Delta-radiomics models based on multi-phase contrast-enhanced magnetic resonance imaging can preoperatively predict glypican-3-positive hepatocellular carcinoma.

Objectives: The aim of this study is to investigate the value of multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) based on the delta radiomics model for identifying glypican-3 (GPC3)-positive hepatocellular carcinoma (HCC). Methods: One hundred and twenty-six patients with pathologically confirmed HCC (training cohort: n = 88 and validation cohort: n = 38) were retrospectively recruited. Basic information was obtained from medical records. Preoperative multi-phase CE-MRI images were reviewed, and the 3D volumes of interest (VOIs) of the whole tumor were delineated on non-contrast T1-weighted imaging (T1), arterial phase (AP), portal venous phase (PVP), delayed phase (DP), and hepatobiliary phase (HBP). One hundred and seven original radiomics features were extracted from each phase, and delta-radiomics features were calculated. After a two-step feature selection strategy, radiomics models were built using two classification algorithms. A nomogram was constructed by combining the best radiomics model and clinical risk factors. Results: Serum alpha-fetoprotein (AFP) (p = 0.013) was significantly related to GPC3-positive HCC. The optimal radiomics model is composed of eight delta-radiomics features with the AUC of 0.805 and 0.857 in the training and validation cohorts, respectively. The nomogram integrated the radiomics score, and AFP performed excellently (training cohort: AUC = 0.844 and validation cohort: AUC = 0.862). The calibration curve showed good agreement between the nomogram-predicted probabilities and GPC3 actual expression in both training and validation cohorts. Decision curve analysis further demonstrates the clinical practicality of the nomogram. Conclusion: Multi-phase CE-MRI based on the delta-radiomics model can non-invasively predict GPC3-positive HCC and can be a useful method for individualized diagnosis and treatment.

Diffusion-Weighted Imaging as a Quantitative Imaging Biomarker for Predicting Proliferation Rate in Hepatocellular Carcinoma: Developing a Radiomics Nomogram.

PURPOSE: This study aimed to explore the predictive performance of diffusion-weighted imaging with apparent diffusion coefficient map in predicting the proliferation rate of hepatocellular carcinoma and to develop a radiomics-based nomogram. METHODS: This was a single-center retrospective study. A total of 110 patients were enrolled. The sample included 38 patients with low Ki67 expression (Ki67 ≤10%) and 72 with high Ki67 expression (Ki67 >10%) as demonstrated by surgical pathology. Patients were randomly divided into either a training (n = 77) or validation (n = 33) cohort. Diffusion-weighted imaging with apparent diffusion coefficient maps was used to extract radiomic features and the signal intensity values of tumor (SI tumor ), normal liver (SI liver ), and background noise (SI background ) from all samples. Subsequently, the clinical model, radiomic model, and fusion model (with clinical data and radiomic signature) were developed and validated. RESULTS: The area under the curve (AUC) of the clinical model for predicting the Ki67 expression including serum α-fetoprotein level ( P = 0.010), age ( P = 0.015), and signal noise ratio ( P = 0.026) was 0.799 and 0.715 in training and validation cohorts, respectively. The AUC of the radiomic model constructed by 9 selected radiomic features was 0.833 and 0.772 in training and validation cohorts, respectively. The AUC of the fusion model containing serum α-fetoprotein level ( P = 0.011), age ( P = 0.019), and rad score ( P < 0.001) was 0.901 and 0.781 in training and validation cohorts, respectively. CONCLUSIONS: Diffusion-weighted imaging as a quantitative imaging biomarker can predict Ki67 expression level in hepatocellular carcinoma across various models.

Clinic-radiological features and radiomics signatures based on Gd-BOPTA-enhanced MRI for predicting advanced liver fibrosis.

OBJECTIVES: To develop and validate a combined model based on Gd-BOPTA-enhanced MRI to identify advanced liver fibrosis. METHODS: A total of 102 patients with chronic HBV infection were divided into a training cohort (n = 80) and a time-independent testing cohort 1 (n = 22). In the training cohort, radiomics signatures were extracted from the hepatobiliary phase. Model 1 was constructed with clinic-radiological factors using multivariable logistic regression to predict advanced liver fibrosis, and model 2 incorporated radiomics signatures based on model 1. The diagnostic performances were compared with serum fibrosis tests and FibroScan tests using area under curve (AUC) in testing cohort 1. Another 45 patients with other causes were collected in testing cohort 2 for further validation. RESULTS: Model 1 showed age (OR = 1.079) and periportal space widening (OR = 7.838) were the independent factors for predicting advanced fibrosis. After integrating radiomics signatures, model 2 enabled more accurately than model 1 in training cohort (0.940 vs. 0.802, p = 0.003). In testing cohort 1, model 2 demonstrated a superior AUC compared with model 1 (0.900 vs. 0.813，p = 0.131), FibroScan test (0.900 vs. 0.733, p = 0.193), and serum fibrosis tests (APRI and Fib-4 was 0.667 and 0.791). In testing cohort 2, model 2 incorporating radiomics signatures showed satisfactory performance (0.874 vs. 0.757，p = 0.010) compared with model 1. CONCLUSIONS: Radiomics signatures derived from Gd-BOPTA-enhanced HBP images may offer complementary information to the clinic-radiological model for predicting advanced liver fibrosis. KEY POINTS: • Linear or reticular hyperintensity on T2WI, periportal space widening, and diffuse periportal enhancement on HBP can be useful for predicting advanced liver fibrosis. • Clinic-radiological features such as patient age and periportal space widening are the two independent factors predicting advanced fibrosis. • Radiomics signatures derived from Gd-BOPTA-enhanced HBP images offer complementary information to the clinic-radiological model for predicting advanced liver fibrosis.

Radiomic model to predict the expression of PD-1 and overall survival of patients with ovarian cancer.

BACKGROUND: Programmed cell death 1 (PD-1), encoded by programmed cell death protein 1 (PDCD1), is widely investigated in clinical trials. We aimed to develop a radiomic model to discriminate its expression levels patients with ovarian cancer (OC) and explore its prognostic value. METHODS: Computed tomography (CT) images with the corresponding sequencing data and clinicopathological features were used. The volumes of interest were manually delineated. After extraction and normalization, the radiomic features were screened using repeat least absolute shrinkage and selection operator. A radiomic model for PD-1 prediction, radiomic score (rad_score), was developed using logistic regression and validated via internal 5-fold cross-validation. The Kaplan-Meier curves, COX proportional hazards model, and landmark analysis were used for survival analysis. RESULTS: The mRNA level of PDCD1 significantly affects the overall survival (OS) of OC patients. The rad_score for PDCD1 prediction was based on four features and was significantly correlated with other genes involved in T-cell exhaustion and immune checkpoint molecules. The areas under the receiver operating characteristic curves reached 0.810 and 0.772 in the training and validation datasets, respectively. The calibration curves and decision curve analysis proved the model's fitness and clinical benefits. Patients with higher rad_score had poorer OS (P < 0.001, 0.031, 0.014, 0.01, and < 0.001, after landmark of 12 months, before and after landmark of 36 months, and before and after landmark of 60 months, respectively). CONCLUSIONS: The radiomic signature from CT images can discriminate the PD-1 expression status and OC prognosis, which is correlated with T-cell exhaustion.

Radiomic analysis based on multi-phase magnetic resonance imaging to predict preoperatively microvascular invasion in hepatocellular carcinoma.

BACKGROUND: The prognosis of hepatocellular carcinoma (HCC) remains poor and relapse occurs in more than half of patients within 2 years after hepatectomy. In terms of recent studies, microvascular invasion (MVI) is one of the potential predictors of recurrence. Accurate preoperative prediction of MVI is potentially beneficial to the optimization of treatment planning. AIM: To develop a radiomic analysis model based on pre-operative magnetic resonance imaging (MRI) data to predict MVI in HCC. METHODS: A total of 113 patients recruited to this study have been diagnosed as having HCC with histological confirmation, among whom 73 were found to have MVI and 40 were not. All the patients received preoperative examination by Gd-enhanced MRI and then curative hepatectomy. We manually delineated the tumor lesion on the largest cross-sectional area of the tumor and the adjacent two images on MRI, namely, the regions of interest. Quantitative analyses included most discriminant factors (MDFs) developed using linear discriminant analysis algorithm and histogram analysis with MaZda software. Independent significant variables of clinical and radiological features and MDFs for the prediction of MVI were estimated and a discriminant model was established by univariate and multivariate logistic regression analysis. Prediction ability of the above-mentioned parameters or model was then evaluated by receiver operating characteristic (ROC) curve analysis. Five-fold cross-validation was also applied via R software. RESULTS: The area under the ROC curve (AUC) of the MDF (0.77-0.85) outperformed that of histogram parameters (0.51-0.74). After multivariate analysis, MDF values of the arterial and portal venous phase, and peritumoral hypointensity in the hepatobiliary phase were identified to be independent predictors of MVI (P < 0.05). The AUC value of the model was 0.939 [95% confidence interval (CI): 0.893-0.984, standard error: 0.023]. The result of internal five-fold cross-validation (AUC: 0.912, 95%CI: 0.841-0.959, standard error: 0.0298) also showed favorable predictive efficacy. CONCLUSION: Noninvasive MRI radiomic model based on MDF values and imaging biomarkers may be useful to make preoperative prediction of MVI in patients with primary HCC.

Multi-Region Radiomic Analysis Based on Multi-Sequence MRI Can Preoperatively Predict Microvascular Invasion in Hepatocellular Carcinoma.

Objectives: Microvascular invasion (MVI) affects the postoperative prognosis in hepatocellular carcinoma (HCC) patients; however, there remains a lack of reliable and effective tools for preoperative prediction of MVI. Radiomics has shown great potential in providing valuable information for tumor pathophysiology. We constructed and validated radiomics models with and without clinico-radiological factors to predict MVI. Methods: One hundred and fifteen patients with pathologically confirmed HCC (training set: n = 80; validation set: n = 35) who underwent preoperative MRI were retrospectively recruited. Radiomics models based on multi-sequence MRI across various regions (including intratumoral and/or peritumoral areas) were built using four classification algorithms. A clinico-radiological model was constructed individually and combined with a radiomics model to generate a fusion model by multivariable logistic regression. Results: Among the radiomics models, the model based on T2WI and arterial phase (T2WI-AP model) in the volume of the liver-HCC interface (VOIinterface) exhibited the best predictive power, with AUCs of 0.866 in the training group and 0.855 in the validation group. The clinico-radiological model exhibited good efficacy (AUC: 0.819 and 0.717, respectively). The fusion model showed excellent predictive ability (AUC: 0.915 and 0.868, respectively), outperforming both the clinico-radiological and the T2WI-AP models in the training and validation sets. Conclusion: The fusion model of multi-region radiomics achieves an enhanced prediction of the individualized risk estimation of MVI in HCC patients. This may be a beneficial tool for clinicians to improve decision-making in personalized medicine.

Continual Decline in Azole Susceptibility Rates in Candida tropicalis Over a 9-Year Period in China.

BACKGROUND: There have been reports of increasing azole resistance in Candida tropicalis, especially in the Asia-Pacific region. Here we report on the epidemiology and antifungal susceptibility of C. tropicalis causing invasive candidiasis in China, from a 9-year surveillance study. METHODS: From August 2009 to July 2018, C. tropicalis isolates (n = 3702) were collected from 87 hospitals across China. Species identification was carried out by mass spectrometry or rDNA sequencing. Antifungal susceptibility was determined by Clinical and Laboratory Standards Institute disk diffusion (CHIF-NET10-14, n = 1510) or Sensititre YeastOne (CHIF-NET15-18, n = 2192) methods. RESULTS: Overall, 22.2% (823/3702) of the isolates were resistant to fluconazole, with 90.4% (744/823) being cross-resistant to voriconazole. In addition, 16.9 (370/2192) and 71.7% (1572/2192) of the isolates were of non-wild-type phenotype to itraconazole and posaconazole, respectively. Over the 9 years of surveillance, the fluconazole resistance rate continued to increase, rising from 5.7 (7/122) to 31.8% (236/741), while that for voriconazole was almost the same, rising from 5.7 (7/122) to 29.1% (216/741), with no significant statistical differences across the geographic regions. However, significant difference in fluconazole resistance rate was noted between isolates cultured from blood (27.2%, 489/1799) and those from non-blood (17.6%, 334/1903) specimens (P-value < 0.05), and amongst isolates collected from medical wards (28.1%, 312/1110) versus intensive care units (19.6%, 214/1092) and surgical wards (17.9%, 194/1086) (Bonferroni adjusted P-value < 0.05). Although echinocandin resistance remained low (0.8%, 18/2192) during the surveillance period, it was observed in most administrative regions, and one-third (6/18) of these isolates were simultaneously resistant to fluconazole. CONCLUSION: The continual decrease in the rate of azole susceptibility among C. tropicalis strains has become a nationwide challenge in China, and the emergence of multi-drug resistance could pose further threats. These phenomena call for effective efforts in future interventions.

A Radiomics Nomogram for Preoperative Prediction of Early Recurrence of Small Hepatocellular Carcinoma After Surgical Resection or Radiofrequency Ablation.

BACKGROUND: Patients with small hepatocellular carcinoma (HCC) (3 cm) still have a poor prognosis. The purpose of this study was to develop a radiomics nomogram to preoperatively predict early recurrence (ER) (2 years) of small HCC. METHODS: The study population included 111 patients with small HCC who underwent surgical resection (SR) or radiofrequency ablation (RFA) between September 2015 and September 2018 and were followed for at least 2 years. Radiomic features were extracted from the entire tumor by using the MaZda software. The least absolute shrinkage and selection operator (LASS0) method was applied for feature selection, and radiomics signature construction. A rad-score was then calculated. Multivariable logistic regression analysis was used to establish a prediction model including independent clinical risk factors, radiologic features and rad-score, which was ultimately presented as a radiomics nomogram. The predictive ability of the nomogram was evaluated using the area under the receiver operating characteristic (ROC) curve and internal validation was performed via bootstrap resampling and 5-fold cross-validation method. RESULTS: A total of 53 (53/111, 47.7%) patients had confirmed ER according to the final clinical outcomes. In univariate logistic regression analysis, cirrhosis and hepatitis B infection (P=0.015 and 0.083, respectively), hepatobiliary phase hypointensity (P=0.089), Child-Pugh score (P=0.083), the preoperative platelet count (P=0.003), and rad-score (P<0.001) were correlated with ER. However, after multivariate logistic regression analysis, only the preoperative platelet count and rad-score were included as predictors in the final model. The area under ROC curve (AUC) of the radiomics nomogram to predict ER of small HCC was 0.981 (95% CI: 0.957, 1.00), while the AUC verified by bootstrap is 0.980 (95% CI: 0.962, 1.00), indicating the goodness-of-fit of the final model. CONCLUSIONS: The radiomics nomogram containing the clinical risk factors and rad-score can be used as a quantitative tool to preoperatively predict individual probability of ER of small HCC.

A radiomics nomogram based on contrast-enhanced MRI for preoperative prediction of macrotrabecular-massive hepatocellular carcinoma.

BACKGROUND: Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) represents an aggressive form of hepatocellular carcinoma and is associated with poor survival outcomes. AIMS: This study aimed to develop a radiomics nomogram based on contrast-enhanced MRI for preoperative prediction of MTM-HCC. METHODS: This study enrolled 88 patients with histologically confirmed HCC, including 32 MTM-HCCs and 56 Non-MTM-HCCs. The clinical and gadobenate dimeglumine (Gd)-enhanced MRI features were retrospectively reviewed by two abdominal radiologists. The regions of interest (ROIs) on the largest cross-sectional image and two adjacent images of the tumor, from which radiomics features were extracted via MaZda software and a radiomics score (Rad-score) was calculated via Python software. Combined with the Rad-score and independent imaging factors, a radiomics nomogram was constructed using R software. Nomogram performance was estimated with calibration curve. RESULTS: A total of eleven top weighted radiomics features were selected among five sequences of MR images. There was a significant difference in Rad-score between MTM-HCC and non-MTM-HCC patients (P < 0.001), where patients with MTM-HCC generally had higher Rad-scores (absolute value). After multivariate analysis, radiomics score (OR = 7.794, P < 0.001) and intratumor fat (OR = 9.963, P = 0.014) were determined as independent predictors associated with MTM-HCC. The area under the receiver operating characteristic (ROC) curve of the selected model was 0.813 (95% CI 0.714-0.912) and the optimal cutoff value was 0.60. The nomogram showed overall satisfactory prediction performance (AUC = 0.785 [95% CI 0.684-0.886]). CONCLUSIONS: A contrast-enhanced MRI-based radiomics nomogram may be useful for preoperative prediction of MTM-HCC in primary HCC patients, allowing opportunity to improve the treatment course and patient outcomes.