Post-Prostatectomy Risk Stratification of Biochemical Recurrence Using Transfer Learning-Based Multi-Modal Artificial Intelligence

PURPOSE/OBJECTIVE(S)

For patients undergoing radical prostatectomy for prostate cancer (PCa), accurate risk stratification is essential to guide post-prostatectomy therapeutic decision making. Recently, there has been success in the use of multi-modal artificial intelligence models for men after prostate biopsy to aid in risk stratification. Herein, we trained and tested a TRansfer learning-based multi-modal Artificial InteLligence model (TRAIL) for biochemical recurrence (BCR) risk stratification following radical prostatectomy.

MATERIALS/METHODS

Patients contained within a prospective PCa registry at a single institution were utilized. Digital pathology slides from the diagnostic biopsies prior to radical prostatectomy for patients with clinically localized PCa were scanned at 20x resolution. Features were extracted for the TRAIL model from pathology slides via two transfer learning steps: (1) InceptionResNetv2 that first determines a heatmap of tumor areas, and (2) A ResNet18 that extracts representative features from the high tumor probability areas. Least Absolute Shrinkage and Selection Operator (LASSO) was used for feature selection from the pathology-extracted features. Finally, TRAIL combines the clinical and pathology-extracted features via a classification ensemble model based on weak tree learners to predict 2- and 5-year BCR defined as two consecutive serum PSA levels ≥0.2 ng/mL. TRAIL training was performed on 250 patients and was then locked and applied to the test set of 125 patients. Accuracy and the area under the curve (AUC) were calculated. Comparison to CAPRA-S and to clinical-only features were assessed.

RESULTS

A total of 818 digital whole pathology biopsy slides from 375 patients treated with subsequent radical prostatectomy were included. Surgical margins were positive in 29% of the patients, and 41% had extra-prostatic extension. The median follow-up was 48 months (Range: 1-132 months). The rates of 2-and 5-year BCR were 11% and 18% respectively. A total of 19 digital pathology-driven features were included in TRAIL. Clinical factors included age, ISUPG, Gleason score, PSA, pathological T and N stages, surgical margin involvement, and the presence of extra-prostatic extension. On the testing set, TRAIL achieved a 2-year BCR AUC of 0.76 and accuracy of 0.87, and was superior to CAPRA-S (AUC = 0.57) and clinical-only features (AUC 0.50, accuracy 0.14). For 5-year BCR, TRAIL achieved an AUC of 0.69 and accuracy of 0.78, and performed better than CAPRA-S (AUC = 0.58), and clinical only features (AUC = 0.50, accuracy = 0.23).

CONCLUSION

Through a combination of deep and ensemble learning, TRAIL incorporates clinical and histopathology features, enabling an improved BCR risk stratification post-prostatectomy when compared to the currently used clinicopathologic models. Future work with larger datasets with metastatic events is warranted to further optimize the model for clinical use.

Identification and Automated Delineation of Radioresistant Biological Tumor Volume in Nasopharyngeal Carcinoma Based on Magnetic Resonance Imaging Radiomics

PURPOSE/OBJECTIVE(S)

Widespread use of intensity modulated radiotherapy (IMRT) has improved the tumor control rate of nasopharyngeal carcinoma (NPC). However, nearly 20% of the patients with local-advanced NPC would relapse after precise irradiation and 80% of the recurrent lesions occur within the high dose field, suggesting that there are radiation-resistant cancer cell subsets within the tumor. In this context, identification and contouring of radiation resistance region of NPC for dose escalation at primary IMRT could be advantageous. In this work, we proposed a two-step radiomics workflow to predict local relapse and the recurrent region of NPC before primary IMRT.

MATERIALS/METHODS

In this single-center, retrospective study, pre-treatment magnetic resonance (MR) sequences of T1-weighted imaging (T1-w) and contrast-enhanced T1-weighted imaging (CET1-w) were collected from 800 patients of newly diagnosed and non-metastatic NPC between April 2009 and December 2015. The primary gross tumor volume (GTVp) of all patients and the actual recurrent lesion (GTVr) of patients who suffered from local recurrence were manually contoured for further analysis. A two-step complete radiomics workflow was designed to predict tumor recurrence and segment the region. First, least absolute shrinkage and selection operator (LASSO) was utilized for radiomics features selection of GTVp and support vector machine (SVM) was adopted to predict the recurrence. If the model predicts a recurrence, then the workflow utilizes an improved 3D U-Net to segment the recurrent region. Area under receiver operating characteristic curve (ROC-AUC) was used to evaluate the performance of tumor recurrence prediction, and Dice similarity coefficient (DSC) was used to assess the consistence between the actual and predicted GTVr.

RESULTS

Of 800 NPC patients, 95 (11.9%) patients developed in-field local recurrence. For recurrence risk prediction, the SVM ensemble model (T1-w+CET1-w) was selected for further application with higher sensitivity. The average ROC-AUC, specificity, sensitivity of the SVM ensemble model in a 5-fold cross-validation and in the independent test set of 160 patients were 0.922, 0.922, 0.777 and 0.928, 0.915, 0.737, respectively. Moreover, for recurrent region segmentation, the multi-modality (T1-w+CET1-w) model was superior to the single-modality (T1-w or CET1-w) model. In an independent test set of 15 patients, the DSC, sensitivity and 95% Hausdorff Distance between actual and predicted GTVr was 0.549±0.176, 0.696±0.118 and 9.813±4.788 which was superior to 0.444±0.188, 0.497±0.218 and 12.047±5.361 of original 3D U-Net.

CONCLUSION

The proposed two-step radiomics workflow showed a good performance in predicting tumor recurrence of NPC. The predicted location of the recurrence lesion was all accurate, but there was still a certain difference between the volume of the automated delineated and actual GTVr, which needed to be further optimized to be used as biological tumor volume.

Cross-Comparison Individual Patient Level Analysis of Three Gene Expression Signatures in Localized Prostate in over 50,000 Men

PURPOSE/OBJECTIVE(S)

Risk stratification guides the management of localized prostate cancer. Multiple commercial gene expression biomarkers have been developed to improve estimates of prognosis, however the 22-gene Decipher genomic classifier (22-GC) is the only test with level 1 evidence supporting its use per NCCN guidelines. It is unknown whether other commercial signatures, Oncotype (GPS) or Prolaris (CCP), are sufficiently correlated to negate the differences in evidence supporting these commercial tests. Herein, we aim to perform a cross-comparison of these signatures in a large cohort of patients diagnosed with localized prostate cancer.

MATERIALS/METHODS

Patients diagnosed with localized prostate cancer who underwent whole transcriptome gene expression microarray analysis on their primary tumor biopsy specimen were included. The 22-GC score was calculated by Veracyte using a commercially locked model. Individual genes in each of the GPS and CCP gene signatures were identified, and the gene weights in each signature were retrained for prediction of metastasis in a multi-institutional cohort of 1,574 men with long-term outcome data. This was performed to improve correlation performance of GPS and CCP given only the 22-GC was trained for prediction of metastasis. For each of the three signatures, both continuous and categorical scores were calculated. Linear regression and spearman correlations were calculated both on univariable and multivariable analyses adjusting for age, grade group, PSA, and T-stage.

RESULTS

A total of 50,881 patients were included (15,379 (30.2%) NCCN low-risk, 14,773 (29.0%) favorable intermediate-risk, 15,544 (30.5%) unfavorable intermediate-risk, and 5,185 (10.2%) high/very high-risk) with a median age of 68 years, and a median PSA of 6.2 ng/mL. On linear regression, the GPS model had poor goodness-of-fit to the 22-GC with an R2 of 0.36, as did the CCP model to the 22-GC with an R2 of 0.32. For CCP, the linear sum of the 31-genes was also tested but had inferior performance (R2 0.28) compared to the reoptimized CCP model. Results were similar on multivariable analysis adjusting for age, PSA, clinical stage and grade group. Spearman correlation between the continuous GPS model scores and the 22-GC was moderate at 0.59, as was the correlation between CCP model and the 22-GC of 0.54. CCP is a measure of proliferation, but in 22-GC high-risk patients, the majority (64.1%) of patients had low-average proliferation and only 35.9% had high proliferation, potentially explaining the lack of strong correlation.

CONCLUSION

There is minimal to moderate correlation between the 22-GC and GPS or CCP gene expression signatures tested. Therefore, these tests should not be viewed as interchangeable, and utilization should be based on the level of evidence supporting each gene expression biomarker.

Delta-Radiomics Guides Adaptive De-Intensification after Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma in the IMRT Era

PURPOSE/OBJECTIVE(S)

In the setting of intensity-modulated radiotherapy (IMRT) and induction chemotherapy (IC), the benefits from concurrent chemotherapy remained controversial for locoregionally advanced nasopharyngeal carcinoma (LANPC). This study aimed to construct a delta-radiomics model for benefit prediction and patient selection for omitting concurrent chemotherapy.

MATERIALS/METHODS

Between December 2009 and December 2015, a total of 718 patients with LANPC treated with IC+IMRT or IC+concurrent chemoradiotherapy (CCRT) were retrospectively enrolled and randomly assigned to a training set (n = 503) and a validation set (n = 215). Radiomic features were extracted from magnetic resonance images of pre-IC and post-IC. Interclass correlation coefficients and Pearson correlation coefficients were calculated to select robust radiomic features. After univariate Cox analysis, a delta-radiomics signature was built using the LASSO-Cox regression. A nomogram incorporating the delta-radiomics signature and clinical prognostic factors was then developed and evaluated for calibration and discrimination. Risk stratification by the nomogram was evaluated by Kaplan-Meier methods. The primary outcome was overall survival (OS).

RESULTS

The delta-radiomics signature, which comprised 19 selected features, was independently associated with prognosis. It yielded an area under the receiver operating characteristic curve (AUC) of 0.77 (95% confidence interval [CI] 0.71 to 0.82) for the training set and 0.71 (95% CI 0.61 to 0.81) for the validation set. The nomogram composed of the delta-radiomic signature, age, T category, N category, pre-treatment Epstein-Barr virus DNA, and treatment showed great calibration and discrimination performance with an AUC of 0.80 (95% CI 0.75 to 0.85) for the training set and 0.75 (95% CI 0.64 to 0.85) for the validation set. Risk stratification by the nomogram excluding the treatment variable resulted in two risk groups with distinct OS. Significant better outcomes were observed in the high-risk patients with IC+CCRT compared to those with IC+IMRT (5-year OS: 73.8% vs. 61.4% in the training set and 85.8% vs. 65.6% in the validation set; all log-rank p < 0.05), while comparable outcomes between IC+CCRT and IC+IMRT were shown for the low-risk patients (95.8% vs. 95.8% in the training set and 92.2% vs. 88.3% in the validation set; all log-rank p > 0.05).

CONCLUSION

The delta-radiomics signature was identified as an independent indicator of LANPC. Integrating clinical predictors with the delta-radiomics signature, the radiomics-based nomogram could predict individual's survival outcomes and benefits from concurrent chemotherapy after IC for LANPC. Low-risk patients with LANPC determined by the nomogram may be potential candidates for omission of concurrent chemotherapy following IC in the IMRT era.

All-in-One Online Radiotherapy for Nasopharyngeal Carcinoma: Preliminary Results of Treatment Time, Contouring Accuracy, Treatment Plan Quality and Patient Compliance

PURPOSE/OBJECTIVE(S)

To explore the feasibility of Fan-beam CT (FBCT)-based all in one (AIO) online workflow for nasopharyngeal carcinoma (NPC) in radical radiotherapy setting, and to preliminarily describe the timing of different steps in the process, contouring accuracy of regions of interest (ROIs), target coverage, organs at risk (OARs) dose and patient compliance.

MATERIALS/METHODS

From March 16, 2022 to January 04, 2023, 25 NPC patients (22/25 diagnosed as phase III/IV disease according to 8th edition of the AJCC/UICC staging system) consecutively treated with AIO radiotherapy were prospectively enrolled. All patients received mask fixation and MRI simulation scan in advance. Primary gross tumor volume (GTVp) of nasopharynx was automatically delineated by AI and edited manually on MRI images. AIO online workflow started with an integrated KV-level CT in a CT-integrated linear accelerator. After that GTVp was registrated to CT images and other ROIs was contoured automatically and then modified manually as needed. Subsequently automatic treatment plan was calculated and optimized until the dose of target and OARs was evaluated satisfactory by physicians and physicists. Finally, treatment was delivered using volumetric modulated arc treatment (VMAT), with prescribed dose of 6996 cGy/ 33 fractions to the GTVp.

RESULTS

Twenty-four patients (24/25, 96%) completed the AIO radiotherapy workflow successfully, with average treatment time of 28.3 min (range: 19.9-42.4 min). the AI-assisted ROIs automatically contouring took 1.55 min in average (range: 1.32-1.77 min), with an average DICE of 97.7% compared with modified contouring, and the average DICE was 95.7% for clinical tumor volume 1 (CTV1), 88.6% for CTV2, 73.6% for GTVn (cervical lymph node), 99.3% for 30 OARs. The automatic treatment plan averagely needed 3.5 min, and the pass rate of radiotherapy planning was 91.7% (22/24). The target coverage for PTVs for GTVp, CTV1, and CTV2 was 99.3%, 99.8%, 98.0% respectively. As for the dose of OARs, the average Dmax of brainstem was 5,583cGy; the Dmax of spinal cord was 3,467cGy; the Dmean of parotid was 3,285 cGy. The average monitor units of all patients was 643 MU and the delivery took 2.93 min. Patient compliance with respect to AIO workflow and total treatment time was excellent.

CONCLUSION

The AIO online radiotherapy was promising for NPC patients, with clinically acceptable AI assisted ROIs contouring and treatment planning, as well as favorable patient compliance to the AIO online workflow.

Automated Contouring of Cervical Lymph Nodes and Clinical Target Volumes for Nasopharyngeal Carcinoma Based on Deep Learning and Experience Constraints

PURPOSE/OBJECTIVE(S)

Application of artificial intelligence (AI) for automated contouring of tumor volumes and organs at risk (OARs) for radiotherapy of nasopharyngeal carcinoma (NPC) leads to improved contouring accuracy and efficiency. However, few studies have involved the automated contouring of gross tumor volume of cervical lymph nodes (GTVn) and clinical target volumes (CTVs). In this work, we proposed an AI automated contouring tool for GTVn and CTVs for radiotherapy of NPC on the plain scans of planning compute tomography (CT).

MATERIALS/METHODS

In this retrospective study, plain scan datasets of planning CT covering the nasopharynx and neck from 139 patients with NPC between March 2022 and December 2022 were collected and divided into training, validation, and testing cohorts of 95, 24, and 20 patients, respectively. Ground truth contours of primary gross tumor volume (GTVp), GTVn (divided into GTVn_L in left neck and GTVn_R in right neck), CTVs (including high risk CTV1 contains GTVp and low risk CTV2 contains GTVp and cervical nodal levels) and OARs were delineated and were defined by consensus of two experts. We first proposed a three-dimensional (3D) U-net using GTVp and OARs as experience constrains to guide the automated delineation of GTVn and CTVs. The average Dice similarity coefficient (DSC) and average surface distance (ASD) were used to quantify the performance of the AI tool. Next, five prospective patients were enrolled for clinical evaluation of our AI tool. DSC between automated contours and radiation oncologist-revised contours and time consuming of the revision were record.

RESULTS

Clinical characteristics of 139 retrospective and 5 prospective patients are list in Table 1. In the independent testing set of 20 patients, our AI tool showed high performance in GTVn and CTVs contouring when compared with the ground truth contours. The mean DSC were 0.73 ± 0.07, 0.74 ± 0.05, 0.93 ± 0.03, and 0.88 ± 0.03, and the mean ASD were 1.01 ± 0.43 mm, 1.14 ± 0.61 mm, 0.51 ± 0.13 mm, 1.17 ± 0.43 mm for GTVn_L, GTVn_R, CTV1 and CTV2, respectively. In the five prospective patients, mean DSC were 0.74 ± 0.07, 0.74 ± 0.10, 0.95 ± 0.01 and 0.89 ± 0.04, respectively. The median time consuming for GTVn and CTVs revision was 2minutes and 10 seconds (range, 1 minutes to 3 minutes).

CONCLUSION

The proposed AI tool integrating clinical experience as constrains showed high accuracy for contouring GTVn and CTVs of NPC. With the assistance of AI contours, contouring efficiency could be probably increased, which is promising in online adaptive radiotherapy of NPC.

First Implementation of Full-Workflow Automation for Online Adaptive Radiotherapy of Nasopharyngeal Carcinoma

PURPOSE/OBJECTIVE(S)

The aim of this work is to established the technical characteristics and implementation procedures of an artificial intelligence (AI)-powered radiotherapy workflow that enables full-process automation for online adaptive radiotherapy (ART); and evaluate its feasibility and performance implemented for ART of nasopharyngeal carcinoma (NPC).

MATERIALS/METHODS

This single center, prospective study has been approved by the ethical committee of the institution. The online ART workflow was developed based on a CT-integrated linear accelerator. During the course of radiotherapy, the patient underwent daily pre-treatment fan-beam CT (FBCT) scan. Then the FBCT was automatically registered to the original planning CT and used to assess the need for the patient to implement ART according to radiation oncologist's discretionary. The online ART workflow incorporates critical radiotherapy procedures from re-simulation, auto-segmentation by integrating image fusion and deep learning method, auto-replanning, beam delivery, and in vivo quality assurance (QA) into one scheme, while the patient is on the treatment couch during the whole process.

RESULTS

From 2th April 2022 to 5th January 2023, 20 patients with newly-diagnosed, non-metastatic NPC were enrolled in this study. Only one-time online ART was performed for each patient, because that the appropriate timing for triggering online ART was explored in parallel with this study. According to radiation oncologists' discretionary, the median fraction for performing online ART was at 21 fractions (interquartile range, 19-24 fractions). All patients were well tolerated and successfully completed the treatment. For tumor targets contouring, minor revisions were required for automated contours of the primary gross tumor volume (GTVp) and clinical target volumes (CTVs, including CTV1 and CTV2), with the mean DSC between before and after revision of 0.91±0.042, 0.94 ± 0.042 and 0.91 ± 0.061, respectively; and much more revisions for the automated contours of cervical lymph nodes GTV (GTVn), with the mean DSC of 0.74 ± 0.28. The automated contours of normal tissues were clinically acceptable with little modifications. Median time consuming for auto-segmentation and revision was 9.5 minutes (min). For treatment planning, 18 automated plans (90%) were passed at their first auto-optimization and two plans (10%) were passed after further optimization of the dose coverage of CTVs by physicist; and the median time consuming for auto-planning was 6.2 min. Time consuming for other procedures were as follows: re-simulation, 2.3 min; plan evaluation, 3.3 min; beam delivery, 4.6 min; and the duration of the entire process was 25.9 min, range from 19.4 min to 32.5 min.

CONCLUSION

We successfully established an AI-powered online ART workflow for adaptive radiotherapy of NPC, and confirmed that current auto-segmentation and auto-replanning methods are powered enough to support the clinical application of its online ART.

Individual Patient Data Analysis of 17 Randomized Trials vs. Real-World Data for Men with Localized Prostate Cancer Receiving Radiotherapy

PURPOSE/OBJECTIVE(S)

Prior work has demonstrated poor correlation between the results of randomized controlled trials (RCTs) and real-world evidence (RWD). However, patients enrolled in RCTs are often considered to poorly represent the real-world population. Herein, we utilize multiple large data repositories to determine differences in baseline characteristics and long-term outcomes between patients enrolled in RCTs and RWD that received radiotherapy for localized prostate cancer.

MATERIALS/METHODS

Meta-Analysis of Randomized trials in Cancer of the Prostate (MARCAP) Consortium was leveraged, and 17 phase III randomized trials were included. RWD were accessed through the Staging Collaboration for Cancer of the Prostate (STAR-CAP) cohort, a cohort that is comprised of >60 centers across the United States and Europe. Additionally, RWD was assessed via the Surveillance, Epidemiology, and End Results (SEER) database. MARCAP and STAR-CAP both contain outcomes for distant metastasis (DM), metastasis-free survival (MFS), prostate cancer-specific mortality (PCSM), and overall survival (OS). SEER only contains PCSM and OS. Wilcoxon signed-rank test and chi-square test were used to compare continuous and categorical variables, respectively. Inverse probability of treatment weighting (IPTW) analysis was conducted, balancing for age, PSA, Gleason score, T stage, and treatment year in the three cohorts. Cox and Fine-Gray regression models were used to compare disease outcomes between RCTs vs. RWD.

RESULTS

Data from 10,666 patients from RCTs, 6,530 patients in STAR-CAP, and 117,586 patients in SEER were included. SEER patients were slightly younger (p<0.001, median age 68 (IQR 62-73) than those in RCTs (70, IQR 65-74) and in STAR-CAP (70, IQR 64-74). 10-year OS in RCTs was 65.4%, STAR-CAP 70.2%, SEER 64.1%. OS was superior in STAR-CAP (RCTs as reference; HR 0.91, 95% CI 0.85-0.96, p<0.0001), but there was no significant difference between SEER and RCTs (HR 0.96, 95% CI 0.91-1.02, p = 0.22). 10-year PCSM cumulative incidence was 7.4% in RCTs, 8.1% in STAR-CAP, and 11.0% in SEER. There was no significant difference in PCSM between STAR-CAP RWD and RCTs (HR 0.88, 95% CI 0.78-1.01, p = 0.08), whereas PCSM was worse in SEER than RCTs (HR 1.37, 95% CI 1.21-1.55, p<0.0001). There was no significant difference in DM between STAR-CAP RWD and RCTs (HR 0.93, 95% CI 0.83-1.04, p = 0.2).

CONCLUSION

While baseline differences exist in patients enrolled on localized prostate cancer RCTs and real-world datasets, there were small if any significant relative differences in oncologic outcomes. This provides reassurance that RCT results are generally applicable to patients in routine practice.

Dosimetric Benefits of Online Adaptive Radiotherapy in Nasopharyngeal Carcinoma

PURPOSE/OBJECTIVE(S)

Online adaptive radiotherapy (ART) has the advantage of compensating for potential underdosing to targets and overdosing to organs-at-risk (OARs) caused by variations in patient anatomy and tumor geometry. Artificial intelligence (AI)-assisted rapid generation of new plans makes online ART possible. We aimed to evaluate the dosimetric benefits of online ART on tumor coverage and OARs sparing in nasopharyngeal carcinoma (NPC).

MATERIALS/METHODS

Twenty patients diagnosed with NPC (19 with stage III and 1 with stage II according to the 8th edition of the AJCC/UICC staging system) who underwent definitive radiotherapy or concurrent chemoradiotherapy and received online ART on CT-Linac between April 2022 and December 2022 were included in this study, consisting of 14 males and 6 females with a median age of 48 years (range: 29-68 years). The prescription dose was 6996 cGy/33 fractions for primary gross tumor volume (GTVp), 6600-6996 cGy/33 fractions for gross tumor volume of nodes (GTVn), 6006 cGy/33 fractions for high-risk clinical tumor volume (CTV1), 5412 cGy/33 fractions for low-risk clinical tumor volume (CTV2). The majority of the patients (15/20) received online ART during the fourth to fifth week of their radiotherapy treatment The auto-segmented contours and auto-plan generated by AI were manually reviewed and edited by radiotherapists and physicists. The paired samples t-test was used to compare the dose and volumes metrics of targets and OARs between scheduled plan and online ART plan.

RESULTS

The results of this study showed that compared to the scheduled plan, the online ART plan resulted in significant reductions in the volumes of all targets and 8/12 OARs (temporal lobes, optic nerves, lenses, eyes, parotids, submandibulars, mandibles, and thyroid) (P<0.05). The online ART plan also improved target coverage, with D98% for GTVp in the scheduled plan compared to the online ART plan being 7063.4 ± 76.1 cGy and 7096.1 ± 53.9 cGy (P = 0.1), CTV1 being 6266.7 ± 114.9 cGy and 6208.7 ± 54.7 cGy (P<0.05), and CTV2 being 4142.5 ± 1700.9 cGy and 5416.4 ± 23.8 cGy (P<0.01), respectively. The dose to all 12 OARs was reduced with the use of online ART, with 5/12 OARs showing statistical significance. The D0.03cm3 for the spinal cord in the scheduled plan and online ART plan were 3630.9 ± 197.6 and 3454.1 ± 132.0 cGy; for the temporal lobes were 7075.2 ± 303.0 and 6994.2 ± 345.1 cGy; and 4396.0 ± 2575.0 and for the pituitary were 4214.5 ± 2499.2 cGy. Meanwhile the Dmean for the eyes in the scheduled plan and online ART plan was 769.0 ± 232.0 and 714.8 ± 200.1 cGy; and for the mandibles were 3187.7 ± 211.5 and 3066.0 ± 152.1 cGy.

CONCLUSION

Online ART was effective in protecting most of the OARs in NPC patients, while simultaneously indicating a trend towards enhancing target coverage. This study demonstrated the promising potential of online ART for patients with NPC. This approach will be tested in an upcoming phase III trial.

Deep Learning-Based Synthesis of Contrast-Enhanced MRI for Automated Delineation of Primary Gross Tumor Volume in Radiotherapy of Nasopharyngeal Carcinoma

PURPOSE/OBJECTIVE(S)

Contrast-enhanced MRIs are necessary to delineate the primary gross tumor volume (GTVp) in radiotherapy of nasopharyngeal carcinoma (NPC). However, using contrast agents to scan contrast-enhanced MRIs is not applicable to some patients due to metal implants or their allergy, and it increases the treatment cost of patients. To address these problems, this work aims at synthesizing contrast-enhance MRIs from unenhanced MRIs by implementing generative adversarial network (GAN).

MATERIALS/METHODS

In this work, 324 MRI datasets of patients with NPC were retrospectively collected between September 2016 and September 2017 from a single institute. MRI examinations were performed with un-enhanced T1-weighted (T1) and T2-weighted (T2) sequences, and contrast-enhanced T1-weighted (T1C) and fat-suppressed T1-weighted (T1FSC) sequences. We designed and developed a modified pix2pix network to synthesize T1C (sT1C) and T1FSC (sT1FSC) from real T1. The end of the generator in this network was assembled with multiple heads (the classification head and gradient head) to learn more representation information and features from real images, the discriminator in this network distinguished whether the synthesized image is real and fake and supervised that the generator outputs more realistic synthesized image. We verified the performance of the synthesized images for automated delineation of GTVp. In an independent testing set of 11 patients, the synthesized sT1C and sT1FSC were inputted into the segmentation deep learning network along with their corresponding T1 and T2 sequences to generate GTVp contours. Delineation performance of the synthesized images and real images for automated delineation were evaluated by dice similarity coefficient (DSC), and average surface distance (ASD), using human expert contours as the ground truth.

RESULTS

In automated contouring of GTVp for NPC, the segmentation deep learning network using one or two synthesized MRIs showed equivalent performance when compared with the automated contours which generated from four real MRI sequences. Mean DSCs between automated contours by sT1C-replaced or sT1C and sT1FSC-replaced network and ground truth contours were 0.726 ± 0.143 and 0.711 ± 0.157, respectively, slightly inferior to that of contours generated from four real MRI sequences (0.740 ± 0.154, both P >0.05). In terms of mean ASD, there was also no significant difference between automated contours generated from synthesized images and real images (3.056 ± 4.216 mm and 3.537 ± 4.793 mm vs. 3.124 ± 4.637 mm; both P > 0.05).

CONCLUSION

We proposed an MRI-synthesis method based on GAN and the synthesized contrast-enhanced MRIs performed equivalent as the real contrast-enhanced MRIs in the automated delineation of gross tumor volume for radiotherapy of NPC.

Expression of FOXO3 in the skin follicles of goose embryos during embryonic development

1. The Forkhead box O3 (FOXO3) transcription factor is a crucial regulator in controlling cell metabolism, proliferation, apoptosis, migration and response to oxidative stress. However, FOXO3 has not previously been studied much in the embryonic skin follicles of geese.2. This study used Zhedong white geese (Anser cygnoides), Jilin white geese (Anser cygnoides) and Hungarian white geese (Anser anser). The feather follicle structure in the dorsal skin during embryonic stages was examined with haematoxylin and eosin (HE) and Pollak staining. The FOXO3 protein content in the embryonic dorsal skin from feather follicles was detected using western blotting and quantitative real-time PCR.3. The mRNA expression level of FOXO3 in the dorsal skin of Jilin white geese was highly expressed on embryonic day 23 (E23; P < 0.01), while mRNA expression of FOXO3 was highly expressed in the feather follicle of Hungarian white geese at E28 (P < 0.01). The expression of FOXO3 protein mainly concentrated in the early embryonic phase among these goose breeds (P < 0.05). This suggested that FOXO3 plays a crucial role in the development and growth of embryonic dorsal skin of feather follicles. The location of the FOXO3 protein was determined using the IHC technique, which further verified the effect of FOXO3 in the dorsal skin for feather follicles during embryogenesis.4. The study demonstrated the differential expression and localisation of the FOXO3 gene among different goose species. It was speculated that the gene could potentially improve goose feather follicle development and feather-related traits and provide a basis for further understanding of FOXO3 function in the dorsal tissue of goose embryos.

Impact of Prior Radiation Therapy on Bone Mineral Density Change Over Time: Secondary Analysis of the Control Arm of a Phase III Randomized Trial

PURPOSE/OBJECTIVE(S)

Retrospective studies have demonstrated that pelvic radiation therapy (RT) can lead to decreased bone mineral density (BMD) and increased risk of fracture. This is more relevant for men with prostate cancer (PCa) who often receive androgen deprivation therapy (ADT) in conjunction with RT. We performed a post-hoc secondary analysis of publicly available data of the control arm of a phase III randomized controlled study (NCT00089674) to determine if history of prior pelvic RT affects change in BMD over time in non-metastatic PCa patients treated with ADT.

MATERIALS/METHODS

In this study, PCa patients with age ≥70 years or <70 years with low BMD (T-score <-1) or history of osteoporotic fracture, on ADT for at least 12 months were randomized to receive densoumab vs. placebo every 6 months for 3 years. Additionally, all patients received daily vitamin D and calcium supplementation. Randomization was stratified by duration of prior ADT (≤6 months vs >6 months) and age (<70 vs ≥70 years). BMD was measured at baseline, and at months 1, 3, 6, 12, 24, and 36 with blind reading by central reviewer. To model the effect of prior pelvic RT on dynamic change in BMD in the hip, lumbar spine, and femoral neck, we applied separate multivariate linear mixed effect models for each site. Age, ECOG performance score, history and number of prior fractures, smoking history, and years from initial cancer diagnosis were included as fixed covariates while patients were included as random intercepts.

RESULTS

Among 734 patients who were randomized to the control arm, 563 participants with baseline and at least one post baseline assessment of BMD were eligible for this analysis. Overall, 34.4% (n = 194) received prior RT. We did not find any significant association of dynamic change in BMD with receipt of prior pelvic RT for left femoral neck (p = 0.7), total hip (p = 0.8), and lumbar spine (p = 0.5), respectively. At 36 months, there was no significant association of prior RT with percent change in BMD in femoral neck (odds ratio [OR]: 0.85; 95% confidence interval [CI]: 0.30-2.41), total hip (OR: 0.96; 95% CI: 0.43-2.15), and lumbar spine (OR: 2.01; 95% CI: 0.63-6.45). However, note should be made of the opposite direction of association of prior RT with percent BMD change at 36 months for femoral neck and hip versus lumbar spine.

CONCLUSION

In this exploratory analysis of the control arm of a phase III randomized trial, we did not find sufficient evidence of an association between prior pelvic RT and dynamic changes in BMD in femoral neck, hip, and lumbar spine over time in men with non-metastatic PCa and low BMD at baseline. This analysis should be interpreted cautiously considering its post-hoc nature with likely inadequate power, the possibility of selection bias, lack of information on receipt of prior ADT, and missing data in longitudinal assessments.

Association of Prostate Specific Antigen Kinetics after Testosterone Recovery with Subsequent Recurrence: Secondary Analysis of a Phase III Randomized Controlled Trial

PURPOSE/OBJECTIVE(S)

The combination of short-term androgen deprivation therapy (ST-ADT) with prostate radiotherapy (RT) is a standard of care for patients with localized prostate cancer (LPCa). After cessation of ST-ADT, it takes about 8 to 10 months for the testosterone (T) to recover to supracastrate levels, which could drive changes in PSA kinetics. It largely remains unknown whether early changes in PSA kinetics after T recovery could predict for subsequent biochemical relapse.

MATERIALS/METHODS

We performed a secondary analysis of a phase III randomized controlled trial in which patients with newly diagnosed LPCa with Gleason score £7, clinical stage T1b to T3a, and PSA <30 ng/mL were randomly allocated to neoadjuvant and concurrent ADT for 6 months starting 4 months before prostate RT (76 Gy in 38 fractions over 7.5 weeks) or concurrent and adjuvant ADT for 6 months starting simultaneously with prostate RT. Clinical assessment and laboratory investigations were repeated 1 month after completion of ADT, every 4 months for the first 2 years, every 6 months for the next 3 years, and annually thereafter. We calculated the PSA doubling time (PSADT) based on PSA values up to 18 months after recovery of T to a supracastrate level (>50 ng/dL). Patients with ³3 PSA measurements after T recovery to supracastrate level were included in this analysis. Fine and Gray cumulative incidence of biochemical recurrence (BCR) was calculated in patients with PSADT at or above median versus below median. Deaths were considered as competing events. All endpoints were calculated from the time of T recovery to supracastrate level. Subdistribution hazard ratios (sHR) with 95% confidence intervals (CI) were estimated for association of PSADT with relative incidence of recurrence using competing risk regression after adjusting for tumor stage, pre-treatment PSA, Gleason score, treatment regimen, and age at randomization.

RESULTS

Overall, 311 patients were eligible for this analysis. Median PSADT was 8 months. Cumulative incidence of BCR at 10 years was 31.0% and 20.7% in patients with PSADT <8 months and ³8 months, respectively. Longer PSADT was associated with a significantly lower risk of cumulative incidence of BCR (sHR for PSADT as a continuous variable 0.43, 95% CI: 0.28-0.66; sHR for PSADT ³8 months 0.54, 95% CI: 0.30-0.99). After adjustment for time to recovery of T to supracastrate level in addition to the aforementioned variables, longer PSADT (³8 months) was associated with lower risk of cumulative incidence of BCR (sHR: 0.53, 95% CI: 0.27-1.01).

CONCLUSION

These findings suggest that early PSA kinetics within 18 months of recovery of T to a supracastrate level predict for subsequent biochemical failure. Taking account of early changes in PSA after testosterone recovery may allow for recognition of potential failures earlier in the disease course and thereby permit greater personalization of management decisions.

Outcomes of Consolidative Thoracic Radiation within First-Line Chemoimmunotherapy in Extensive-Stage Small-Cell Lung Cancer: Results from a Single Cancer Center

PURPOSE/OBJECTIVE(S)

Thoracic radiation (TRT) benefits local control undoubtedly and survival with some minor controversy in extensive-stage small-cell lung cancer (ES-SCLC) patients undergoing radiotherapy in the chemoradiotherapy era. However, whether TRT could further enhance the benefit of immune checkpoint inhibitors (ICIs) maintenance on outcomes in the immunotherapy era is still unclear. This study aims to investigate the role of consolidative TRT in ES-SCLC patients receiving first-line chemoimmunotherapy followed by immunotherapy maintenance.

MATERIALS/METHODS

Outcomes of patients who were treated with first-line chemo-immunotherapy followed by ICIs maintenance for ES-SCLC were reviewed. Based on TRT or not, patients were allocated to TRT group or non-TRT group. Progression-free survival (PFS), overall survival (OS) and local-recurrence free survival (LRFS) were calculated by the Kaplan-Meier method and compared by log-rank test.

RESULTS

A total of 100 patients with no progressive disease after 4 cycles of chemotherapy were retrospectively analyzed between January 2020 and December 2021 and were allocated into TRT group (n = 47) and non-TRT group (n = 53). The median follow-up time was 20.3 months. The median PFS and OS in TRT were 9.1 months and 21.8 months, versus 8.8 months (p = 0.93) and 24.3 months (p = 0.63), respectively, in non-TRT. ICIs agents consisted of Durvalumab (59.0%) and Atezolizumab (41.0%). The median dose of TRT is 50 Gy (IQR: 45 - 54), while the median interval time from chemotherapy completion to TRT was 31 days (IQR: 12 - 44.5). Only 10 (21.3%) patients terminated ICIs in the period of TRT. The rate of intrathoracic progression after the first-line therapy in TRT significantly decreased compared to that with non-TRT (20.0% versus 55.9%, p = 0.003). The median LRFS time in TRT was not reached, but significantly longer than 10.8 months in non-TRT (HR = 0.27, p < 0.01). Second-line chemotherapy significantly prolonged survival compared to that with chemo-free patients (mOS: 24.5 vs. 21.4 months, p = 0.026). The subgroup analysis showed a trend of patients with brain metastases benefit from TRT (21.8 versus 13.7 months, HR 0.61, p = 0.38) while liver metastases did not (13.3 versus 15.0 months, HR 1.80, p = 0.21). Of 47 patients with TRT, only 10.6% of patients experienced grade 3 radiation-induced pneumonitis, while no grade 4 or 5 adverse events occurred. None of patients experienced grade ≥ 3 treatment-related cardiac events.

CONCLUSION

Consolidative TRT in the period of immunotherapy maintenance followed first-line chemo-immunotherapy did not prolong OS and PFS but increased LRFS in ES-SCLC.

m6A Reader YTHDC1 Inhibits Ferroptosis and Radiosensitivity by Promoting SREBF1 mRNA Nuclear Export in Nasopharyngeal Carcinoma

PURPOSE/OBJECTIVE(S)

Radioresistance is the main reason for nasopharyngeal carcinoma (NPC) recurrence leading to treatment failure, and inducing ferroptosis has gradually been a new way to enhance radiosensitivity. N6-methyladenosine (m6A) is involved in regulation of numerous biological processes. However, whether m6A affects ferroptosis in NPC is still unclear. In this study, we conducted a siRNA library screening to identify m6A reader YTHDC1 as an essential oncogene that suppressed ferroptosis and radiosensitivity by promoting SREBF1 mRNA nuclear export in nasopharyngeal carcinoma.

MATERIALS/METHODS

The expression and function of YTHDC1 were assessed via CCK8 cell viability assay, immunostaining, real-time PCR, western blot, radiation clonogenic assay and fluorescence in situ hybridization assay. Ferroptosis was determined by detecting cell viability, lipid peroxidation, abnormal mitochondrial and cell death rate. The in vivo effects of YTHDC1 were examined with RSL3 treatment or lentivirus modification of YTHDC1 expression in radiated mouse models.

RESULTS

Based on RSL3-induced ferroptotic cell death model and a siRNA library about m6A modification associated gene screening, we identified m6A reader YTHDC1 could inhibit ferroptosis as well as radiosensitivity of NPC, both in vivo and in vitro. Mechanistically, YTHDC1 protein could recognize m6A sites in the CDS region and 3' untranslated region (3'UTR) of SREBF1 mRNA and promote SREBF1 mRNA nuclear export, which finally resulted in transcriptional upregulation of genes key to ferroptosis such as SCD and FASN. Furthermore, the high expression of YTHDC1 was negatively regulated by ZNF598 via ubiquitination and associated with unfavorable survival in NPC patients due to radioresistance.

CONCLUSION

Our findings reveal the critical role of YTHDC1 specifically in inhibiting ferroptosis and radiosensitivity via m6A-dependent mechanism and provide an exploitable target and therapeutic strategy for overcoming radioresistance in NPC.

Clinical prior Knowledge-Based One-Shot Learning for Automatic Delineation of Clinical Target Volumes in Adaptation Radiotherapy of Cervical Cancer

PURPOSE/OBJECTIVE(S)

Rapid and accurate delineation of clinical target volumes (CTV) of cervical cancer is the crux to ensure the efficiency and benefits of adaptation radiotherapy (ART). However, contour propagation using deformation image registration (DIR) is difficult to ensure the accuracy of CTV contours due to the significant tumor recession in next fraction, and the tumor progress in each fraction is not considered by conventional automatic delineation methods based on deep learning (DL). Currently, one-shot learning (OSL) is feasible to learn the tumor progress from former fractions to improve the accuracy of automatically delineating CTV.

MATERIALS/METHODS

We retrospectively collected 45 patients with cervical cancer from January 2021 to May 2022 in our department. All patients consist of a pair of planning CT and daily CT in ART. A personalized automatic delineation method based on one-shot learning was developed to delineate CTV in daily CT by learning the clinical prior knowledge from the CTV contours and images of planning CT. The performance of our proposed method was evaluated by dice similarity coefficient (DSC), 95% Harsdorff distance (95HD) and average surface distance (ASD) with human experts, and its automatic delineation performance were compared with DIR and DL in daily CT.

RESULTS

Our automatic delineation method OSL performed the best results in all evaluation metrics (denoted by mean ± standard deviation) as shown in Table 1, it is superior to method DL: 0.92 & 0.90 of DSC, 2.33 mm & 2.68 mm of HD95, 0.68 mm & 0.82 mm of ASD, P < 0.05 for DSC and ASD. Specifically, our method is significantly superior to the automatic delineation results by method DIR: 0.92 & 0.84 of DSC, 2.33 mm & 4.11 mm of HD95, 0.68 mm & 1.52 mm of ASD, P < 0.05 for all. In addition, OSL can significantly overcome the delineation problems in fuzzy boundary and delineation missing and perform better generalization for some unusual images, compared with DIR and DL.

CONCLUSION

We proposed an automatic delineation method based on one-shot learning for CTV of cervical cancer in ART, the results demonstrated that the proposed method could improve the precision and generalization of automatically delineating CTV compared against current popular methods. Therefore, it is potential to improve the quality and efficiency of ART for personalized patients and have a positive impact on tumor control and patient survival.

A Feasible Study for Auto Planning and Auto Re-Planning for Nasopharyngeal Carcinoma (NPC) Adaptive Radiation Therapy (ART)

PURPOSE/OBJECTIVE(S)

To explore the necessity of Image Guided Radiation Therapy (IGRT) for Nasopharyngeal Carcinoma (NPC) adaptive radiation therapy (ART), and evaluate the consistency and robustness of auto re-planning during ART.

MATERIALS/METHODS

Eleven NPC patients were enrolled in this study at one institution. We used a CT-integrated linear accelerator, which integrates a 16-slice helical CT to acquire diagnostic-grade fan-beam CT (FBCT) for IGRT. Electron density accuracy from FBCT provides a solid foundation for accurate radiation dose calculation. PGTVp, PTV1 and PTV2 prescription dose are 69.96 Gy, 60.06 Gy and 54.12 Gy with 33 fractions. All ROIs, including Targets and OARs, were auto delineated via a treatment planning system (TPS), and modified by a senior physician with more than 15-year experience to confirm that they follow the clinical requirement. An initial plan (Plan A) was automatically generated based on the first CT-Sim images on the TPS. Another adjusted re-plan (Plan B) was also automatically generated based on the second CT-Sim images after 20 fractions of treatment for ART implementation. During the whole course of the 33 fractions delivery, there are 20 fractions Plan A (with 4 weekly IGRTi, i = 1∼4) and 13 fractions Plan B (with 3 weekly IGRTj, j = 5∼7). After carefully rigid registration between the CT-Sim images and their following weekly FBCT images, we copied Plan A and Plan B to IGRTi and IGRTj, respectively. Plan_IGRT would be re-calculated for dose evaluation. In addition, the Plan A was copied to the second CT-Sim (Plan A_2nd CT-Sim) after first CT-Sim and second CT-Sim rigid images registration.

RESULTS

There is a significant target volume change of -5%±4%, -3%±3%, and -5%±3% from Plan A to Plan B, for PGTVp, PTV1 and PTV2 (p<0.05), respectively. All the Plan A and Plan B could be generated within 210.2s±1.4s, which is more time-saving than manual planning greatly, and there is no statistical difference between Plan A and Plan B of the plan quality index (p>0.05). The plans for IGRT7 are inferior to the plans for IGRT5 with higher V110% for PGTVp (4.40%±8.60% for Plan A, 2.37%±8.91% for Plan B). PlanA_2nd CT-Sim for each patient is inferior to Plan B, with higher V110% for PGTVp (19.12%±18.91%), lower V100% for PTV2 (-2.84%±2.89%) and higher Dmax for Brainstem (315.88 cGy ± 190.39 cGy) statistically. Furthermore, all the Plan B_IGRTj are superior to Plan A_IGRTj, with the dose index difference of -17.50% ± 23.15%/-15.47% ± 14.85%, 2.45% ± 3.23%/2.31% ± 3.09% and -194.03 cGy ± 221.91 cGy/-170.07 cGy ± 168.41 cGy for V110% of PGTVp, V100% for PTV2 and Dmax of Brainstem for j = 5/7 (p<0.05), respectively.

CONCLUSION

The world's first integrated CT-Linac platform, equipped with FBCT, can provide a diagnostic-quality FBCT for achieve offline ART. It is necessary for NPC patients to have the IGRT, ART and re-planning after 20 fractions treatment, for the target volumes change sharply. Auto planning and auto re-planning for NPC ART are able to maintain the plan consistency and robustness while shorten the planning time.

Observation of New Ω_{c}^{0} States Decaying to the Ξ_{c}^{+}K^{-} Final State

Two new excited states, Ω_{c}(3185)^{0} and Ω_{c}(3327)^{0}, are observed in the Ξ_{c}^{+}K^{-} invariant-mass spectrum using proton-proton collision data collected by the LHCb experiment, corresponding to an integrated luminosity of 9  fb^{-1}. Five previously observed excited Ω_{c}^{0} states are confirmed, namely Ω_{c}(3000)^{0}, Ω_{c}(3050)^{0}, Ω_{c}(3065)^{0}, Ω_{c}(3090)^{0}, and Ω_{c}(3119)^{0}. The masses and widths of these seven states are measured with the highest precision to date.

Evidence of a J/ψK_{S}^{0} Structure in B^{0}→J/ψϕK_{S}^{0} Decays

An amplitude analysis of B^{0}→J/ψϕK_{S}^{0} decays is performed using proton-proton collision data, corresponding to an integrated luminosity of 9  fb^{-1}, collected with the LHCb detector at center-of-mass energies of 7, 8, and 13 TeV. Evidence with a significance of 4.0 standard deviations of a structure in the J/ψK_{S}^{0} system, named T_{ψs1}^{θ}(4000)^{0}, is seen, with its mass and width measured to be 3991_{-10}^{+12} _{-17}^{+9}  MeV/c^{2} and 105_{-25}^{+29} _{-23}^{+17}  MeV, respectively, where the first uncertainty is statistical and the second systematic. The T_{ψs1}^{θ}(4000)^{0} state is likely to be the isospin partner of the T_{ψs1}^{θ}(4000)^{+} state, previously observed in the J/ψK^{+} system of the B^{+}→J/ψϕK^{+} decay. When isospin symmetry for the charged and neutral T_{ψs1}^{θ}(4000) states is assumed, the signal significance increases to 5.4 standard deviations.

[Value of autocrine motility factors in the prediction of the disease progression of PBC- associated hepatocellular carcinoma]

Objective: To clarify the value of autocrine motility factor (ATX) in predicting the disease progression of primary biliary cholangitis (PBC)-associated hepatocellular carcinoma (HCC). Methods: A prospective cohort of 179 newly diagnosed autoimmune liver disease (PBC) patients admitted to the Department of Hepatology at the Fifth Medical Center of the People's Liberation Army General Hospital from January 2016 to January 2018 was selected. All PBC patients received ursodeoxycholic acid (UDCA) treatment and were followed up.The endpoint of the follow-up was the occurrence of primary liver cancer. The relationship between ATX and the clinical characteristics of patients and its significance in predicting disease progression and HCC were analyzed. Results: The peripheral blood ATX level was significantly higher in PBC patients than that of alcoholic cirrhosis (t = 3.278, P = 0.001) and healthy controls (t = 6.594, P < 0.001), but there was no significant difference in ATX levels compared with patients with non-PBC- associated HCC (t = -0.240, P = 0.811). The expression of ATX in liver tissue of PBC patients was significantly higher than that of healthy individuals (Z = -3.633, P < 0.001) and patients with alcoholic cirrhosis (Z = -3.283, P < 0.001), while the expression of ATX in the advanced stage was significantly higher than that in early-stage PBC patients (Z = -2.018, P = 0.034). There was a significant difference in baseline ATX levels between PBC patients without HCC and PBC patients with HCC (228.451 ± 124.093 ng/ml vs. 301.583 ± 100.512 ng/ml, t = 2.339, P = 0.021). Multivariate logistic regression analysis showed that ATX was an independent predictor of PBC progression to HCC (OR = 1.245, 95%CI 1.097-1.413). The baseline peripheral blood ATX level in predicting AUROC of PBC-associated HCC was 0.714, 95%CI 0.597-0.857 and the sensitivity and specificity were 84.6%, and 59.0%, respectively. The optimal cutoff value for predicting serum ATX levels in the occurrence of HCC was 235.254 ng/ml. Conclusion: Patients with PBC have significantly higher levels of ATX expression in their peripheral blood and liver tissue, which can be utilized to assess treatment effectiveness and predict disease progression.

[The predictive value of melanin-concentrating hormone combined with other related biomarkers in cerebrospinal fluid in preoperative cognitive dysfunction of elderly patients]

Objective: To explore the predictive value of cerebrospinal fluid melanin-concentrating hormone (MCH) combined with other related biomarkers in preoperative cognitive dysfunction of elderly patients. Methods: A total of 80 patients who underwent elective hip or knee replacement under intravertebral anesthesia in Chifeng Municipal Hospital, Inner Mongolia, from March to November 2022 were prospectively included, with 32 males and 48 females, and aged 65-85 (70.7±5.2) years old. According to the evaluation results of the Montreal Cognitive Assessment (MoCA), patients were divided into the preoperative cognitive dysfunction (n=23) and control (n=57) groups. The levels of MCH, amyloid-β 40 (Aβ40), amyloid-β 42 (Aβ42), and phosphorylated tau protein (p-tau) in cerebrospinal fluid were determined by enzyme-linked immunosorbent assay (ELISA). The receiver operating characteristic (ROC) curve was drawn to evaluate the predictive value of each biomarker separately or in combination for preoperative cognitive dysfunction. Spearman's rank correlation analysis was utilized to test the correlation between the level of each biomarker and MoCA scores. Results: The levels of MCH, Aβ40, Aβ42, p-tau, and Aβ42/p-tau in the preoperative cognitive dysfunction group were (35.53±5.94) μg/L, (39.21±9.18) ng/L, (221.83±43.17) ng/L, (42.64±9.74) ng/L, and 5.53±1.92, and the levels of these biomarkers in the control group were (28.74±4.90) μg/L, (36.37±7.87) ng/L, (280.23±45.67) ng/L, (35.00±9.27) ng/L, and 8.62±2.78, respectively. Compared with the control group, the levels of cerebrospinal fluid MCH and p-tau in the preoperative cognitive dysfunction group were significantly increased (all P<0.01), and the levels of Aβ42 and Aβ42/p-tau were significantly decreased (all P<0.001). MCH and Aβ42/p-tau provided higher predictive values. The area under the curve (AUC) of MCH and Aβ42/p-tau were 0.807 (95%CI: 0.703-0.911) and 0.842 (95%CI: 0.741-0.943), the sensitivity were 78.3% and 87.0%, and the specificity were 75.4% and 94.7%. MCH combined with Aβ42/p-tau have the higher AUC of 0.915 (95%CI: 0.837-0.992), the sensitivity (87.0%) and specificity (86.0%) were both high, which had a higher predictive value. The levels of cerebrospinal fluid MCH and p-tau were negatively correlated with MoCA score (r=-0.467, -0.321, all P<0.01), and the levels of Aβ42 and Aβ42/p-tau were positively correlated with MoCA score (r=0.480, 0.520, all P<0.001). Conclusion: The increase in cerebrospinal fluid MCH levels is associated with preoperative cognitive dysfunction in elderly patients. MCH combined with Aβ42/p-tau has the greatest predictive value.

Measurement of the Ratios of Branching Fractions R(D^{*}) and R(D^{0})

The ratios of branching fractions R(D^{*})≡B(B[over ¯]→D^{*}τ^{-}ν[over ¯]_{τ})/B(B[over ¯]→D^{*}μ^{-}ν[over ¯]_{μ}) and R(D^{0})≡B(B^{-}→D^{0}τ^{-}ν[over ¯]_{τ})/B(B^{-}→D^{0}μ^{-}ν[over ¯]_{μ}) are measured, assuming isospin symmetry, using a sample of proton-proton collision data corresponding to 3.0  fb^{-1} of integrated luminosity recorded by the LHCb experiment during 2011 and 2012. The tau lepton is identified in the decay mode τ^{-}→μ^{-}ν_{τ}ν[over ¯]_{μ}. The measured values are R(D^{*})=0.281±0.018±0.024 and R(D^{0})=0.441±0.060±0.066, where the first uncertainty is statistical and the second is systematic. The correlation between these measurements is ρ=-0.43. The results are consistent with the current average of these quantities and are at a combined 1.9 standard deviations from the predictions based on lepton flavor universality in the standard model.

Measurement of the Prompt D^{0} Nuclear Modification Factor in p-Pb Collisions at sqrt[s_{NN}]=8.16 TeV

The production of prompt D^{0} mesons in proton-lead collisions in both the forward and backward rapidity regions at a center-of-mass energy per nucleon pair of sqrt[s_{NN}]=8.16  TeV is measured by the LHCb experiment. The nuclear modification factor of prompt D^{0} mesons is determined as a function of the transverse momentum p_{T}, and the rapidity in the nucleon-nucleon center-of-mass frame y^{*}. In the forward rapidity region, significantly suppressed production with respect to pp collisions is measured, which provides significant constraints on models of nuclear parton distributions and hadron production down to the very low Bjorken-x region of ∼10^{-5}. In the backward rapidity region, a suppression with a significance of 2.0-3.8 standard deviations compared to parton distribution functions in a nuclear environment expectations is found in the kinematic region of p_{T}>6  GeV/c and -3.25 Collapse

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Measurement of the Time-Integrated CP Asymmetry in D^{0}→K^{-}K^{+} Decays

The time-integrated CP asymmetry in the Cabibbo-suppressed decay D^{0}→K^{-}K^{+} is measured using proton-proton collision data, corresponding to an integrated luminosity of 5.7  fb^{-1} collected at a center-of-mass energy of 13 TeV with the LHCb detector. The D^{0} mesons are required to originate from promptly produced D^{*+}→D^{0}π^{+} decays, and the charge of the companion pion is used to determine the flavor of the charm meson at production. The time-integrated CP asymmetry is measured to be A_{CP}(K^{-}K^{+})=[6.8±5.4±1.6]×10^{-4} where the first uncertainty is statistical and the second systematic. The direct CP asymmetries in D^{0}→K^{-}K^{+} and D^{0}→π^{-}π^{+} decays, a_{K^{-}K^{+}}^{d} and a_{π^{-}π^{+}}^{d}, are derived by combining A_{CP}(K^{-}K^{+}) with the time-integrated CP asymmetry difference, ΔA_{CP}=A_{CP}(K^{-}K^{+})-A_{CP}(π^{-}π^{+}), and other inputs, giving a_{K^{-}K^{+}}^{d}=(7.7±5.7)×10^{-4},a_{π^{-}π^{+}}^{d}=(23.2±6.1)×10^{-4},with a correlation coefficient corresponding to ρ=0.88. The compatibility of these results with CP symmetry is 1.4 and 3.8 standard deviations for D^{0}→K^{-}K^{+} and D^{0}→π^{-}π^{+} decays, respectively. This is the first evidence for direct CP violation in a specific D^{0} decay.