Prediction of 5-year progression-free survival in advanced nasopharyngeal carcinoma with pretreatment PET/CT using multi-modality deep learning-based radiomics

ObjectiveDeep learning-based radiomics (DLR) has achieved great success in medical image analysis and has been considered a replacement for conventional radiomics that relies on handcrafted features. In this study, we aimed to explore the capability of DLR for the prediction of 5-year progression-free survival (PFS) in advanced nasopharyngeal carcinoma (NPC) using pretreatment PET/CT images.MethodsA total of 257 patients (170/87 patients in internal/external cohorts) with advanced NPC (TNM stage III or IVa) were enrolled. We developed an end-to-end multi-modality DLR model, in which a 3D convolutional neural network was optimized to extract deep features from pretreatment PET/CT images and predict the probability of 5-year PFS. The TNM stage, as a high-level clinical feature, could be integrated into our DLR model to further improve the prognostic performance. For a comparison between conventional radiomics and DLR, 1,456 handcrafted features were extracted, and optimal conventional radiomics methods were selected from 54 cross-combinations of six feature selection methods and nine classification methods. In addition, risk group stratification was performed with clinical signature, conventional radiomics signature, and DLR signature.ResultsOur multi-modality DLR model using both PET and CT achieved higher prognostic performance (area under the receiver operating characteristic curve (AUC) = 0.842 ± 0.034 and 0.823 ± 0.012 for the internal and external cohorts) than the optimal conventional radiomics method (AUC = 0.796 ± 0.033 and 0.782 ± 0.012). Furthermore, the multi-modality DLR model outperformed single-modality DLR models using only PET (AUC = 0.818 ± 0.029 and 0.796 ± 0.009) or only CT (AUC = 0.657 ± 0.055 and 0.645 ± 0.021). For risk group stratification, the conventional radiomics signature and DLR signature enabled significant difference between the high- and low-risk patient groups in both the internal and external cohorts (p < 0.001), while the clinical signature failed in the external cohort (p = 0.177).ConclusionOur study identified potential prognostic tools for survival prediction in advanced NPC, which suggests that DLR could provide complementary values to the current TNM staging

THE EFFECT OF ARCH-SUPPORTED FUNCTIONAL INSOLES TO AVOID OVERUSED INJURIES DURING RACE WALKING

This study investigates the effectiveness of functional insoles on plantar pressure distribution during race walking in order to reduce the high plantar pressure and force on race walkers, who tend to suffer from overuse injury. A total of 20 male race walkers were recruited as subjects. Each participant completed a race walk with and without functional insoles. Plantar pressure insoles were used to collect plantar pressure data. A two-way analysis of variance with a mixed design was used to determine the difference between the two conditions. Results show that the use of functional insoles reduced the peak pressure and the impulse in the metatarsal heads and heels and thus suggest that functional insoles reduce the overuse injury risks of these parts. The first ground reaction force peak also decreased. This result suggests that functional insoles reduce the risks of foot and leg injuries

DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT

Nasopharyngeal Carcinoma (NPC) is a malignant epithelial cancer arising from the nasopharynx. Survival prediction is a major concern for NPC patients, as it provides early prognostic information to plan treatments. Recently, deep survival models based on deep learning have demonstrated the potential to outperform traditional radiomics-based survival prediction models. Deep survival models usually use image patches covering the whole target regions (e.g., nasopharynx for NPC) or containing only segmented tumor regions as the input. However, the models using the whole target regions will also include non-relevant background information, while the models using segmented tumor regions will disregard potentially prognostic information existing out of primary tumors (e.g., local lymph node metastasis and adjacent tissue invasion). In this study, we propose a 3D end-to-end Deep Multi-Task Survival model (DeepMTS) for joint survival prediction and tumor segmentation in advanced NPC from pretreatment PET/CT. Our novelty is the introduction of a hard-sharing segmentation backbone to guide the extraction of local features related to the primary tumors, which reduces the interference from non-relevant background information. In addition, we also introduce a cascaded survival network to capture the prognostic information existing out of primary tumors and further leverage the global tumor information (e.g., tumor size, shape, and locations) derived from the segmentation backbone. Our experiments with two clinical datasets demonstrate that our DeepMTS can consistently outperform traditional radiomics-based survival prediction models and existing deep survival models.Comment: Accepted at IEEE Journal of Biomedical and Health Informatics (JBHI

Comparison of 18F-FDG and 68Ga-FAPI PET/CT in the diagnosis of lung metastasis in different malignant tumors

Background and purpose: 18F-flurodeoxyglucose (18F-FDG) positron emission tomography and computed tomography (PET/CT) is a common method for the diagnosis of malignant tumors with distant metastases. However the detection of lung metastases, especially small lesions, is still unsatisfactory. 68Ga-labeled fibroblast activation protein inhibitors (68Ga-FAPI) have been used to target fibroblast activating proteins and visualize tumor stroma. The diagnostic value of 68Ga-FAPI PET/CT is higher than that of 18F-FDG in the primary sites and metastases of most cancers, but no comparative study has been seen in lung metastases of malignant tumors. Therefore, this study aimed to investigate the diagnostic value of 18F-FDG and 68Ga-FAPI PET/CT in different malignant lung metastases. Methods: The clinical, pathological and imaging data of 20 patients with lung metastasis who underwent 18F-FDG and 68Ga-FAPI PET/CT examination within one week in Fudan University Shanghai Cancer Center from May 2020 to March 2022 were retrospectively analyzed. Among them, 11 cases were epithelial malignant tumors (carcinoma), and 9 cases were mesophyll malignant tumors (sarcoma). The semi-quantitative metabolic parameters including maximum standard uptake value (SUVmax) and target-to-background ratio (TBR) of 68Ga-FAPI and 18F-FDG were compared by paired t test. The linear correlation between SUVmax and TBR and the short diameter of lung metastasis were analyzed. Results: A total of 81 lung metastases (51 carcinomas and 30 sarcomas) were detected in 20 patients, 72 positive lesions were detected by 18F-FDG and 70 positive lesions by 68Ga-FAPI. Compared with 68Ga-FAPI, 18F-FDG uptake was higher in lung metastases, especially those of carcinoma (P<0.001). The results of linear correlation analysis showed that the semi-quantitative metabolic parameters of the two imaging probes were positively correlated with the short diameter of lung metastases (P<0.001). Conclusion: 68Ga-FAPI has no obvious advantage in the detection of lung metastases from malignant tumors. Especially in the diagnosis of lung metastases from epithelial tissues, the uptake of 18F-FDG tends to be higher

Genome-Wide Analysis of ATP-Binding Cassette (ABC) Transporters in the Sweetpotato Whitefly, \u3cem\u3eBemisia tabaci\u3c/em\u3e

Background: ABC transporter superfamily is one of the largest and ubiquitous groups of proteins. Because of their role in detoxification, insect ABC transporters have gained more attention in recent years. In this study, we annotated ABC transporters from a newly sequenced sweetpotato whitefly genome. Bemisia tabaci Q biotype is an emerging global invasive species that has caused extensive damages to field crops as well as ornamental plants. Results: A total of 55 ABC transporters containing all eight described subfamilies (A to H) were identified in the B. tabaci Q genome, including 8 ABCAs, 3 ABCBs, 6 ABCCs, 2 ABCDs, 1 ABCE, 3 ABCFs, 23 ABCGs and 9 ABCHs. In comparison to other species, subfamilies G and H in both phloem- and blood-sucking arthropods are expanded. The temporal expression profiles of these 55 ABC transporters throughout B. tabaci developmental stages and their responses to imidacloprid, a neonicotinoid insecticide, were investigated using RNA-seq analysis. Furthermore, the mRNA expression of 24 ABC transporters (44% of the total) representing all eight subfamilies was confirmed by the quantitative real-time PCR (RT-qPCR). Furthermore, mRNA expression levels estimated by RT-qPCR and RNA-seq analyses were significantly correlated (r = 0.684, p \u3c 0.01). Conclusions: It is the first genome-wide analysis of the entire repertoire of ABC transporters in B. tabaci. The identification of these ABC transporters, their temporal expression profiles during B. tabaci development, and their response to a neonicotinoid insecticide lay the foundation for functional genomic understanding of their contribution to the invasiveness of B. tabaci

Interim position emission tomography-computed tomography during multimodality treatment of locally advanced esophageal cancer: a scoping review.

BACKGROUND Among cancers, esophageal cancer (EC) has one of the highest incidences and mortality in Asia. As recognized in many national guidelines, functional imaging performed with position emission tomography is recommended for patients with locally advanced disease. This review evaluated evidence for the use of fluorodeoxyglucose (FDG) interim positron emission tomography (PETint) in bimodality (chemoradiation) and trimodality (chemoradiation followed by surgery) management of locally advanced esophageal cancer (LAEC), with a focus on its prognostic and predictive value. METHODS The MEDLINE database was searched from January 1, 2001, to January 1, 2022, as part of a scoping review. References of selected articles were manually checked to identify other articles meeting the inclusion criteria; only original articles were included, and reviews, guidelines, letters, editorials, and case reports were excluded. RESULTS A total of 63 articles were included in this review. PET-computed tomography (PET-CT) is recognized as having a significant role in the assessment of treatment response. Studies on the predictive PETint suggest that it has a certain value, particularly for early response. Identification of poor responders or nonresponders soon after commencement of multimodality treatment allows for treatment modification. CONCLUSIONS The scoping review indicated variable utility for the prognostic value of PETint. There is a need to improve its accuracy, which can likely be achieved through greater standardization of measurements and reporting and testing as well as combination with other promising measures of response to residual disease

Hierarchical meso/macro-porous TiO2/graphitic carbon nitride nanofibers with enhanced hydrogen evolution

Constructing a hierarchical structure with tunable pore size is a practical method to improve the capacity of photocatalytic hydrogen production of catalysts. In this work, titanium dioxide/graphitic carbon nitride (TiO2/g-C3N4) nanofibers with hierarchical meso/macro-porous structure are fabricated by combining a one-step electrospinning method and calcination process, in which the hierarchical meso/macro-porous structure is developed by introducing polyvinylpyrrolidone and liquid paraffin into the electrospinning solution. Comprehensive characterizations reveal that the hierarchical meso/macro-porous TiO2/g-C3N4 nanofibers have improved ultraviolet-visible light absorption, the separation efficiency of carriers, and photocatalytic performance. The photocatalytic H2 evolution is up to 1202 μmol g-1 in 7 hours, which is better than those of corresponding TiO2/g-C3N4 photocatalysts previously reported. This work provides a new strategy to build a hierarchical meso/macro-porous nanofiber and an ideal solution to improve the hydrogen production of TiO2/g-C3N4