Personalised follow-up and management schema for patients with screen-detected pulmonary nodules: A dynamic modelling study

BACKGROUND

Selecting the time target for follow-up testing in lung cancer screening is challenging. We aim to devise dynamic, personalized lung cancer screening schema for patients with pulmonary nodules detected through low-dose computed tomography.

METHODS

We developed and validated dynamic models using data of pulmonary nodule patients (aged 55-74 years) from the National Lung Screening Trial. We predicted patient-specific risk profiles at baseline (R0) and updated the risk evaluation results in repeated screening rounds (R1 and R2). We used risk cutoffs to optimize time-dependent sensitivity at an early decision point (3 months) and time-dependent specificity at a late decision point (1 year).

RESULTS

In validation, area under receiver operating characteristic curve for predicting 12-month lung cancer onset was 0.867 (95 % confidence interval: 0.827-0.894) and 0.807 (0.765-0.948) at R0 and R1-R2, respectively. The personalized schema, compared with National Comprehensive Cancer Network (NCCN) guideline and Lung-RADS, yielded lower rates of delayed diagnosis (1.7% vs. 1.7% vs. 6.9 %) and over-testing (4.9% vs. 5.6% vs. 5.6 %) at R0, and lower rates of delayed diagnosis (0.0% vs. 18.2% vs. 18.2 %) and over-testing (2.6% vs. 8.3% vs. 7.3 %) at R2. Earlier test recommendation among cancer patients was more frequent using the personalized schema (vs. NCCN: 29.8% vs. 20.9 %, p = 0.0065; vs. Lung-RADS: 33.2% vs. 22.8 %, p = 0.0025), especially for women, patients aged ≥65 years, and part-solid or non-solid nodules.

CONCLUSIONS

The personalized schema is easy-to-implement and more accurate compared with rule-based protocols. The results highlight value of personalized approaches in realizing efficient nodule management.

Structural performance of detachable precast concrete column-column joint

A novel type of detachable precast concrete column-column joint (DPC) is proposed in this study to solve the problems in current column-column dry connections including complex load path, uncertainty of structural stiffness of beam-column joints and inconvenience for disassembly. The dry connection technology is applied by composing of steel plate and concrete. Finite element models of DPC were created to study its structural performance including hysteresis curve, skeleton curve, ductility, and energy dissipation capacity. The benchmark models are firstly established and validated against the test data and after that a small-scale parametric study is prepared. The effect of axial pressure ratio and eccentricity distance size on the seismic performance of DPC was studied. Results indict that the optimal value of axial pressure ratio ranges from 0.5 to 0.7. With increase of the axial pressure ratio, the ductility coefficient shows a decreasing trend in general. The eccentricity has little effect on the energy dissipation capacity of the joint.

Computational Screening of a Single-Atom Catalyst Supported by Monolayer Nb2S2C for Oxygen Reduction Reaction

The search for high-performance catalysts to improve the catalytic activity for an oxygen reduction reaction (ORR) is crucial for developing a proton exchange membrane fuel cell. Using the first-principles method, we have performed computational screening on a series of transition metal (TM) atoms embedded in monolayer Nb2S2C to enhance the ORR activity. Through the scaling relationship and volcano plot, our results reveal that the introduction of a single Ni or Rh atom through substitutional doping into monolayer Nb2S2C yields promising ORR catalysts with low overpotentials of 0.52 and 0.42 V, respectively. These doped atoms remain intact on the monolayer Nb2S2C even at elevated temperatures. Importantly, the catalytic activity of the Nb2S2C doped with a TM atom can be effectively correlated with an intrinsic descriptor, which can be computed based on the number of d orbital electrons and the electronegativity of TM and O atoms.

Toripalimab plus axitinib versus sunitinib as first-line treatment for advanced renal cell carcinoma: RENOTORCH, a randomized, open-label, phase III study

BACKGROUND

Immune checkpoint inhibitors in combination with tyrosine kinase inhibitors are standard treatments for advanced clear cell renal cell carcinoma (RCC). This phase III RENOTORCH study compared the efficacy and safety of toripalimab plus axitinib versus sunitinib for the first-line treatment of patients with intermediate-/poor-risk advanced RCC.

PATIENTS AND METHODS

Patients with intermediate-/poor-risk unresectable or metastatic RCC were randomized in a ratio of 1 : 1 to receive toripalimab (240 mg intravenously once every 3 weeks) plus axitinib (5 mg orally twice daily) or sunitinib [50 mg orally once daily for 4 weeks (6-week cycle) or 2 weeks (3-week cycle)]. The primary endpoint was progression-free survival (PFS) assessed by an independent review committee (IRC). The secondary endpoints were investigator-assessed PFS, overall response rate (ORR), overall survival (OS), and safety.

RESULTS

A total of 421 patients were randomized to receive toripalimab plus axitinib (n = 210) or sunitinib (n = 211). With a median follow-up of 14.6 months, toripalimab plus axitinib significantly reduced the risk of disease progression or death by 35% compared with sunitinib as assessed by an IRC [hazard ratio (HR) 0.65, 95% confidence interval (CI) 0.49-0.86; P = 0.0028]. The median PFS was 18.0 months in the toripalimab-axitinib group, whereas it was 9.8 months in the sunitinib group. The IRC-assessed ORR was significantly higher in the toripalimab-axitinib group compared with the sunitinib group (56.7% versus 30.8%; P < 0.0001). An OS trend favoring toripalimab plus axitinib was also observed (HR 0.61, 95% CI 0.40-0.92). Treatment-related grade ≥3 adverse events occurred in 61.5% of patients in the toripalimab-axitinib group and 58.6% of patients in the sunitinib group.

CONCLUSION

In patients with previously untreated intermediate-/poor-risk advanced RCC, toripalimab plus axitinib provided significantly longer PFS and higher ORR than sunitinib and had a manageable safety profile TRIAL REGISTRATION: ClinicalTrials.gov NCT04394975.

Factors associated with nocturnal and diurnal glycemic variability in patients with type 2 diabetes: a cross-sectional study

PURPOSE

There is little information on factors that influence the glycemic variability (GV) during the nocturnal and diurnal periods. We aimed to examine the relationship between clinical factors and GV during these two periods.

METHODS

This cross-sectional study included 134 patients with type 2 diabetes. 24-h changes in blood glucose were recorded by a continuous glucose monitoring system. Nocturnal and diurnal GV were assessed by standard deviation of blood glucose (SDBG), coefficient of variation (CV), and mean amplitude of glycemic excursions (MAGE), respectively. Robust regression analyses were performed to identify the factors associated with GV. Restricted cubic splines were used to determine dose-response relationship.

RESULTS

During the nocturnal period, age and glycemic level at 12:00 A.M. were positively associated with GV, whereas alanine aminotransferase was negatively associated with GV. During the diurnal period, homeostatic model assessment 2-insulin sensitivity (HOMA2-S) was positively associated with GV, whereas insulin secretion-sensitivity index-2 (ISSI2) was negatively associated with GV. Additionally, we found a J-shape association between the glycemic level at 12:00 A.M. and MAGE, with 9.0 mmol/L blood glucose level as a cutoff point. Similar nonlinear associations were found between ISSI2 and SDBG, and between ISSI2 and MAGE, with ISSI2 value of 175 as a cutoff point.

CONCLUSION

Factors associated with GV were different between nocturnal and diurnal periods. The cutoff points we found in this study may provide the therapeutic targets for beta-cell function and pre-sleep glycemic level in clinical practice.

Personalized Computational Causal Modeling of the Alzheimer Disease Biomarker Cascade

BACKGROUND

Mathematical models of complex diseases, such as Alzheimer's disease, have the potential to play a significant role in personalized medicine. Specifically, models can be personalized by fitting parameters with individual data for the purpose of discovering primary underlying disease drivers, predicting natural history, and assessing the effects of theoretical interventions. Previous work in causal/mechanistic modeling of Alzheimer's Disease progression has modeled the disease at the cellular level and on a short time scale, such as minutes to hours. No previous studies have addressed mechanistic modeling on a personalized level using clinically validated biomarkers in individual subjects.

OBJECTIVES

This study aimed to investigate the feasibility of personalizing a causal model of Alzheimer's Disease progression using longitudinal biomarker data.

DESIGN/SETTING/PARTICIPANTS/MEASUREMENTS

We chose the Alzheimer Disease Biomarker Cascade model, a widely-referenced hypothetical model of Alzheimer's Disease based on the amyloid cascade hypothesis, which we had previously implemented mathematically as a mechanistic model. We used available longitudinal demographic and serial biomarker data in over 800 subjects across the cognitive spectrum from the Alzheimer's Disease Neuroimaging Initiative. The data included participants that were cognitively normal, had mild cognitive impairment, or were diagnosed with dementia (probable Alzheimer's Disease). The model consisted of a sparse system of differential equations involving four measurable biomarkers based on cerebrospinal fluid proteins, imaging, and cognitive testing data.

RESULTS

Personalization of the Alzheimer Disease Biomarker Cascade model with individual serial biomarker data yielded fourteen personalized parameters in each subject reflecting physiologically meaningful characteristics. These included growth rates, latency values, and carrying capacities of the various biomarkers, most of which demonstrated significant differences across clinical diagnostic groups. The model fits to training data across the entire cohort had a root mean squared error (RMSE) of 0.09 (SD 0.081) on a variable scale between zero and one, and were robust, with over 90% of subjects showing an RMSE of < 0.2. Similarly, in a subset of subjects with data on all four biomarkers in at least one test set, performance was high on the test sets, with a mean RMSE of 0.15 (SD 0.117), with 80% of subjects demonstrating an RMSE < 0.2 in the estimation of future biomarker points. Cluster analysis of parameters revealed two distinct endophenotypic groups, with distinct biomarker profiles and disease trajectories.

CONCLUSION

Results support the feasibility of personalizing mechanistic models based on individual biomarker trajectories and suggest that this approach may be useful for reclassifying subjects on the Alzheimer's clinical spectrum. This computational modeling approach is not limited to the Alzheimer Disease Biomarker Cascade hypothesis, and can be applied to any mechanistic hypothesis of disease progression in the Alzheimer's field that can be monitored with biomarkers. Thus, it offers a computational platform to compare and validate various disease hypotheses, personalize individual biomarker trajectories and predict individual response to theoretical prevention and therapeutic intervention strategies.

Clinical service evaluation of the feasibility and reproducibility of novel artificial intelligence based-echocardiographic quantification of global longitudinal strain and left ventricular ejection fraction in trastuzumab-treated patients

Introduction

Cardiotoxicity is a potential prognostically important complication of certain chemotherapeutic agents that may result in preclinical or overt clinical heart failure. In some cases, chemotherapy must be withheld when left ventricular (LV) systolic function becomes significantly impaired, to protect cardiac function at the expense of a change in the oncological treatment plan, leading to associated changes in oncological prognosis. Accordingly, patients receiving potentially cardiotoxic chemotherapy undergo routine surveillance before, during and following completion of therapy, usually with transthoracic echocardiography (TTE). Recent advancements in AI-based cardiac imaging reveal areas of promise but key challenges remain. There are ongoing questions as to whether the ability of AI to detect subtle changes in individual patients is at a level equivalent to manual analysis. This raises the question as to whether AI-based left ventricular strain analysis could provide a potential solution to left ventricular systolic function analysis in a manner equivocal to or superior to conventional assessment, in a real-world clinical service. AI based automated analyses may represent a potential solution for addressing the pressure of increasing echocardiographic demands within limited service-capacity healthcare systems, in addition to facilitating more accurate diagnoses.

Methods

This clinical service evaluation aims to establish whether AI-automated analysis compared to conventional methods (1) is a feasible method for assessing LV-GLS and LVEF, (2) yields moderate to good correlation between the two approaches, and (3) would lead to different clinical recommendations with serial surveillance in a real-world clinical population.

Results and Discussion

We observed a moderate correlation (r = 0.541) in GLS between AI automated assessment compared to conventional methods. The LVEF quantification between methods demonstrated a strong correlation (r = 0.895). AI-generated GLS and LVEF values compared reasonably well with conventional methods, demonstrating a similar temporal pattern throughout echocardiographic surveillance. The apical-three chamber view demonstrated the lowest correlation (r = 0.423) and revealed to be least successful for acquisition of GLS and LVEF. Compared to conventional methodology, AI-automated analysis has a significantly lower feasibility rate, demonstrating a success rate of 14% (GLS) and 51% (LVEF).

Erratum: Search for Cosmic-Ray Boosted Sub-GeV Dark Matter Using Recoil Protons at Super-Kamiokande [Phys. Rev. Lett. 130, 031802 (2023)]

This corrects the article DOI: 10.1103/PhysRevLett.130.031802.

Determining the Dosimetric Accuracy of Deep Learning-Based Fully Automated Registration-Segmentation Approach for Thoracic Cancer Organs-at-Risk Contouring

PURPOSE/OBJECTIVE(S)

For adaptive radiation therapy (ART), the contours on the planning CT (pCT) are frequently propagated to cone-beam CT (CBCT) via deformable image registration and manually edited, which is observer-dependent and time-consuming. To automate this process, we created a fully automated workflow by combining a deep learning (DL)-based pCT segmentation model with a CT-to-CBCT registration-segmentation DL model. The purpose of our research is to determine how using the proposed workflow's automatically generated contours affects thoracic organs-at-risk sparing (OAR).

MATERIALS/METHODS

Seven patients with locally advanced non-small cell lung cancer who underwent treatment with intensity modulated radiation therapy were included in this study. Each patient's pCT was segmented using a published DL model that has been used for generating thoracic OAR segmentation and radiotherapy planning in the clinic since July of 2020. Next, pCT was deformably registered using a published recurrent deep registration-segmentation method. Whereas the original method's segmentation sub-network was only trained to segment esophagus, the registration sub-network was used to propagate contours for heart, esophagus, and the proximal bronchial tree (PBT). Geometric segmentation accuracy using the Dice Similarity Coefficient (DSC) and the 95th percentile Hausdorff Distance (HD) and dose metrics including the mean esophageal dose (MED) and D90% of the heart (D90) were computed from the total accumulated dose for the first two weeks of treatment.

RESULTS

The esophagus had a high DSC and a low HD (0.93 and 2.85mm) and conversely, the heart had lower accuracy (DSC = 0.85, HD = 22.06mm). PBT showed relatively high performance as well, with DSC of 0.91 and HD of 2.28mm, owing to its proximity to the esophagus. The accumulated MED for manual contour was slightly lower than AI-contours (11.34 vs 11.83 Gy), suggesting reliability of the proposed workflow. The reverse is seen for the D90 of the heart (manual = 1.74 and AI-contour = 1.56 Gy), likely due to the heart not being included in the original DL framework.

CONCLUSION

This study reported preliminary results on the feasibility of using a fully automated and patient-specific workflow for CBCT auto-segmentation in ART, confirming its role as a geometrically and dosimetrically accurate solution for thoracic OARs. However, because it is currently limited to the esophagus, we believe that re-training the algorithm will increase confidence in other OARs such as the heart and lungs.

Clinical Feasibility of Deep Learning-Based CT during Treatment CBCT Tumor Registration-Segmentation in Thoracic Radiotherapy (RT)

PURPOSE/OBJECTIVE(S)

Accurate tumor segmentation on weekly cone-beam computed tomography (CBCT) images is critical for image-guided and adaptive radiation therapy (ART). In thoracic RT, low image contrast, imaging artifact, and geometry and image modality differences from the planning CT (pCT) typically limits accurate tumor segmentation and registration. Here, we explored the clinical feasibility of using 3D recurrent registration-segmentation deep learning (DL) that combines patient-specific anatomic and shape context from higher contrast pCT and planning contours (PACs) for tumor segmentation on during treatment CBCTs.

MATERIALS/METHODS

We included the pCT and CBCTs from six patients with locally advanced non-small cell lung cancer (LA-NSCLC) who had underwent RT. Cases were selected with a primary GTV contoured and labeled separately from the nodal GTV. Using rigidly aligned pCT and CBCT as inputs, DL auto-segmented the GTV on week 1 and 6 CBCTs, and these auto-segmented contours were manually inspected by a radiation oncologist that edited the GTV according to clinical standard quality. The Dice similarity coefficient (DSC), Hausdorff distance (HD95), mean surface distance (MSD), surface DSC (sDSC) and added path length (APL) were used to quantitively compare the DL and the edited GTV.

RESULTS

The primary GTV was in the right lung in five cases, and left lung in one case. Manual adjustments were typically made at the interface of GTV and lung parenchyma with partial inclusion of adjacent vessels. Hypodensities within the GTV were sometimes not segmented in all axial slices resulting in discontinuous components. The quantitative comparison between the edited and DL-generated GTV is shown in Table 1. For week 1, the average DSC and HD95 were 0.87 and 6.94 mm, respectively. The performance for week 6 was slightly lower than week 1, with a DSC of 0.85 and HD95 of 7.22 mm.

CONCLUSION

The agreement with the generated DL GTV and the edited GTV was high in week 1 and decreased somewhat later during the treatment course possibly due to a higher impact of geometric changes in tumor and adjacent structures. The proposed DL algorithm showed reasonable performance throughout the treatment, supporting its potential for use into clinical routine for LA-NSCLC.

Augmenting Prediction of Intracranial Aneurysms' Risk Status Using Velocity-Informatics: Initial Experience

Our primary goal here is to demonstrate that innovative analytics of aneurismal velocities, named velocity-informatics, enhances intracranial aneurysm (IA) rupture status prediction. 3D computer models were generated using imaging data from 112 subjects harboring anterior IAs (4-25 mm; 44 ruptured and 68 unruptured). Computational fluid dynamics simulations and geometrical analyses were performed. Then, computed 3D velocity vector fields within the IA dome were processed for velocity-informatics. Four machine learning methods (support vector machine, random forest, generalized linear model, and GLM with Lasso or elastic net regularization) were employed to assess the merits of the proposed velocity-informatics. All 4 ML methods consistently showed that, with velocity-informatics metrics, the area under the curve and prediction accuracy both improved by approximately 0.03. Overall, with velocity-informatics, the support vector machine's prediction was most promising: an AUC of 0.86 and total accuracy of 77%, with 60% and 88% of ruptured and unruptured IAs being correctly identified, respectively.

Correlation between nocturnal intermittent hypoxemia and mild cognitive impairment in the older adult and the role of BDNF Val66Met polymorphism: a hospital-based cross-sectional study

PURPOSE

To explore the prevalence of nocturnal intermittent hypoxemia (NIH) in a tertiary hospital geriatric department and the relationship between NIH and mild cognitive impairment (MCI) in older adults, and to examine the role of brain-derived neurotrophic factor (BDNF) Val66Met polymorphism.

METHODS

Older adults aged ≥ 60 were enrolled. NIH and cognitive assessments were conducted. BDNF concentrations and BDNF Val66Met polymorphism were detected for a preliminary exploration of the possible mechanism of the process.

RESULTS

Of 325 older adults enrolled, 157 (48%) had NIH and were further divided into mild, moderate, and severe NIH groups according to their oxygen desaturation of ≥ 4% per hour of sleep (ODI4). MCI detection rate in the four groups gradually increased, and the differences were statistically significant (chi-square = 4.457, P = 0.035). ODI4 was negatively correlated with MoCA score in all participants (r =  - 0.115, P = 0.039) and patients with NIH (r =  - 0.199, P = 0.012). After adjusting for sex, age, and cardiovascular risk factors, NIH and MCI remained independently associated (OR = 3.13, 95% CI 1.03-9.53, P = 0.045). BDNF levels were positively correlated with MoCA score (r = 0.169, P = 0.028) and negatively correlated with nocturnal average oxygen saturation in patients with NIH (r =  - 0.288, P = 0.008). Older adults with different BDNF Val66Met genotypes did not show significant differences in MCI rate and BDNF levels (P > 0.05).

CONCLUSION

The older adults with NIH have a higher MCI detection rate. BDNF levels may be a potential biomarker for cognitive dysfunction in patients with NIH.

The Effect of MDSC-Derived Exosomes Played in Esophageal Squamous Carcinoma Cells after Ionizing Radiation

PURPOSE/OBJECTIVE(S)

Radiotherapy is the main treatment for esophageal cancer. Previous studies have shown that radiotherapy not only kills tumor cells directly, but also reshapes the immune microenvironment of the tumor. It has been reported an increase in the recruitment of myeloid-derived suppressor cells (MDSC) can occur in tumor tissue after ionizing radiation. Exosomes are mediators of intercellular information exchange and are also involved in the regulation of the tumor microenvironment. In this study, we wanted to understand whether MDSC in esophageal cancer tissue are involved in the regulation of tumor cell response to ionizing radiation via exosomes.

MATERIALS/METHODS

KYSE-150 was used to construct a subcutaneous transplantation tumor model in nude mice. And then mice irradiated with 5 Gy×5fx and 0 Gy×5fx respectively. After irradiation, the spleens of the mice were used to isolate MDSC, and collect the cell supernatants to extract the exosomes. Based on the exosomes, we divided the experiment into three groups (control, exosomes, exosomes+radiation). Exosomes were injected into a nude mouse model of esophageal cancer via the tail vein or co-cultured with KYSE-150 cells. Mice were irradiated with a 5 Gy×5fx after completion of injection, and KYSE-150 cells were irradiated with a single dose 4 Gy. After radiation, KYSE-150 cells were used to detect cell cloning, apoptosis and cell cycle by flow cytometry, cell proliferation by CCK 8. XRCC4,XRCC5,XRCC6,γH2AX,ATM expression in cells and tumor tissue were measured by Western blot and RT-PCR.

RESULTS

The tumor volume was significantly reduced after 5 Gy x 5fx radiation. When exosomes co-cultured with KYSE-150 cells, decrease in apoptosis and increase in cell cloning and cell proliferation were found in the exosomes+radiation group and exosomes group after radiation when compared with the control group, with this change being more pronounced in the exosome+radiation group. The results of the cell cycle assay showed that after ionizing radiation, the proportion of cells in the G0/G1 phase was significantly lower, and the proportion of cells in the S and G2/M phases were significantly higher in the exosomes+radiation group and exosomes group when compared to the Control group. The protein and mRNA expression of XRCC4,XRCC5,XRCC6,γH2AX,ATM in cells were increased in exosomes+radiation group and exosomes group after radiation when compared with the control group, with this change being more obvious in the exosome+radiation group. After irradiation, tumor volumes were measured in nude mice and the results showed that exosomes+radiation group tumors were the largest in volume, while the control group regressed most significantly after irradiation.

CONCLUSION

MDSC-derived exosomes have a tumor growth-promoting effect in esophageal squamous carcinoma, which is enhanced by ionizing radiation, and this may be related to the accelerated repair of damage in tumor tissue after radiation.

A Combined Phase 0/2 "Trigger" Trial Evaluating Pamiparib or Olaparib with Concurrent Radiotherapy in Patients with Newly-Diagnosed or Recurrent Glioblastoma

PURPOSE/OBJECTIVE(S)

This study evaluates the pharmacokinetic (PK) and pharmacodynamic (PD) profiles and clinical efficacy of PARP1/2 selective inhibitors, pamiparib and olaparib, in newly-diagnosed or recurrent glioblastoma (GBM) patients in combination with radiotherapy (RT).

MATERIALS/METHODS

In this combined phase 0/2 trial presumed newly-diagnosed (Arm A) or recurrent (Arm B) GBM patients received 4 days of pamiparib (60 mg BID) prior to resection either 2-4 or 8-12 hours following the final dose. Arm C enrolled patients with recurrent GBM to 4 days of olaparib (200 mg BID) prior to resection. Enhancing and nonenhancing tumor tissue, cerebrospinal fluid (CSF) and plasma were collected. Total and unbound drug concentrations were measured using validated LC-MS/MS methods. A PK 'trigger', defined as unbound drug and gt; 5-fold biochemical IC 50 in nonenhancing tumor, determined eligibility for the therapeutic expansion phase 2. PARP inhibition was assessed via ex vivo radiation and quantification of PAR levels compared to non-radiated control. Newly-diagnosed MGMT unmethylated GBMs and recurrent GBMs exceeding the PK threshold were eligible for an expansion phase of pamiparib (Arms A and B) or olaparib (Arm C) with concurrent RT followed by maintenance pamiparib or olaparib. RT was 60 Gy in 30 fractions in newly-diagnosed patients and 40 Gy in 15 fractions in recurrent patients, delivered using volumetric-modulated arc therapy (VMAT).

RESULTS

A total of 38 patients (Arm A, n = 16; Arm B, n = 16; Arm C, n = 6) were enrolled in the initial phase 0 study. The mean unbound concentrations of pamiparib in nonenhancing tumor region for Arm A and Arm B were 167.3 nM and 109.4 nM respectively, and in Arm C the mean unbound concentration of olaparib was 5.2 nM. All patients in the pamiparib arms (n = 32/32) but only 1 of 6 patients in the olaparib Arm C exceeded the PK threshold. Radiation-induced PAR expression was 2.44-fold in untreated control vs 1.16 in Arm A (p<0.05), 0.85 in Arm B (p<0.01) and 1.11 in Arm C patients, respectively. In Arm A, 11 patients had unmethylated tumors, and of those, 7 patients enrolled in phase 2. In Arm B, 9 of the 16 clinically eligible patients with positive PK results were enrolled in phase 2. At a median follow-up of 8.4 months [range: 1.3-15.7 months], the median progression-free survival (PFS) was 5.4, 6.0, and 3.8 months for Arms A (n = 7), B (n = 9), and C (n = 1), respectively. Grade 3+ toxicities related to pamiparib occurred in 4 patients, with 2 adverse events resulting in treatment discontinuation. No grade 3+ toxicities were documented in the olaparib arm.

CONCLUSION

Pamiparib achieved pharmacologically-relevant concentrations in nonenhancing GBM tissue and suppressed induction of PAR levels ex vivo post-radiation. The majority of patients with MGMT-unmethylated GBM advanced to the phase 2 portion of the trial, and pamiparib was generally well-tolerated in these patients.

AI Serial Image Prediction of Progression-Free Survival (PFS) for Locally Advanced Non-Small Cell Lung Cancer (LA-NSCLC) Patients Treated with Chemoradiation (CRT) and Durvalumab Consolidation

PURPOSE/OBJECTIVE(S)

Patient outcomes with definitive CRT for LA-NSCLC remain poor, with no imaging biomarkers to predict benefit. Hence, we developed a serial image AI model using paired planning CT (pCT) and first week cone-beam CT (CBCT) to predict PFS and measured AI model fairness defined as the bias in the classification with respect to gender as a protected attribute.

MATERIALS/METHODS

Sixty-four consecutive patients with LA-NSCLC treated with concurrent CRT to 60 Gy in 30 fractions and durvalumab consolidation were analyzed. Three prediction models were created. A previously developed AI image foundation model [1] was pre-trained with unlabeled 6,402 3D CT scans sourced from institutional and the Cancer Imaging Archive and modified to predict PFS as a binarized outcome (high PFS > 6 months and low PFS < 6 months) using pCT scans. Serial image AI model was created by adding the first week CBCT scan. The third model measured tumor growth rate (TGR) as relative change in tumor and nodal volume from pCT to CBCT derived using a different published AI model [2]. Association with PFS using univariable and multivariable Cox regression after adjusting for age, gender, planning tumor volume, and smoking status were measured using TGR and the two AI model predictions using a cutoff of > 50% probability for low PFS. AI model fairness metrics area under receiver operating curve (AUROC), precision, sensitivity, and specificity were computed.

RESULTS

TGR was not associated with PFS on univariate (Hazard ratio [HR] of 1.515, 95% confidence interval [CI] of 0.32 to 7.26, p = 0.60) or multivariate analysis (HR: 1.58, 95% CI: 0.32 to 7.80, p = 0.58) and resulted in a Harrell's C-index of 54.7%. The serial image AI model prediction was associated with PFS in both univariable (HR: 2.12, 95% CI: 1.02 to 4.40, p = 0.045) and multivariable analysis (HR 2.39, 95% CI of 1.09 to 5.25, p = 0.029), and a C-index of 62.5%. The pCT AI model was associated with PFS in univariate (HR 2.06, 95% CI of 1.06 to 4.01, p = 0.034) but not in multivariable analysis (HR 1.89, 95% CI of 0.93 to 3.87, p = 0.08), and a C-index of 59.9%. The serial image AI model reduced the parity in classification compared to pCT AI model indicating higher fairness (Table I).

CONCLUSION

The multi-image AI model predicted PFS with slightly higher accuracy and resulted in higher fairness than the pCT AI model. These results underscore the potential for incorporating multi-imaging biomarkers to predict treatment response.

[Comparison of long-term clinical outcomes between transvaginal mesh and pelvic floor reconstruction with native tissue repair in the treatment of advanced pelvic organ prolapse]

Objective: To study the long-term clinical effect of transvaginal mesh (TVM) and pelvic floor reconstruction with native tissue repair (NTR) in the treatment of advanced pelvic organ prolapse (POP). Methods: Totally 207 patients with advanced POP who were treated in Hunan Provincial Maternal and Child Health Care Hospital from Jan. 2016 to Sep. 2019 were enrolled. The patient's pelvic organ prolapse quantification were all at degree Ⅲ or above, and they all complained for different degree of symptoms. They were divided into two groups according to the different surgical methods, TVM group and NTR group. In TVM group, the mesh was implanted through the vagina for pelvic floor reconstruction, while in NTR group, the traditional transvaginal hysterectomy combined with uterosacral ligament suspension and anterior and posterior wall repair, as well as perineal body repair were performed. The median follow-up time was 60 months, during the follow up time, 164 cases (79.2%, 164/207) had completed follow-up, including 76 cases in TVM group and 88 cases in NTR group. The perioperative data and complication rates of the two groups were compared, and the subjective and objective outcomes of the two groups at 1, 3 and 5 years were observed, respectively. The objective efficacy was evaluated by three composite criteria, namely: (1) the distance from the farthest end of the prolapse of the anterior and posterior wall of the vagina to the hymen is ≤0 cm, and the descending distance of the top is ≤1/2 of the total length of the vagina; (2) determine the disappearance of relevant POP symptoms according to "Do you often see or feel vaginal mass prolapse？"; (3) no further operation or pessary treatment was performed due to prolapse. If the above three criteria were met at the same time, the operation is successful; otherwise, it was recurrence. The subjective efficacy was evaluated by the pelvic floor distress inventory-short form 20 (PFDI-20) and pelvic floor impact questionnaire-short form 7 (PFIQ-7). Results: The median follow-up time of the two groups was 60 months (range: 41-82 months). Five years after the operation, the subjective and objective cure rates of TVM group were 89.5% (68/76) and 94.7% (72/76), respectively. The subjective and objective cure rates in NTR group were 80.7% (71/88) and 85.2% (75/88), respectively. There were significant differences in the subjective and objective cure rates between the two groups (χ2=9.869, P=0.002; χ2=3.969, P=0.046). The recurrence rate of TVM group was 5.3% (4/76), and that of NTR group was 14.8% (13/88). There was a significant difference between the two groups (P=0.046). The postoperative PFDI-20 and PFIQ-7 scores of the two groups were significantly lower than those before surgery, and there were significant differences of the two groups before and after surgery (all P<0.05). Postoperative mesh exposure in TVM group was 1.3% (1/76). Conclusions: The long-term outcomes between the two groups show that the subjective and objective outcomes of pelvic floor reconstruction in TVM group are significantly higher than those in NTR group, and the recurrence rate is significantly lower than that in NTR group. TVM has certain advantages in the treatment of advanced POP.

[A clustered epidemic investigation of non-marital non-commercial heterosexual contact of HIV in Zhejiang Province]

Objective: To identify the transmission relationship between HIV infection cases the non-marital non-commercial heterosexual contact in Zhejiang Province. Methods: When HIV positive was informed during January 2020 to January 2022, the staff conducted an epidemiological investigation to collect cases information on sociodemographic characteristics, mobility information, past HIV testing history, high-risk sexual behaviors, sexual partners, and etcetera. At the same time, 6-8 ml of blood from the new diagnosis of people infected with HIV before antiviral treatment was collected to separate the bleeding plasma. pol gene was amplified by nucleic acid extraction and PCR, sequenced by Sequencer 5.0 software, and Cytoscape 3.6.0 software was used to draw HIV molecular transmission network. Results: From January 2020 to January 2022, 88 HIV infected individuals were found in Pujiang County, of which 74 were transmitted through heterosexual transmission, of which 31 were infected through non-marital non-commercial heterosexual contact. Preliminary case studies have found that three female cases have engaged in unprotected non-marital non-commercial heterosexual contact with one male case. Among the 4 infected individuals, 2 of their spouses tested positive for HIV antibodies. Molecular transmission network monitoring was carried out on 65 newly diagnosed cases of heterosexual transmission with acquired sequences, forming 9 transmission clusters. The largest cluster contained 10 cases. A total of 11 HIV-infected individuals were involved in this HIV cluster epidemic. They were 3 males and 8 females, all over 50 years old and were farmers or rural housewives. They were traced to 7 sexual partners (6 negatives of HIV, 1 undetected). Among the 18 respondents' sexual social network relationships, there were 6 couples, 8 permanent partners, and 3 temporary partners. Among 11 HIV infected individuals, there were 9 cases of non-marital non-commercial heterosexual transmission and 2 cases of intramarital transmission. The epidemiological association between 7 non-married non-commercial heterosexual partners and case 2 (56-year-old male farmer), 3 cases confirmed by epidemiological investigation and molecular transmission cluster results, 3 cases confirmed by molecular transmission cluster and epidemiological investigation results, and 1 case confirmed by epidemiological investigation results. Conclusions: The transmission mode of this cluster epidemic was to spread HIV through heterosexual sex with a male case as the core, then cause the transmission within marriage and between fixed sexual partners. The combination of epidemiological investigation and molecular transmission network traceability survey supports the conclusion of this study.

[Development and validation of prognostic nomogram for malignant pleural mesothelioma]

Objective: To development the prognostic nomogram for malignant pleural mesothelioma (MPM). Methods: Two hundred and ten patients pathologically confirmed as MPM were enrolled in this retrospective study from 2007 to 2020 in the People's Hospital of Chuxiong Yi Autonomous Prefecture, the First and Third Affiliated Hospital of Kunming Medical University, and divided into training (n=112) and test (n=98) sets according to the admission time. The observation factors included demography, symptoms, history, clinical score and stage, blood cell and biochemistry, tumor markers, pathology and treatment. The Cox proportional risk model was used to analyze the prognostic factors of 112 patients in the training set. According to the results of multivariate Cox regression analysis, the prognostic prediction nomogram was established. C-Index and calibration curve were used to evaluate the model's discrimination and consistency in raining and test sets, respectively. Patients were stratified according to the median risk score of nomogram in the training set. Log rank test was performed to compare the survival differences between the high and low risk groups in the two sets. Results: The median overall survival (OS) of 210 MPM patients was 384 days (IQR=472 days), and the 6-month, 1-year, 2-year, and 3-year survival rates were 75.7%, 52.6%, 19.7%, and 13.0%, respectively. Cox multivariate regression analysis showed that residence (HR=2.127, 95% CI: 1.154-3.920), serum albumin (HR=1.583, 95% CI: 1.017-2.464), clinical stage (stage Ⅳ: HR=3.073, 95% CI: 1.366-6.910) and the chemotherapy (HR=0.476, 95% CI: 0.292-0.777) were independent prognostic factors for MPM patients. The C-index of the nomogram established based on the results of Cox multivariate regression analysis in the training and test sets were 0.662 and 0.613, respectively. Calibration curves for both the training and test sets showed moderate consistency between the predicted and actual survival probabilities of MPM patients at 6 months, 1 year, and 2 years. The low-risk group had better outcomes than the high-risk group in both training (P=0.001) and test (P=0.003) sets. Conclusion: The survival prediction nomogram established based on routine clinical indicators of MPM patients provides a reliable tool for prognostic prediction and risk stratification.

Circ_0002111/miR-134-5p/FSTL1 signal axis regulates tumor progression and glycolytic metabolism in papillary thyroid carcinoma cells

BACKGROUND

Circular RNAs (circRNAs) have essential roles in the malignant progression of papillary thyroid carcinoma (PTC). Circ_0002111 was reported to facilitate cell proliferation and invasion abilities in PTC. This study was performed to explore the regulatory mechanism of circ_0002111 in PTC progression.

METHODS

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used for the level detection of circ_0002111, microRNA-134-5p (miR-134-5p) and Follistatin Like 1 (FSTL1). Cell proliferation was assessed by 3-(4, 5-dimethylthiazol-2-y1)-2, 5-diphenyl tetrazolium bromide (MTT) assay, EdU assay and colony formation assay. Cell migration ability was determined by transwell assay. Glycolysis was analyzed by extracellular acidification rate (ECAR), oxygen consumption rate (OCR), glucose consumption and lactate production. The protein quantification was performed through western blot. Xenograft tumor assay was used for the functional analysis of circ_0002111 in vivo. The target interaction was confirmed by dual-luciferase reporter assay and RNA pull-down assay.

RESULTS

The significant upregulation of circ_0002111 was detected in PTC samples and cells. PTC cell proliferation, migration and glycolytic metabolism were suppressed after circ_0002111 downregulation. PTC tumorigenesis in vivo was also inhibited by circ_0002111 knockdown. In addition, circ_0002111 could target miR-134-5p and si-circ_0002111#1-induced inhibition of PTC progression was relieved by miR-134-5p expression downregulation. Furthermore, FSTL1 was a target gene for miR-134-5p and miR-134-5p served as a tumor repressor in PTC by targeting FSTL1. Moreover, circ_0002111 could increase the FSTL1 level via sponging miR-134-5p.

CONCLUSION

All results indicated that circ_0002111 promoted the malignant behaviors of PTC cells partly by regulating the miR-134-5p/FSTL1 molecular network.

Can we explain machine learning-based prediction for rupture status assessments of intracranial aneurysms?

Although applying machine learning (ML) algorithms to rupture status assessment of intracranial aneurysms (IA) has yielded promising results, the opaqueness of some ML methods has limited their clinical translation. We presented the first explainability comparison of six commonly used ML algorithms: multivariate logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron neural network (MLPNN), and Bayesian additive regression trees (BART). A total of 112 IAs with known rupture status were selected for this study. The ML-based classification used two anatomical features, nine hemodynamic parameters, and thirteen morphologic variables. We utilized permutation feature importance, local interpretable model-agnostic explanations (LIME), and SHapley Additive exPlanations (SHAP) algorithms to explain and analyze 6 Ml algorithms. All models performed comparably: LR area under the curve (AUC) was 0.71; SVM AUC was 0.76; RF AUC was 0.73; XGBoost AUC was 0.78; MLPNN AUC was 0.73; BART AUC was 0.73. Our interpretability analysis demonstrated consistent results across all the methods; i.e., the utility of the top 12 features was broadly consistent. Furthermore, contributions of 9 important features (aneurysm area, aneurysm location, aneurysm type, wall shear stress maximum during systole, ostium area, the size ratio between aneurysm width, (parent) vessel diameter, one standard deviation among time-averaged low shear area, and one standard deviation of temporally averaged low shear area less than 0.4 Pa) were nearly the same. This research suggested that ML classifiers can provide explainable predictions consistent with general domain knowledge concerning IA rupture. With the improved understanding of ML algorithms, clinicians' trust in ML algorithms will be enhanced, accelerating their clinical translation.