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Background and purpose: Deep learning methods can be used for automatic segmentation and detection of lymphocytes on pathological images. This study aimed to assess the performance of using variational autoencoding pre-training method for lymphocyte infiltration detection on pathological images, as well as the impact of removing tumor necrosis regions on model performance. Methods: Using variational autoencoding (VAE) pre-training method, pre-training was performed on a large number of unlabeled pathological images from the Cancer Genome Atlas (TCGA) database (TCGA-COAD and TCGA-READ) to obtain an auto-encoding pre-training model, and then a classifier model of lymphocyte infiltration was trained on the public data samples. To avoid confusion with necrotic regions, a Unet segmentation model for tumor necrotic regions was trained to remove the influence of tumor necrotic regions on lymphocyte identification. Results: The lymphocyte infiltration detection model pre-trained with the VAE model had an area under curve (AUC) of 0.979 (95% CI: 0.978-0.980), an accuracy of 92.5% (95% CI: 92.3%-92.6%), a kappa value of 0.849, sensitivity of 0.908, specificity of 0.941, precision of 0.939, recall of 0.908, and F1 of 0.923 under the receiver operating characteristic (ROC) curve on the training set. The AUC for the validation set was 0.968 (95% CI: 0.964-0.972), the accuracy was 91.3% (95% CI: 90.6%-92.0%), kappa value was 0.826, sensitivity was 0.898, specificity was 0.928, precision was 0.925, recall was 0.898, and F1 was 0.912. The results of Resnet18 model on the labeled dataset were as follows: accuracy of the validation set was 83.2 % (95% CI: 82.2%-84.1%), kappa value was 0.664, sensitivity was 0.823, specificity was 0.840, precision was 0.838, recall was 0.823 and F1 was 0.830. The Unet model that segmented the necrotic regions of the tumors had a final DICE of 0.78 for the training set, and 0.76 for the validation. After removing the necrotic region, the predictive performance of the pre-trained lymphocyte infiltration detection model using the VAE proposed in this article was improved to some extent, with the AUC on the validation set increasing from 0.968 (95% CI: 0.964-0.972) to 0.971 (95% CI: 0.968-0.975). The accuracy was 92.4% (95% CI: 91.7%-93.0%), kappa value was 0.849, sensitivity was 0.928, specificity was 0.921, precision was 0.921, recall was 0.928, and F1 was 0.925. Conclusion: Using the variational autoencoding model pre-training method to classify the pathological pictures of lymphocyte infiltration can obtain better model performance compared with direct training, and removing the influence of tumor necrosis areas through Unet can further improve the performance of the model.

Background and purpose: BRCA1/2 plays an important role in maintaining the genome stability. Whether BRCA1/2 germline mutation could increase the tumor sensitivity to radiotherapy, thereby inducing secondary primary cancer after radiotherapy is unclear. This study aimed to investigate whether postoperative radiotherapy is a risk factor for the development of second primary cancer in triple-negative breast cancer (TNBC) patients with BRCA1/2 germline mutation. Methods: This research was based on a previously reported retrospective cohort, i.e., the Fudan University Shanghai Cancer Center TNBC cohort. Between January 1, 2007 and December 31, 2014, a total of 292 female TNBC patients with BRCA1/2 mutation were enrolled. We performed logistic regression analysis in patients without BRCA1/2 germline mutation (n=261) and BRCA1/2 germline mutation patients (n=31), respectively, to assess the risk factors affecting the incidence of second primary cancer. We then performed interactive analysis on the above two analyses to evaluate the interactive effect between BRCA1/2 germline mutation and postoperative radiotherapy. P

Background and Purpose: In recent years, domestic radiotherapy equipment and related software have made great progress, and testing the functionality and stability of the equipment and software is an essential step. This paper focused on comparing the differences in intensity-modulated radiation therapy (IMRT) plans dosimetry and organ at risk (OAR) volume calculations for common cancers between uRT-treatment planning system (TPS) and Monaco-TPS, and to evaluate the feasibility of dose calculation for Infinity linac (linear accelerator, Elekta, Sweden) using uRT-TPS. Methods: Twenty cases of rectal cancer, lung cancer, breast cancer and nasopharyngeal carcinoma were selected. The IMRT plans were completed in uRT-TPS and Monaco-TPS. The dose uniformity and conformity, mean dose, maximum dose of planning target volume (PTV) and OAR between two plans under the same prescribed dose of PTV were compared. And the pass rates of two TPS plans validated at the same linear accelerator were compared. Meanwhile, monitor units (MU), source skin distance (SSD) and the volume of OAR in uRT-TPS and Monaco-TPS were compared. Results: Wonderful plans that met the clinical requirements were obtained in uRT-TPS and Monaco-TPS. Comparable uniformity and conformability was received in PTV, and the maximum dose of PTV was reduced by 1.1 Gy for uRT-TPS (P = 0.006). For breast cancer and lung cancer, the dose in lung was lower for Monaco-TPS (P95%) for both plans met the clinical requirements. The region of interest in uRT-TPS was smaller compared with Monaco-TPS (P

Background and purpose: Accelerated go live (AGL) is the entire management process for medical accelerators from installation, commissioning, modeling to radiotherapy implementation. Compared with the traditional time-consuming accelerator acceptance process, AGL has established a set of gold standard models based on the big data of thousands of medical accelerators, which makes the accelerator installation greatly optimized in the data acquisition and modeling stage. This study aimed to evaluate and verify an efficient beam modeling method for Monaco treatment planning system (TPS) using golden beam data (GBD). Methods: Two Elekta Infinity™ linear accelerators with agility head in the Department of Radiation Oncology, Fudan University Shanghai Cancer Center, were installed and matched with the GBD. Then, a customer acceptance test (CAT) and beam model validations were performed on measured percentage depth dose (PDD) and profile for open-field (OF) point-doses in a three-dimensional water tank. Furthermore, a total of 16 clinical cases from different sites were used for radiotherapy planning design, measurement and dose analysis to validate and verify the accuracy of the beam model. Results: For beam model verification, the agreement of all validation PDD and profile was larger than 90%, and the majority point passing rate was larger than 95% (99.4% for linac 1 and 98.5% for linac 2). For point-dose validation, 96.8% and 97.2% of OF point measured were found to be within 2% agreement of TPS-calculations for linac 1 and linac 2, respectively. The deviation of R50 and R90 between TPS calculation and measured were within 3 mm at all evaluated points of electron beam. The Gamma passing rate for all plans was greater than 90% when using 3%/3 mm criteria, and point-dose agreement was within 3%. Conclusion: Compared with the traditional accelerator process from installation to commission, the Monaco TPS modeling process AGL based on GBD can not only ensure the model accuracy, but also shorten the entire process time, which has the feasibility of clinical application.

Background and purpose: The incidence of pancreatic cancer has increased annually. Intensity-modulated radiotherapy (IMRT) has been widely used in the treatment of pancreatic cancer, however most dosimetric studies for pancreatic cancer have prescribed doses less than 60 Gy. The purpose of this study was to investigate the feasibility and dosimetric differences between coplanar IMRT (CO-IMRT) and non-coplanar IMRT (NC-IMRT) when using 75 Gy simultaneous integrated boost for patients with locally advanced pancreatic cancer (LAPC). Methods: Ten patients with LAPC treated with simultaneous integrated boost at Fudan University Shanghai Cancer Center from January 2018 to December 2021 were included. The prescribed dose for the planning target volume (PTV)50 Gy was 50 Gy in 25 fractions, and 75 Gy in 25 fractions for the simultaneous integrated boost PTV75 Gy. CO-IMRT and NC-IMRT plans were designed for each patient separately, with identical number of fields, prescription doses and OAR constrains for the same patient. The PTV dose distribution, conformability index (CI), homogeneity index (HI), indices to organs at risk (OAR) for multiple end points, beam-on time and monitor units (MU) were analyzed. We evaluated whether the PTV met clinical requirements and quantitative analysis of normal tissue effects in the clinic (QUANTEC) limits, and compared dosimetric differences between the two plans. Results: Both CO-IMRT and NC-IMRT could achieve the required dose coverage of PTV and meet the QUANTEC dose limits for OAR. The differences in CI, HI, beam-on time and MU between the two plans were not statistically significant (P>0.05). NC-IMRT was significantly decreased left kidney Dmean[(10.15±1.53) Gy vs (9.29±1.78) Gy, P0.05). Conclusion: In the treatment of LAPC using 75 Gy simultaneous integrated boost, both CO-IMRT and NC-IMRT can achieve excellent PTV coverage and meet the OAR constraints in all patients. Without affecting the quality of PTV coverage, NC-IMRT has more advantages in the dose distribution of OAR, which can better protect the OAR and reduce the toxicity to the gastrointestinal tract, liver and kidneys.

Introduction: The primary objective of our investigation was to assess the repercussions of prolonged exposure to heavy metals and smoking on the microbiome of the oral buccal mucosa. Concurrently, we aimed to elucidate the intricate interplay between external environmental exposures and the composition of the oral microbial ecosystem, thereby discerning its potential implications for human health. Methods: Our study cohort was stratified into four distinct groups: MS (characterized by concurrent exposure to heavy metals and smoking), M (exposed solely to heavy metals), S (exposed solely to smoking), and C (comprising individuals serving as a control group). Specimens of buccal mucosa and blood were systematically acquired from the participants, facilitating subsequent microbial diversity analysis across the four oral buccal mucosa sample cohorts through 16S rRNA gene sequencing techniques. Simultaneously, blood samples were tested for heavy metal concentrations. In addition, we performed topological analyses by constructing microbial networks. Results: Our findings notably indicate that co-exposure to heavy metals and smoking yielded a more pronounced alteration in the diversity of oral microflora when compared to singular exposures to either heavy metals or smoking. By comparing the oral bacterial communities and functional pathways between the four groups, we found significant differences in bacterial communities and functional pathways between the groups. Notably, the impact of heavy metal exposure overshadowed that of smoking, with concurrent exposure to heavy metals and smoking eliciting marginally greater effects than exposure to heavy metals alone. In addition, our analysis of the correlation between microbiota and blood heavy metal concentrations showed that the heavy metal cadmium (Cd) had a significantly greater effect on oral microbiota than other heavy metals. Discussion: Chronic exposure to heavy metals and smoking disrupts the normal bacterial communities in the oral mucosa of residents of contaminated areas. This exposure reduces the complexity and stability of microbial networks and increases the risk of various diseases reduces the complexity and stability. [ABSTRACT FROM AUTHOR]

To evaluate the clinical applicability of deep learning based auto-segmentation of organs at risk (OARs) based on geometric and dosimetric indices. In this study, a total of 35 patients with rectal cancer who received radiotherapy in the Fudan University Shanghai Cancer Center were enrolled, and an in-house developed deep learning system was used to automatically segment OARs. Taking manual delineation as reference, the geometric evaluation indices included the dice similarity coefficient (DSC), jaccard similarity coefficient (JSC), hausdorff distance (HD), and mean distance to agreement (MDA). For each case, the treatment planning was optimized based on the auto-segmented OARs. The plan optimization and evaluation process was consistent with clinical procedure, recorded as Plan_FD. The dosimetric differences between Plan_FD and the original clinical treatment plan (recorded as Plan_Treat) were compared through dose-volume parameters and three-dimensional gamma analysis. The auto-segmented and manually delineated OARs were not only highly overlapping in three-dimensional space with an average value of DSC greater than 0.85 but also well matched in edge details with an average value of MDA less than 2.8 mm. In the dosimetric evaluation, a statistically significant difference was observed only for the bladder (p < 0.05); the dose-volume parameters of the other OARs and PTV were not statistically significant. Three-dimensional γ analysis (3 mm / 3 % standard) was performed on Plan_FD and Plan_Treat, where the percentage of points with γ ≤ 1.0 was (91.63 ± 6.27) %. Deep learning-based auto-segmentation of OARs has a high geometric similarity with manual delineation. The auto-segmented OARs have no significant impact on treatment planning and dose distribution, and have the potential to be directly applied to clinical treatment planning.

Background and purpose: The precise radiotherapy of tumor site depends on the precise dose distribution of the therapeutic target. The purpose of this study was to explore the influence of different computed tomography value to the relative electron density (CT-RED) calibration curve on the dose distribution of tumor and the method of fitting one CT-RED curve. Methods: Two CT scanners in Fudan University Shanghai Cancer Center were used to scan the CIRS-062 electronic density phantom, the CT- RED calibration curves of different sites (head, chest and abdomen) were obtained, and an improved CT-RED curve was optimized based on each curve. The original and modified CT-RED curves were used to calculate the dose distribution of planning target volume (PTV) (nasopharyngeal cancer, lung cancer and cervical cancer, 16 cases of each) and organ at risk (OAR) in the treatment planning system (TPS). Finally, 5 cases of each tumor were selected to verify the feasibility of optimized CT-RED. Results: The dose calculated by different CT-RED curves showed minimal differences in cervical cancer and head and neck tumor ( ＜ 1.00% and ＜ 1.13%), but there was a large difference (2.50%) in low-density area of lung tumor. There was no significant difference ( ＜ 1.00%) between the optimized CT-RED curve and the original six CT-RED curves in the PTV and OAR. Conclusion: The corresponding CT- RED calibration curve should be selected to calculate the dose distribution of different tumors. And the improved CT-RED calibration curve scheme can also be used to reduce the potential risk of wrong calibration curve selection in the center with multiple positioning CT.

Introduction: This study primarily aims to investigate the suitability of Halcyon in the context of cervical-thoracic esophageal cancer by exploring the dosimetric quality and delivery efficiency of Halcyon plans with different arc configurations. Additionally, it compares these findings with the dosimetric indices and delivery efficiency of TrueBeam and TomoTherapy accelerators, focusing on their capability to optimize protection for organs at risk (OARs) while maintaining efficient treatment delivery strategies. Methods: This retrospective study involved 26 patients diagnosed with cervical-thoracic esophageal cancer, and new radiotherapy plans were created using Halcyon, TrueBeam, and TomoTherapy. Dose volume histogram (DVH) metrics and delivery efficiency for plans involving different arc numbers on Halcyon (2, 3, and 4 arcs) were compared with those from TrueBeam and TomoTherapy. T-tests were employed to evaluate differences in organ protection among the accelerators. Results: The Halcyon plans, especially those with 4 arcs, provided superior protection for organs at risk, including the heart, lungs, and spinal cord, while maintaining excellent delivery efficiency. Compared to TrueBeam 2arc plans and TomoTherapy helical plans, Halcyon plans with 3 arcs also showed slight advantages. Although TomoTherapy offered better uniformity in dose distribution, it did not demonstrate a clear advantage over the other accelerators in terms of OAR protection or treatment efficiency. Furthermore, despite the lack of clear advantages in TrueBeam 2arc plans with flattening filter (FF), TrueBeam with flattening filter free (FFF) plans may hold potential in the treatment. Conclusion: Halcyon, particularly with 4 arcs, offers an optimal balance between reducing toxicity to organs at risk and maintaining treatment efficiency, making it a preferred choice in cervical thoracic esophageal cancer radiotherapy. The findings highlight the need for careful selection of radiotherapy accelerators based on specific clinical goals, with Halcyon showing potential advantages in scenarios where both treatment efficiency and OAR protection are paramount. [ABSTRACT FROM AUTHOR]

Inorganic carbon is an important component of soil carbon stocks, exerting a profound influence on climate change and ecosystem functioning. Drylands account for approximately 80% of the global soil inorganic carbon (SIC) pool within the top 200 cm. Despite its paramount importance, the components of SIC and their contributions to CO2 fluxes have been largely overlooked, resulting in notable gaps in understanding its distribution, composition, and responses to environmental factors across ecosystems, especially in deserts and temperate grasslands. Utilizing a dataset of 6011 samples from 173 sites across 224 million hectares, the data revealed that deserts and grasslands in northwestern China contain 20 ± 2.5 and 5 ± 1.3 petagrams of SIC in the top 100 cm, representing 5.5 and 0.76 times the corresponding soil organic carbon stock, respectively. Pedogenic carbonates (PIC), formed by the dissolution and re‐precipitation of carbonates, dominated in grasslands, accounting for 60% of SIC with an area‐weighted density of 3.4 ± 0.4 kg C m−2 at 0–100 cm depth, while lithogenic carbonates (LIC), inherited from soil parent materials, prevailed in deserts, constituting 55% of SIC with an area‐weighted density of 7.1 ± 1.0 kg C m−2. Soil parent materials and elevation determined the SIC stocks by regulating the formation and loss of LIC in deserts, whereas natural acidification, mainly induced by rhizosphere processes including cation uptake and H+ release as well as precipitation, reduced SIC (mainly by PIC) in grasslands. Overall, the massive SIC pool underscores its irreplaceable role in maintaining the total carbon pool in drylands. This study sheds light on LIC and PIC and highlights the critical impact of natural acidification on SIC loss in grasslands. [ABSTRACT FROM AUTHOR]