ARDS has a high mortality in these Chinese PICUs, especially in those with pneumonia and sepsis, and adequate management including lung protective ventilation strategy is required.

Chimeric antigen receptors T cells (CAR T) had been used for treating various tumor patients in clinic, and owned an incredible efficacy in part of malignancies. However, CAR T therapy remains controversial due to doubts about its efficacy and safety in the clinical treatment of various malignancies. A total of 997 tumor patients from 52 studies were included in this review. Eligible studies were searched and reviewed from the databases of PubMed, Web of Science, Wanfang and Clinicaltrials.gov. Then meta-analysis and subgroup analysis were used to investigate the overall response rate (ORR), complete response rate (CRR), common side effect rate (CSER) and relapse rate (RR) of CAR T therapy for patients in clinical researches, respectively. The results further confirmed that CAR T therapy had a higher response rate for hematologic malignancies. More importantly, CAR T therapy had a higher CSER in patients with hematologic malignancies, and it had a similar RR in patients with different malignancies. Cell cultured without the addition of IL-2 and total administration less than 108 cells were recommended. This study offers a reference for future research regarding the application in solid and hematologic malignancies, side effects and relapse, and even the production processes of CAR T cells.

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PurposeTo investigate the feasibility of constant dose rate volumetric modulated arc therapy (CDR-VMAT) in the treatment of nasopharyngeal cancer (NPC) patients and to introduce rotational arc radiotherapy for linacs incapable of dose rate variation.Materials and methodsTwelve NPC patients with various stages treated previously using variable dose rate (VDR) VMAT were enrolled in this study. CDR-VMAT, VDR-VMAT and mutlicriteria optimization (MCO) VMAT plans were generated for each patient on RayStation treatment planning system with identical objective functions and the dosimetric differences among these three planning schemes were evaluated and compared. Non dosimetric parameters of optimization time, delivery time and delivery accuracy were also evaluated.ResultsThe planning target volume of clinical target volume (PTV-CTV) coverage of CDR-VMAT was a bit inferior to those of VDR- and MCO-VMAT. The V93 (p = 0.01) and V95 (percent volume covered by isodose line) (p = 0.04) for CDR-VMAT, VDR-VMAT and MCO-VMAT were 98.74% ± 0.31%, 99.76% ± 0.16%, 99.38% ± 0.43%, and 98.40% ± 0.48%, 99.53% ± 0.28%, 99.07% ± 0.52%, respectively. However, the CDR-VMAT showed a better dose homogeneity index (HI) (p = 0.01) in PTV-CTV. No significant difference in other target coverage parameters was observed. There was no significant difference in OAR sparing among these three planning schemes except for a higher maximum dose (Dmax) on the brainstem for CDR-VMAT. The brainstem Dmax of CDR-VMAT, VDR-VMAT and MCO-VMAT were 54.26 ± 3.21 Gy, 52.19 ± 1.65 Gy, and 52.79 ± 4.77 Gy, respectively. The average delivery time (p < 0.01) and the average percent γ passing rates (p = 0.02) of CDR-VMAT, VDR-VMAT and MCO-VMAT were 7.01 ± 0.43 min, 4.75 ± 0.07 min, 4.01 ± 0.28 min, and 95.75% ± 2.57%, 97.65% ± 1.45%, 97.36% ± 2.45%, respectively.ConclusionCDR-VMAT offers an additional option of rotational arc radiotherapy for linacs incapable of dose rate variation with a lower initial cost. Its plan quality was acceptable but should be thoroughly checked compared with VDR-VMAT and MCO-VMAT in the treatment of NPC.

The impact of granulocyte-macrophage colony stimulating factor (GM-CSF) on hematologic indexes and complications remains existing contradictory evidence in cancer patients after treatment of chemotherapy. Eligible studies up to March 2017 were searched and reviewed from PubMed and Wanfang databases. Totally 1043 cancer patients from 15 studies were included in our research. The result indicated that GM-CSF could significantly improve white blood cells count (SMD = 1.16, 95% CI: 0.71 – 1.61, Z = 5.03, P < 0.00001) and reduce the time to leukopenia recovery (SMD = -0.85, 95% CI: -1.16 – -0.54, Z = 5.38, P < 0.00001) in cancer patients after treatment of chemotherapy. It also could improve absolute neutrophil count (SMD = 1.11, 95% CI: 0.39 – 1.82, Z = 3.04, P = 0.002) and significantly shorten the time to neutropenia recovery (SMD = -1.47, 95% CI: -2.20 – -1.75, Z = 3.99, P < 0.0001). However, GM-CSF could not improve blood platelet (SMD = 0.46, 95% CI: -0.37 – -1.29, Z = 1.10, P = 0.27). And GM-CSF had significant connection with fever (RR = 3.44, 95% CI: 1.43 – 8.28, Z = 2.76, P = 0.006). The publication bias existed in the data of the impact of GM-CSF on blood platelet and complication. In conclusions, GM-CSF had an intimate association with some hematologic indexes and complications. Our study suggested that more hematological indexes and even more other indexes need to be observed in future studies.

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The aim of this study was to investigate the feasibility and sensitivity of using individual volume–based 3D gamma indices for composite dose–volume histogram (DVH)–based intensity-modulated radiation therapy (IMRT) quality assurance (QA). Composite IMRT QA for 15 cervical cancer patients was performed with ArcCHECK. The percentage dosimetric errors (%DEs) of DVH metrics when comparing treatment planning system and QA-reconstructed dose distribution, percentage gamma passing rates (%GPs) with different criteria for individual volumes and global gamma indices were evaluated, as well as their correlations. Receiver operating characteristic (ROC) curves were applied in order to study the sensitivities of the global and individual volume gamma indices. Most %DEs of the DVH metrics were within 3%. The γPTV and γrectum were <80% at 2%/2 mm; apart from these two individual volume indices, all other individual volume gamma indices and global indices had acceptable %GPs. For the criteria of 2%/2 mm, 3%/3 mm and 4%/4 mm, individual volume-based %GPs and global %GPs were correlated in 11, 1 and 12 out of 24 %DE metrics, and in 5, 4 and 5 out of 24 %DE metrics, respectively. Individual volume–based %GPs had a higher percentage of correlation with DVH metrics (%DEs) compared with global %GPs in composite IMRT QA. The areas under the curve (AUCs) of individual volume %GPs were higher than those of global %GPs. In conclusion, individual volume–based %GPs had a higher correlation with %DEs of metrics and a higher sensitivity presented by ROC analysis compared with global %GPs for composite IMRT QA. Thus, use of individual volume-based 3D gamma indices was found to be feasible and sensitive for composite IMRT QA.

Purpose To predict the voxel-based dose distribution for postoperative cervical cancer patients underwent volumetric modulated arc therapy using deep learning models. Method A total of 254 patients with cervical cancer received volumetric modulated arc therapy in authors’ hospital from January 2018 to September 2021 were enrolled in this retrospective study. Two deep learning networks (3D deep residual neural network and 3DUnet) were adapted to train (203 cases) and test (51 cases) the feasibility and effectiveness of the prediction method. The performance of deep learning models was evaluated by comparing the results with those of treatment planning system according to metrics of dose-volume histogram of target volumes and organs at risk. Results The dose distributions predicted by deep learning models were clinically acceptable. The automatic dose prediction time was around 5 to 10 min, which was about one-eighth to one-tenth of the manual optimization time. The maximum dose difference was observed in D98 of rectum with a | δD| of 5.00 ± 3.40% and 4.88 ± 3.99% for Unet3D and ResUnet3D, respectively. The minimum difference was observed in the D2 of clinical target volume with a |δD| of 0.53 ± 0.45% and 0.83 ± 0.45% for ResUnet3D and Unet3D, respectively. Conclusion The 2 deep learning models adapted in the study showed the feasibility and reasonable accuracy in the voxel-based dose prediction for postoperative cervical cancer underwent volumetric modulated arc therapy. Automatic dose distribution prediction of volumetric modulated arc therapy with deep learning models is of clinical significance for the postoperative management of patients with cervical cancer.