Survival Benefit of Lenvatinib Plus PD-1 Inhibitor with or Without HAIC in Advanced Hepatocellular Carcinoma Beyond Oligometastasis: a Multicenter Cohort Study.

Purpose: The outcome between Lenvatinib plus programmed cell death protein-1 (PD-1) inhibitor and Lenvatinib in HCC beyond oligometastasis was unclear. In this multicenter, we compared the prognosis of Lenvatinib plus PD-1 inhibitor with Lenvatinib in HCC beyond oligometastasis. Patients and Methods: A total of 296 patients from six institutions were included. The patients were divided into two groups: (a) concurrent Lenvatinib plus PD-1 inhibitor treatment (Len+PD-1 group) and (b) Lenvatinib monotherapy (Len group). The primary endpoint was overall survival (OS), the second endpoint was progression-free survival (PFS) and efficacy. Results: The median OS was 20.1 ± 1.2 (17.7-22.5) months and 15.7 ± 1.5 (12.8-18.6) months in the Len+PD-1 and Len groups, respectively. The 12-, 24-, and 36-month OS rates were 79.1%, 39.4%, and 10.7% in the Len+PD-1 group, and 76.3%, 29.7%, and 0% in the Len group, respectively. The OS and PFS rates of the Len+PD-1 group were significantly longer compared with the Len group (hazard ratio [HR], 0.88; 95% confidence index [CI], 0.49-0.94; P = 0.021) and (HR, 0.66; 95% CI, 0.50-0.87; P = 0.003). A subgroup analysis revealed that OS (HR, 0.57; 95% CI, 0.36-0.90; P = 0.016) was improved between the Len+PD-1 and Len groups with hepatic artery infusion chemotherapy (HAIC) treatment, whereas OS (HR, 1.11; 95% CI, 0.68-1.80; P = 0.689) was similar between the Len and Len+PD-1 groups without HAIC. Conclusion: Lenvatinib combined with PD-1 inhibitor significantly improves the survival of HCC beyond oligometastasis. For patients with HAIC, there was obviously significance between Len and Len+PD-1 groups.

Lenvatinib as one of system therapy, is recommended treatment for HCC with multimetastases. The LEAP-002 trial, which evaluated Lenvatinib combined with Pembrolizumab exhibited improved progression-free survival (PFS) and overall survival (OS) compared with Lenvatinib alone. However, the combination efficacy on HCC beyond oligometastasis is unknown. In this multicenter study, we found that Lenvatinib combined with PD-1 inhibitor significantly improved both the OS and PFS and this combination could be recommended for HCC beyond oligometastases. OS and PFS were improved in the Len+PD-1 versus the Len group with hepatic artery infusion chemotherapy (HAIC) treatment, whereas the OS and PFS were similar between the Len and Len+PD-1 groups without HAIC. We provided clinical value that HAIC could be recommended as an effective local therapy to improve the prognosis for advanced HCC.

Hepatic Arterial Infusion Chemotherapy Combined Lenvatinib and PD-1 Inhibitor Showed Improved Survival for Infiltrative Hepatocellular Carcinoma: A Multicenter Cohort Study.

Purpose: Lenvatinib and programmed cell death protein-1 (PD-1) inhibitor on infiltrative hepatocellular carcinoma (HCC) have obtained demonstrated efficacy and still need improvement. Hepatic arterial infusion chemotherapy (HAIC) has shown promising results for advanced HCC. This study aimed to compare the efficacy of HAIC combined Lenvatinib and PD-1 inhibitor versus Lenvatinib combined PD-1 inhibitor for infiltrative HCC. Patients and Methods: A total of 232 patients were enrolled. There were 114 patients received Lenvatinib combined PD-1 inhibitor (Len+PD-1 group) and 118 patients received HAIC combined Lenvatinib and PD-1 inhibitor (HAIC+Len+PD-1 group). Overall survival (OS), progression-free survival (PFS) and safety of patients were compared between the two groups by propensity score-matching (PSM). Results: The 6-, 12-, and 24-month OS rates were 93.8%, 65.1% and 13.4% in Len+PD-1 group, and 100%, 77.3% and 32.1% in HAIC+Len+PD-1 group, respectively. The 3-, 6-, and 12-month PFS rates were 86.4%, 45.7% and 14.1% in Len+PD-1 group, and 95.1%, 59.3% and 25.9% in HAIC+Len+PD-1 group, respectively. The HAIC+Len+PD-1 group had obviously better survival than the Len+PD-1 group both in OS (P=0.002) and PFS (P=0.004). Subgroup analysis revealed that OS in patients with metastasis was improved with HAIC+Len+PD-1 treatment. Patients with alpha-fetoprotein (AFP) response after treatment showed better survival than the non-response. In addition, HAIC+Len+PD-1 group showed manageable adverse events (AEs). Conclusion: Patient with infiltrative HCC, HAIC+Len+PD-1 treatment had longer OS and PFS than Len+PD-1 treatment. Early AFP response was an effective indicator of better survival and tumor response to therapy.

Infiltrative hepatocellular carcinoma (HCC) is an odd group that is not well adjudicated in the current staging systems, and treatment options for patients with infiltrative HCC are challenging with scant and insufficient clinical evidence. In this multi-center study, we innovatively analyzed the outcome of hepatic arterial infusion chemotherapy (HAIC) combined lenvatinib and PD-1 inhibitor (HAIC+Len+PD-1) was associated longer progression-free survival and overall survival than Lenvatinib plus PD-1 inhibitor combination (Len+PD-1) for patient with infiltrative HCC. In addition, further intragroup analysis revealed that OS of patients with and without metastasis in Len+PD-1 group was significant difference. However, no difference was observed in OS for patients with and without metastasis in HAIC+Len+PD-1 group. Patients with alpha-fetoprotein (AFP) response after treatment showed better survival than the non-response. Our research provides evidence that HAIC combined Lenvatinib and PD-1 inhibitor results in clinically significant improvements in infiltrative HCC. It could be recommended as a first choice for infiltrative HCC therapy.

Addition of Immune Checkpoint Inhibitor Showed Better Efficacy for Infiltrative Hepatocellular Carcinoma Receiving Hepatic Arterial Infusion Chemotherapy and Lenvatinib: A Multicenter Retrospective Study.

Purpose: The prognosis of infiltrative hepatocellular carcinoma (HCC) is dismal. Hepatic arterial infusion chemotherapy (HAIC) plus Lenvatinib (Len) and immune checkpoint inhibitor (ICI) have shown promising results for HCC. However, this three combination therapy on infiltrative HCC is unknown. In this study, we compared HAIC plus lenvatinib (Len) and programmed cell death protein-1 (PD-1) inhibitor with HAIC plus Len for infiltrative HCC. Patients and Methods: This multi-center cohort study included patients with infiltrative HCC who received HAIC combined with Len (HAIC+Len group, n = 173) or HAIC combined with Len and PD-1 inhibitor (HAIC+Len+ICI group, n = 128) as the first-line treatment from January 2019 to December 2021. To balance any intergroup differences, one-to-one propensity score matching (PSM) was applied. Overall survival (OS) and progression-free survival (PFS) were compared between the two groups. Results: After PSM, the median OS was 14.1 ± 1.0 and 16.1 ± 1.4 months in the HAIC+Len and HAIC+Len+ICI groups, respectively. The median PFS was 4.6 ± 0.4 months in the HAIC+Len group and 7.5 ± 0.8 months in the HAIC+Len+ICI group. The HAIC+Len+ICI group showed significantly better OS (hazard ratio [HR], 0.66; 95% CI, 0.49-0.90; P = 0.008) and PFS (HR, 0.53; 95% confident index [CI], 0.40-0.70; P < 0.001) compared with the HAIC+Len group. Subgroup analysis revealed that for OS in HCC without metastasis, the addition of PD-1 inhibitor was not significant (HR, 0.68; 95% CI, 0.43-1.07; P = 0.091). No difference was observed in OS between low (2-3 cycles) and high (4-6 cycles) level of HAIC cycles (HR, 0.99; 95% CI, 0.67-1.44; P = 0.938). Conclusion: The HAIC+Len+ICI group had a longer PFS and OS compared with the HAIC+Len group, demonstrating an acceptable safety profile. This triple combination strategy may be an alternative treatment for infiltrative HCC management.

The evidence of HAIC plus Len and PD-1 inhibitors for infiltrative HCC is limited. There was no study to evaluate the efficacy of HAIC combined with Len and PD-1 inhibitors for infiltrative HCC. In this study, we found that HAIC plus Len and PD-1 inhibitor (HAIC+Len+ICI) was associated with longer progression-free survival and overall survival than HAIC plus Len combination (HAIC+Len) for patient with infiltrative HCC. In addition, OS in patients with metastasis was improved with HAIC+Len+ICI treatment. OS in patients without metastasis, addition of PD-1 inhibitor after HAIC and Len was not beneficial. What's more, three cycles of HAIC are adequate, especially for patients with high tumor burden, especially with main branch portal vein tumor thrombus (PVTT). Our research provides new evidence that HAIC+Len+ICI treatment significantly improved the OS and PFS of infiltrative HCC patients compared with those who received HAIC+Len treatment. It provides a strong reference for clinical treatment.

Magnetic Resonance Imaging Radiomics Predicts Histological Response to Neoadjuvant Chemotherapy in Localized High-grade Osteosarcoma of the Extremities.

RATIONALE AND OBJECTIVES: Research involving radiomics models based on magnetic resonance imaging (MRI) has mainly used radiomics features derived from a single MRI sequence at a single time point to develop predictive models. This study aimed to construct radiomics models based on before and after neoadjuvant chemotherapy (NAC) MRI for predicting the histological response to NAC in patients with high-grade osteosarcoma. MATERIALS AND METHODS: We included 109 patients with localized high-grade osteosarcomas of the extremities, who underwent pre- and post-NAC MRI examinations, from which radiomics features were extracted. According to the tumor necrosis rate, all patients were classified as good responders (GRs) or poor responders (PRs) and were randomly allocated into training and test sets at a 7:3 ratio. Radiomics features were extracted from T2-weighted (T2WI) and contrast-enhanced T1-weighted imaging (T1CE) of the two MRI scans to construct three models: pre-NAC, post-NAC, and combined pre-NAC and post-NAC (combined model). RESULTS: In total, 1175 radiomics features were extracted from each sequence. Following feature selection, nine radiomics features were selected for each model to construct radiomics signatures. The radiomics signatures of the pre-NAC, post-NAC, and combined models demonstrated good predictive performance for chemotherapy response in osteosarcoma. The combined model achieved the highest areas under the receiver operating curve (AUC) values of 0.999 and 0.915 in the training and test sets, respectively. The AUCs of the post-NAC model were higher than those of the pre-NAC model. CONCLUSION: MRI-based radiomics models demonstrate excellent performance in predicting the histological response to NAC in patients with high-grade osteosarcoma.

Laparoscopic indocyanine green fluorescence imaging technique in rare type II cystic biliary atresia.

In recent years, it has been reported that indocyanine green can be used for intraoperative navigation in Kasai surgery. However, there are no reports of its application in surgery for rare type II cystic biliary atresia. We report a girl presented with jaundice and light-colored stools. Laboratory tests showed impaired liver function with elevated serum bilirubin and bile acid levels. The abdominal ultrasound and MRCP suggested a common hepatic duct cyst. A diagnosis of choledochal cyst was suspected and biliary atresia could not be excluded. Conservative treatment was unsatisfactory. Laparoscopic exploration with indocyanine green fluorescence was performed on the 38th day of her life, and intraoperative diagnosis of type II CBA was made because the common hepatic duct cyst and its downstream anatomical structures did not show fluorescence. The postoperative bilirubin and bile acid levels decreased significantly and she was discharged two weeks after surgery. This result suggests that indocyanine green can be safely used in laparoscopic surgery for type II CBA, which not only helps in the differential diagnosis of CBA and choledochal cyst, but also confirms bile flow in real time.

Lung nodule false positive reduction using a central attention convolutional neural network on imbalanced data.

Computer-aided detection systems for lung nodules play an important role in the early diagnosis and treatment process. False positive reduction is a significant component in pulmonary nodule detection. To address the visual similarities between nodules and false positives in CT images and the problem of two-class imbalanced learning, we propose a central attention convolutional neural network on imbalanced data (CACNNID) to distinguish nodules from a large number of false positive candidates. To solve the imbalanced data problem, we consider density distribution, data augmentation, noise reduction, and balanced sampling for making the network well-learned. During the network training, we design the model to pay high attention to the central information and minimize the influence of irrelevant edge information for extracting the discriminant features. The proposed model has been evaluated on the public dataset LUNA16 and achieved a mean sensitivity of 92.64%, specificity of 98.71%, accuracy of 98.69%, and AUC of 95.67%. The experimental results indicate that our model can achieve satisfactory performance in false positive reduction.

Indocyanine green fluorescence imaging localization-assisted thoracoscopy revision surgery after repair of esophageal atresia.

Revision surgery for the complications after repair of esophageal atresia is often complex because of previous surgeries and chest infections and thus requires surgical expertise. This study describes surgical experiences with the use of indocyanine green (ICG) fluorescence imaging localization-assisted thoracoscopy during revision surgery, including recurrent tracheoesophageal fistula (rTEF) (8 cases, one of which was esophageal-pulmonary fistula) and delayed esophageal closure (1 case). We performed fistula repair and esophageal reconstruction according to the indications of ICG. The application of this method avoids the excessive trauma caused by freeing the trachea and esophagus. Contrast imaging taken one week and one month after surgery indicated no spillover of the contrast agent from the esophagus, except in 1 case. Indocyanine green fluorescence imaging localization-assisted thoracoscopy is worth promoting for revision surgery after esophageal atresia repair.

Prognostic role of multiparameter MRI and radiomics in progression of advanced unresectable hepatocellular carcinoma following combined transcatheter arterial chemoembolization and lenvatinib therapy.

BACKGROUND: Current study aims to determine the prognostic value of Multiparameter MRI after combined Lenvatinib and TACE therapy in patients with advanced unresectable hepatocellular carcinoma (HCC). METHODS: A total of 61 HCC patients with pre-treatment Multiparameter MRI in Sun Yat-sen University Cancer Center from January 2019 to March 2021 were recruited in the current study. All patients received combined Lenvatinib and TACE treatment. Potential clinical and imaging risk factors for disease progression were analyzed using Cox regression model. Each patient extracts signs from the following 7 sequences: T1WI, T1WI arterial phase, T1WI portal phase, T1WI delay phase, T2WI, DWI (b = 800), ADC.1782 quantitative 3D radiomic features were extracted for each sequence, A random forest algorithm is used to select the first 20 features by feature importance. 7 logit regression-based prediction model was built for seven sequences based on the selected features and fivefold cross validation was used to evaluate the performance of each model. RESULTS: CR, PR, SD were reported in 14 (23.0%), 35 (57.4%) and 7 (11.5%) patients, respectively. In multivariate analysis, tumor number (hazard ratio, HR = 4.64, 95% CI 1.03-20.88), and arterial phase intensity enhancement (HR = 0.24, 95% CI 0.09-0.64; P = 0.004) emerged as independent risk factors for disease progression. In addition to clinical factors, the radiomics signature enhanced the accuracy of the clinical model in predicting disease progression, with an AUC of 0.71, a sensitivity of 0.99%, and a specificity of 0.95. CONCLUSION: Radiomic signatures derived from pretreatment MRIs could predict response to combined Lenvatinib and TACE therapy. Furthermore, it can increase the accuracy of a combined model for predicting disease progression. In order to improve clinical outcomes, clinicians may use this to select an optimal treatment strategy and develop a personalized monitoring protocol.

Dynamic Behavior of the Structural Phase Transition of Hydrogel Formation Induced by Temperature Ramp and Addition of Ibuprofen.

Understanding the dynamic behavior of hydrogel formation induced by a temperature ramp is essential for the design of gel-based injectable formulation as drug-delivery vehicles. In this study, the dynamic behavior of the hydrogel formation of Pluronic F108 aqueous solutions within different heating rates was explored in both macroscopic and microscopic views. It was discovered that when the heating rate is increased, the gelation temperature window (hard gel region) shrinks and the mechanical strength of the hydrogel decreases. A given system at different heating rates would lead to different crystalline structural evolutions. The time-resolved small-angle X-ray scattering (SAXS) experiments at a heating rate of 10 °C/min disclose that the crystalline structure of micelle packing in the hydrogel exhibits a series of transitions: hexagonal close-packed (HCP) to face-centered cubic (FCC) and body-centered cubic (BCC) structures coexisting and then to the BCC structure along with the increasing temperature. For the system at equilibrium, the BCC structure exclusively dominates the system. Furthermore, the addition of a hydrophobic model drug (ibuprofen) to the F108 aqueous solution promotes hard gel formation at even lower temperatures and concentrations of F108. The SAXS results for the system with ibuprofen at a heating rate of 10 °C/min demonstrate a mixture of FCC and BCC structures coexisting over the whole gelation window compared to the BCC structure that exclusively dominates the system at equilibrium. The addition of ibuprofen would alter the structural evolution to change the delivery path of the encapsulated drug, which is significantly related to the performance of drug release.

Automatic recognition of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNNs.

Ground-glass opacity (GGO) is a common CT imaging sign on high-resolution CT, which means the lesion is more likely to be malignant compared to common solid lung nodules. The automatic recognition of GGO CT imaging signs is of great importance for early diagnosis and possible cure of lung cancers. The present GGO recognition methods employ traditional low-level features and system performance improves slowly. Considering the high-performance of CNN model in computer vision field, we proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling is performed on multi-views and multi-receptive fields, which reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has the ability to obtain the optimal fine-tuning model. Multi-CNN models fusion strategy obtains better performance than any single trained model. We evaluated our method on the GGO nodule samples in publicly available LIDC-IDRI dataset of chest CT scans. The experimental results show that our method yields excellent results with 96.64% sensitivity, 71.43% specificity, and 0.83 F1 score. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images. Graphical abstract We proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has ability to obtain the optimal fine-tuning model. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images.