post-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions. We aimed to develop a radiogenomic signature for the pre-treatment prediction of PRNN to guide re-radiotherapy in patients with LRNPC. METHODS: This multicenter study included 761 re-irradiated patients with LRNPC at four centers in NPC endemic area and divided them into training, internal validation, and external validation cohorts. We built a machine learning (random forest) radiomic signature based on the pre-treatment multiparametric magnetic resonance images for predicting PRNN following re-radiotherapy. We comprehensively assessed the performance of the radiomic signature. Transcriptomic sequencing and gene set enrichment analyses were conducted to identify the associated biological processes. RESULTS: The radiomic signature showed discrimination of 1-year PRNN in the training, internal validation, and external validation cohorts (area under the curve (AUC) 0.713-0.756). Stratified by a cutoff score of 0.735, patients with high-risk signature had higher incidences of PRNN than patients with low-risk signature (1-year PRNN rates 42.2-62.5% vs. 16.3-18.8%, P < 0.001). The signature significantly outperformed the clinical model (P < 0.05) and was generalizable across different centers, imaging parameters, and patient subgroups. The radiomic signature had prognostic value concerning its correlation with PRNN-related deaths (hazard ratio (HR) 3.07-6.75, P < 0.001) and all causes of deaths (HR 1.53-2.30, P < 0.01). Radiogenomics analyses revealed associations between the radiomic signature and signaling pathways involved in tissue fibrosis and vascularity. CONCLUSIONS: We present a radiomic signature for the individualized risk assessment of PRNN following re-radiotherapy, which may serve as a noninvasive radio-biomarker of radiation injury-associated processes and a useful clinical tool to personalize treatment recommendations for patients with LANPC.

A Novel Prognostic Score Based on Multiple Quantitative Parameters of Chest CT for Patients with Synchronous Multiple Primary Lung Cancer: Is Solid Component Size a Better Prognostic Indicator?

BACKGROUND: There is no simple and definitive way to predict the prognosis of synchronous multiple primary lung cancer (SMPLC). In this study, we developed a clinical prognostic score for predicting the survival of patients with SMPLC. PATIENTS AND METHODS: This study included 206 patients with SMPLC between 2011 and 2020 at three hospitals. Kaplan-Meier analysis was used to determine the optimal cutoff values for the quantitative chest computed tomography (CT) parameters. Multivariable Cox proportional hazards regression was carried out to identify independent prognostic factors for predicting overall survival (OS) and disease-free survival (DFS). The time-dependent receiver operating characteristic curve was analyzed to evaluate the prognostic performance. RESULTS: A CT-based prognostic score (CTPS) comprising six chest CT parameters was developed. Compared with T stage, CTPS had a higher prediction accuracy for OS and DFS. All C-indices of the model reached a satisfactory level in both the development and validation cohorts. Significant differences in the OS and DFS curves were observed when the patients were stratified into different risk groups. The high-risk group (CTPS of 5-6) had poorer survival than the low-risk group (CTPS of 0-4). CONCLUSIONS: The developed CTPS and the corresponding risk stratification system are valid for predicting the survival of patients with SMPLC.

Benefit of [18F] FDG PET/CT in the diagnosis and salvage treatment of recurrent nasopharyngeal carcinoma.

PURPOSE: To compare PET/CT, MRI and ultrasonography in detecting recurrence of nasopharyngeal carcinoma and identify their benefit in staging, contouring and overall survival (OS). METHODS: Cohort A included 1453 patients with or without histopathology-confirmed local recurrence, while cohort B consisted of 316 patients with 606 histopathology-confirmed lymph nodes to compare the sensitivities and specificities of PET/CT, MRI and ultrasonography using McNemar test. Cohorts C and D consisted of 273 patients from cohort A and 267 patients from cohort B, respectively, to compare the distribution of PET/CT-based and MRI-based rT-stage and rN-stage and the accuracy of rN-stage using McNemar test. Cohort E included 30 random patients from cohort A to evaluate the changes in contouring with or without PET/CT by related-samples T test or Wilcoxon rank test. The OS of 61 rT3-4N0M0 patients staged by PET/CT plus MRI (cohort F) and 67 MRI-staged rT3-4N0M0 patients (cohort G) who underwent similar salvage treatment were compared by log-rank test and Cox regression. RESULTS: PET/CT had similar specificity to MRI but higher sensitivity (93.9% vs. 79.3%, P < 0.001) in detecting local recurrence. PET/CT, MRI and ultrasonography had comparable specificities, but PET/CT had greater sensitivity than MRI (90.9% vs. 67.6%, P < 0.001) and similar sensitivity to ultrasonography in diagnosing lymph nodes. According to PET/CT, more patients were staged rT3-4 (82.8% vs. 68.1%, P < 0.001) or rN + (89.9% vs. 69.3%, P < 0.001), and the rN-stage was more accurate (90.6% vs. 73.8%, P < 0.001). Accordingly, the contours of local recurrence were more precise (median Dice similarity coefficient 0.41 vs. 0.62, P < 0.001) when aided by PET/CT plus MRI. Patients staged by PET/CT plus MRI had a higher 3-year OS than patients staged by MRI alone (85.5% vs. 60.4%, P = 0.006; adjusted HR = 0.34, P = 0.005). CONCLUSION: PET/CT more accurately detected and staged recurrence of nasopharyngeal carcinoma and accordingly complemented MRI, providing benefit in contouring and OS.

CT texture analysis in predicting treatment response and survival in patients with hepatocellular carcinoma treated with transarterial chemoembolization using random forest models.

BACKGROUND: Using texture features derived from contrast-enhanced computed tomography (CT) combined with general imaging features as well as clinical information to predict treatment response and survival in patients with hepatocellular carcinoma (HCC) who received transarterial chemoembolization (TACE) treatment. METHODS: From January 2014 to November 2022, 289 patients with HCC who underwent TACE were retrospectively reviewed. Their clinical information was documented. Their treatment-naïve contrast-enhanced CTs were retrieved and reviewed by two independent radiologists. Four general imaging features were evaluated. Texture features were extracted based on the regions of interest (ROIs) drawn on the slice with the largest axial diameter of all lesions using Pyradiomics v3.0.1. After excluding features with low reproducibility and low predictive value, the remaining features were selected for further analyses. The data were randomly divided in a ratio of 8:2 for model training and testing. Random forest classifiers were built to predict patient response to TACE treatment. Random survival forest models were constructed to predict overall survival (OS) and progress-free survival (PFS). RESULTS: We retrospectively evaluated 289 patients (55.4 ± 12.4 years old) with HCC treated with TACE. Twenty features, including 2 clinical features (ALT and AFP levels), 1 general imaging feature (presence or absence of portal vein thrombus) and 17 texture features, were included in model construction. The random forest classifier achieved an area under the curve (AUC) of 0.947 with an accuracy of 89.5% for predicting treatment response. The random survival forest showed good predictive performance with out-of-bag error rate of 0.347 (0.374) and a continuous ranked probability score (CRPS) of 0.170 (0.067) for the prediction of OS (PFS). CONCLUSIONS: Random forest algorithm based on texture features combined with general imaging features and clinical information is a robust method for predicting prognosis in patients with HCC treated with TACE, which may help avoid additional examinations and assist in treatment planning.

Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma.

BACKGROUND: The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the prediction of VETC status. PURPOSE: This study aimed to build and compare VETC-MVI related models (clinical, radiomics, and deep learning) associated with recurrence-free survival of HCC patients. STUDY TYPE: Retrospective. POPULATION: 398 HCC patients (349 male, 49 female; median age 51.7 years, and age range: 22-80 years) who underwent resection from five hospitals in China. The patients were randomly divided into training cohort (n = 358) and test cohort (n = 40). FIELD STRENGTH/SEQUENCE: 3-T, pre-contrast T1-weighted imaging spoiled gradient recalled echo (T1WI SPGR), T2-weighted imaging fast spin echo (T2WI FSE), and contrast enhanced arterial phase (AP), delay phase (DP). ASSESSMENT: Two radiologists performed the segmentation of HCC on T1WI, T2WI, AP, and DP images, from which radiomic features were extracted. The RFS related clinical characteristics (VETC, MVI, Barcelona stage, tumor maximum diameter, and alpha fetoprotein) and radiomic features were used to build the clinical model, clinical-radiomic (CR) nomogram, deep learning model. The follow-up process was done 1 month after resection, and every 3 months subsequently. The RFS was defined as the date of resection to the date of recurrence confirmed by radiology or the last follow-up. Patients were followed up until December 31, 2022. STATISTICAL TESTS: Univariate COX regression, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier curves, log-rank test, C-index, and area under the curve (AUC). P < 0.05 was considered statistically significant. RESULTS: The C-index of deep learning model achieved 0.830 in test cohort compared with CR nomogram (0.731), radiomic signature (0.707), and clinical model (0.702). The average RFS of the overall patients was 26.77 months (range 1-80 months). DATA CONCLUSION: MR deep learning model based on VETC and MVI provides a potential tool for survival assessment. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Quantitative synthetic MRI for predicting locally advanced rectal cancer response to neoadjuvant chemoradiotherapy.

OBJECTIVES: To investigate the value of pre-treatment quantitative synthetic MRI (SyMRI) for predicting a good response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer. METHODS: This prospective study enrolled 63 patients with locally advanced rectal cancer scheduled to undergo preoperative chemoradiotherapy from January 2019 to June 2021. T1 relaxation time (T1), T2 relaxation time (T2), proton density (PD) from synthetic MRI, and apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI) were measured. Independent-sample t-test, the Mann-Whitney U test, the Delong test, and receiver operating characteristic curve (ROC) analyses were used to predict the pathologic complete response (pCR) and T-downstaging. RESULTS: Among the 63 patients, 19 (30%) achieved pCR and 44 (70%) did not, and 24 (38%) achieved T-downstaging, while 44 (62%) did not. The mean T1 and T2 values were significantly lower in the pCR group compared with those in the non-pCR group and in the T-downstage group compared with those in the non-T-downstage group (all p < 0.05). There were no significant differences in the PD and ADC values between the two groups. There were no significant differences between the mean values of T1 and T2 for predicting pCR after CRT (AUC, 0.767 vs. 0.831, p = 0.37). There were no significant differences between the AUC values of T1 and T2 values for the assessment of post-CRT T-downstaging (AUC, 0.746 vs. 0.820, p = 0.506). CONCLUSIONS: In patients with locally advanced rectal cancer, the synthetic MRI-derived T1 relaxation time and T2 relaxation time values are promising imaging markers for predicting a good response to neoadjuvant chemoradiotherapy. KEY POINTS: • Mean T1 and T2 values were significantly lower in the pathologic complete response group and the T-downstage group. • There were no significant differences in the proton density and apparent diffusion coefficient values between the two groups.

Preoperative diagnosis of metastatic lymph nodes by CT-histopathologic matching analysis in gastric adenocarcinoma using dual-layer spectral detector CT.

OBJECTIVES: There still remain challenges to accurate diagnosis of lymph node (LN) involvement in gastric cancer (GC) on conventional CT. This study evaluated the quantitative data derived from dual-layer spectral detector CT (DLCT) for preoperative diagnosis of metastatic LNs compared to conventional CT images. METHODS: Patients with adenocarcinoma scheduled for gastrectomy were enrolled in this prospective study from July, 2021, to February, 2022. Regional LNs were labeled on preoperative DLCT. The LNs were located and matched using carbon nanoparticle solution during surgery according to their locations and anatomic landmarks on preoperative images. The matched LNs were randomly split into training and validation cohorts in a ratio of 2:1. The DLCT quantitative parameters in the training cohort were investigated using logistic regression models to identify independent predictors of metastatic LNs, and these predictors were subsequently applied to the validation cohort. Receiver operating characteristic curves were compared between the DLCT parameters and conventional CT images. RESULTS: Fifty-five patients were included in the study, with 267 successfully matched LNs (90 metastatic, 177 nonmetastatic). Independent predictors included arterial phase CT attenuation on 70-keV images, venous phase electron density, and clustered feature. These combination predictors had areas under the curve (AUC) of 0.855 and 0.907 in the training and validation cohorts, respectively. Compared to conventional CT criteria alone, the model had higher AUC and accuracy (0.741 vs. 0.907, 75.28% vs. 87.64%; p < 0.01) for LN diagnosis. CONCLUSION: Incorporating DLCT parameters improved preoperative diagnosis of LN metastasis in GC, increasing the accuracy of clinical N stage. CLINICAL RELEVANCE STATEMENT: Compared to conventional CT criteria, quantitative parameters from dual-layer spectral detector CT showed higher diagnostic efficacy for the preoperative diagnosis of lymph node metastases in gastric cancer, increasing the accuracy of clinical N stage. KEY POINTS: • Quantitative parameters from dual-layer spectral detector CT are useful for the preoperative diagnosis of lymph node metastases in gastric adenocarcinoma, increasing the accuracy of clinical N stage. • The values for metastatic lymph nodes are higher than those of nonmetastatic ones. The arterial phase of CT attenuation on 70-keV images, venous phase of electron density, and clustered feature independently predicted lymph node metastases. • Prediction model had area under the curve of 0.907, sensitivity of 81.82%, specificity of 91.07%, and accuracy of 87.64% for the preoperative diagnosis of lymph node metastasis.

AI-assisted compressed sensing and parallel imaging sequences for MRI of patients with nasopharyngeal carcinoma: comparison of their capabilities in terms of examination time and image quality.

OBJECTIVE: To compare examination time and image quality between artificial intelligence (AI)-assisted compressed sensing (ACS) technique and parallel imaging (PI) technique in MRI of patients with nasopharyngeal carcinoma (NPC). METHODS: Sixty-six patients with pathologically confirmed NPC underwent nasopharynx and neck examination using a 3.0-T MRI system. Transverse T2-weighted fast spin-echo (FSE) sequence, transverse T1-weighted FSE sequence, post-contrast transverse T1-weighted FSE sequence, and post-contrast coronal T1-weighted FSE were obtained by both ACS and PI techniques, respectively. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and duration of scanning of both sets of images analyzed by ACS and PI techniques were compared. The images from the ACS and PI techniques were scored for lesion detection, margin sharpness of lesions, artifacts, and overall image quality using the 5-point Likert scale. RESULTS: The examination time with ACS technique was significantly shorter than that with PI technique (p < 0.0001). The comparison of SNR and CNR showed that ACS technique was significantly superior with PI technique (p < 0.005). Qualitative image analysis showed that the scores of lesion detection, margin sharpness of lesions, artifacts, and overall image quality were higher in the ACS sequences than those in the PI sequences (p < 0.0001). Inter-observer agreement was evaluated for all qualitative indicators for each method, in which the results showed satisfactory-to-excellent agreement (p < 0.0001). CONCLUSION: Compared with the PI technique, the ACS technique for MR examination of NPC can not only shorten scanning time but also improve image quality. CLINICAL RELEVANCE STATEMENT: The artificial intelligence (AI)-assisted compressed sensing (ACS) technique shortens examination time for patients with nasopharyngeal carcinoma, while improving the image quality and examination success rate, which will benefit more patients. KEY POINTS: • Compared with the parallel imaging (PI) technique, the artificial intelligence (AI)-assisted compressed sensing (ACS) technique not only reduced examination time, but also improved image quality. • Artificial intelligence (AI)-assisted compressed sensing (ACS) pulls the state-of-the-art deep learning technique into the reconstruction procedure and helps find an optimal balance of imaging speed and image quality.

Predicting muscle invasion in bladder cancer by deep learning analysis of MRI: comparison with vesical imaging-reporting and data system.

OBJECTIVES: To compare the diagnostic performance of a novel deep learning (DL) method based on T2-weighted imaging with the vesical imaging-reporting and data system (VI-RADS) in predicting muscle invasion in bladder cancer (MIBC). METHODS: A total of 215 tumours (129 for training and 31 for internal validation, centre 1; 55 for external validation, centre 2) were included. MIBC was confirmed by pathological examination. VI-RADS scores were provided by two groups of radiologists (readers 1 and readers 2) independently. A deep convolutional neural network was constructed in the training set, and validation was conducted on the internal and external validation sets. ROC analysis was performed to evaluate the performance for MIBC diagnosis. RESULTS: The AUCs of the DL model, readers 1, and readers 2 were as follows: in the internal validation set, 0.963, 0.843, and 0.852, respectively; in the external validation set, 0.861, 0.808, and 0.876, respectively. The accuracy of the DL model in the tumours scored VI-RADS 2 or 3 was higher than that of radiologists in the external validation set: for readers 1, 0.886 vs. 0.600, p = 0.006; for readers 2, 0.879 vs. 0.636, p = 0.021. The average processing time (38 s and 43 s in two validation sets) of the DL method was much shorter than the readers, with a reduction of over 100 s in both validation sets. CONCLUSIONS: Compared to radiologists using VI-RADS, the DL method had a better diagnostic performance, shorter processing time, and robust generalisability, indicating good potential for diagnosing MIBC. KEY POINTS: • The DL model shows robust performance for MIBC diagnosis in both internal and external validation. • The diagnostic performance of the DL model in the tumours scored VI-RADS 2 or 3 is better than that obtained by radiologists using VI-RADS. • The DL method shows potential in the preoperative assessment of MIBC.

Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy.

OBJECTIVES: The accurate prediction of post-hepatectomy early recurrence in patients with hepatocellular carcinoma (HCC) is crucial for decision-making regarding postoperative adjuvant treatment and monitoring. We aimed to explore the feasibility of deep learning (DL) features derived from gadoxetate disodium (Gd-EOB-DTPA) MRI, qualitative features, and clinical variables for predicting early recurrence. METHODS: In this bicentric study, 285 patients with HCC who underwent Gd-EOB-DTPA MRI before resection were divided into training (n = 195) and validation (n = 90) sets. DL features were extracted from contrast-enhanced MRI images using VGGNet-19. Three feature selection methods and five classification methods were combined for DL signature construction. Subsequently, an mp-MR DL signature fused with multiphase DL signatures of contrast-enhanced images was constructed. Univariate and multivariate logistic regression analyses were used to identify early recurrence risk factors including mp-MR DL signature, microvascular invasion (MVI), and tumor number. A DL nomogram was built by incorporating deep features and significant clinical variables to achieve early recurrence prediction. RESULTS: MVI (p = 0.039), tumor number (p = 0.001), and mp-MR DL signature (p < 0.001) were independent risk factors for early recurrence. The DL nomogram outperformed the clinical nomogram in the training set (AUC: 0.949 vs. 0.751; p < 0.001) and validation set (AUC: 0.909 vs. 0.715; p = 0.002). Excellent DL nomogram calibration was achieved in both training and validation sets. Decision curve analysis confirmed the clinical usefulness of DL nomogram. CONCLUSION: The proposed DL nomogram was superior to the clinical nomogram in predicting early recurrence for HCC patients after hepatectomy. KEY POINTS: • Deep learning signature based on Gd-EOB-DTPA MRI was the predominant independent predictor of early recurrence for hepatocellular carcinoma (HCC) after hepatectomy. • Deep learning nomogram based on clinical factors and Gd-EOB-DTPA MRI features is promising for predicting early recurrence of HCC. • Deep learning nomogram outperformed the conventional clinical nomogram in predicting early recurrence.

Does dual-layer spectral detector CT provide added value in predicting spread through air spaces in lung adenocarcinoma? A preliminary study.

OBJECTIVES: To examine the predictive value of dual-layer spectral detector CT (DLCT) for spread through air spaces (STAS) in clinical lung adenocarcinoma. METHODS: A total of 225 lung adenocarcinoma cases were retrospectively reviewed for demographic, clinical, pathological, traditional CT, and spectral parameters. Multivariable logistic regression analysis was carried out based on three logistic models, including a model using traditional CT features (traditional model), a model using spectral parameters (spectral model), and an integrated model combining traditional CT and spectral parameters (integrated model). Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were performed to assess these models. RESULTS: Univariable analysis showed significant differences between the STAS and non-STAS groups in traditional CT features, including nodule density (p < 0.001), pleural indentation types (p = 0.006), air-bronchogram sign (p = 0.031), the presence of spiculation (p < 0.001), long-axis diameter of the entire nodule (LD) (p < 0.001), and consolidation/tumor ratio (CTR) (p < 0.001). Multivariable analysis revealed that LD > 20 mm (odds ratio [OR] = 2.271, p = 0.025) and CTR (OR = 24.208, p < 0.001) were independent predictors in the traditional model, while electronic density (ED) in the venous phase was an independent predictor in the spectral (OR = 1.062, p < 0.001) and integrated (OR = 1.055, p < 0.001) models. The area under the curve (AUC) for the integrated model (0.84) was the highest (spectral model, 0.83; traditional model, 0.80), and the difference between the integrated and traditional models was statistically significant (p = 0.015). DCA showed that the integrated model had superior clinical value versus the traditional model. CONCLUSIONS: DLCT has added value for STAS prediction in lung adenocarcinoma. CLINICAL RELEVANCE STATEMENT: Spectral CT has added value for spread through air spaces prediction in lung adenocarcinoma so may impact treatment planning in the future. KEY POINTS: • Electronic density may be a potential spectral index for predicting spread through air spaces in lung adenocarcinoma. • A combination of spectral and traditional CT features enhances the performance of traditional CT for predicting spread through air spaces.

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma.

OBJECTIVES: To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements. METHODS: A total of 542 patients with clinical stage 0-I peripheral LUAD and with preoperative CT data of 1-mm slice thickness were included. Maximal solid size on axial image (MSSA) was evaluated by two chest radiologists. MSSA, volume of solid component (SV), and mass of solid component (SM) were evaluated by DL. Consolidation-to-tumor ratios (CTRs) were calculated. For ground glass nodules (GGNs), solid parts were extracted with different density level thresholds. The prognosis prediction efficacy of DL was compared with that of manual measurements. Multivariate Cox proportional hazards model was used to find independent risk factors. RESULTS: The prognosis prediction efficacy of T-staging (TS) measured by radiologists was inferior to that of DL. For GGNs, MSSA-based CTR measured by radiologists (RMSSA%) could not stratify RFS and OS risk, whereas measured by DL using 0HU (2D-AIMSSA0HU%) could by using different cutoffs. SM and SV measured by DL using 0 HU (AISM0HU% and AISV0HU%) could effectively stratify the survival risk regardless of different cutoffs and were superior to 2D-AIMSSA0HU%. AISM0HU% and AISV0HU% were independent risk factors. CONCLUSION: DL algorithm can replace human for more accurate T-staging of LUAD. For GGNs, 2D-AIMSSA0HU% could predict prognosis rather than RMSSA%. The prediction efficacy of AISM0HU% and AISV0HU% was more accurate than of 2D-AIMSSA0HU% and both were independent risk factors. CLINICAL RELEVANCE STATEMENT: Deep learning algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma. KEY POINTS: • Deep learning (DL) algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma (LUAD). • For GGNs, maximal solid size on axial image (MSSA)-based consolidation-to-tumor ratio (CTR) measured by DL using 0 HU could stratify survival risk than that measured by radiologists. • The prediction efficacy of mass- and volume-based CTRs measured by DL using 0 HU was more accurate than of MSSA-based CTR and both were independent risk factors.

The investigation of reduced field-of-view diffusion-weighted imaging (DWI) in patients with nasopharyngeal carcinoma: comparison with conventional DWI.

BACKGROUND: Field-of-view optimized and constrained undistorted single-shot imaging (FOCUS) is a new sequence that shows enhanced anatomical details, improving the diffusion-weighted (DW) images. PURPOSE: To investigate the value of FOCUS diffusion-weighted imaging (DWI) in the evaluation of nasopharyngeal carcinoma (NPC) and compare it with the single-shot echo planner imaging (SS-EPI) DWI approach. MATERIAL AND METHODS: A total of 87 patients with NPC underwent magnetic resonance imaging, including FOCUS and SS-EPI DWI sequences. The signal-to-noise ratio (SNR), signal-intensity ratio (SIR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values of the nasopharyngeal lesions were measured and compared. According to the clinical stages of patients, T and N were divided into early and advanced stage groups, respectively. The mean ADC values of the two techniques were computed, and the area under the curve (AUC) was estimated to calculate the diagnostic efficiency. RESULTS: Subjective and objective image qualitative values of FOCUS were significantly higher than those of SS-EPI. The ADC values for FOCUS of early T and N stages were significantly lower than those of the advanced stages. CONCLUSION: FOCUS provides significantly better image quality in NPC compared to SS-EPI, with lower ADC values for early-stage disease than late-stage disease.

A Noninvasive Tool Based on Magnetic Resonance Imaging Radiomics for the Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.

PURPOSE: This study aimed to identify patients with pathological complete response (pCR) and make better clinical decisions by constructing a preoperative predictive model based on tumoral and peritumoral volumes of multiparametric magnetic resonance imaging (MRI) obtained before neoadjuvant chemotherapy (NAC). METHODS: This study investigated MRI before NAC in 448 patients with nonmetastatic invasive ductal breast cancer (Sun Yat-sen Memorial Hospital, Sun Yat-sen University, n = 362, training cohort; and Sun Yat-sen University Cancer Center, n = 86, validation cohort). The tumoral and peritumoral volumes of interest (VOIs) were segmented and MRI features were extracted. The radiomic features were filtered via a random forest algorithm, and a supporting vector machine was used for modeling. The receiver operator characteristic curve and area under the curve (AUC) were calculated to assess the performance of the radiomics-based classifiers. RESULTS: For each MRI sequence, a total of 863 radiomic features were extracted and the top 30 features were selected for model construction. The radiomic classifiers of tumoral VOI and peritumoral VOI were both promising for predicting pCR, with AUCs of 0.96 and 0.97 in the training cohort and 0.89 and 0.78 in the validation cohort, respectively. The tumoral + peritumoral VOI radiomic model could further improve the predictive accuracy, with AUCs of 0.98 and 0.92 in the training and validation cohorts. CONCLUSIONS: The tumoral and peritumoral multiparametric MRI radiomics model can promisingly predict pCR in breast cancer using MRI images before surgery. Our results highlighted the potential value of the tumoral and peritumoral radiomic model in cancer management.