The association between ferroptosis-related patterns and tumor microenvironment in colorectal cancer

Ferroptosis, a form of regulated cell death (RCD), exhibits distinct characteristics such as iron-dependence and lipid peroxidation accumulation (ROS), setting it apart from other types of cell death like apoptosis and necrosis. Its role in cancer biology is increasingly recognized, particularly its potential interaction with tumor microenvironment (TME) and CD8 T cells in cancer immunotherapy. However, the impact of ferroptosis on TME cell infiltration remains unclear. In this study, we conducted unsupervised clustering analysis on patient data from public databases, identifying three ferroptosis patterns with distinct TME cell infiltration characteristics: immune-inflamed, immune-excluded, and immune-desert phenotypes. We developed a ferroptosis score based on differentially expressed genes (DEGs) among these patterns, which correlated with various biological features including chemotherapy-resistance and immune cells infiltration. Despite patients with high ferroptosis scores exhibiting worse prognosis, they showed increased likelihood of benefiting from immunotherapy. Our findings highlight the importance of ferroptosis-related patterns in understanding TME cell infiltration and suggest novel strategies for drug combinations and immune-related therapies.

[Epidemiological and clinical features of newly reported advanced schistosomiasis cases in Sichuan Province from 2011 to 2022]

OBJECTIVE

To analyze the epidemiological characteristics of newly reported advanced schistosomiasis cases in Sichuan Province, so as to provide the evidence for analyzing the causes and formulating targeted control measures of newly reported advanced schistosomiasis cases.

METHODS

Individual case investigation forms for advanced schistosomiasis cases were collected from the Sichuan Provincial Epidemic Annual Report System from 2011 to 2022, and patients' demographics, previous medical history and liver parenchymal grading were retrieved. All advanced schistosomiasis cases' medical records were reviewed, and the subtypes of schistosomiasis-endemic villages where the cases' household registration were, floating population, survival and death and time of death were collected.

RESULTS

A total of 321 newly reported advanced schistosomiasis cases were found in Sichuan Province from 2011 to 2022, with a male to female ratio of 0.99 to 1. There were 274 cases at ages of over 50 years (85.4%), with the highest proportion seen at ages of 60 to 69 years (87 cases, 27.1%), and splenomegaly was the most common type (180 cases, 56.1%), with no dwarfism type detected. The highest number of cases was reported in 2011 (78 cases), followed by in 2022 (74 cases), and the highest number of cases were reported in Meishan City (199 cases, 62.0%), Dongpo District (131 cases, 40.8%), and hilly subtype areas (136 cases, 42.4%). As of the end of 2022, there were 111 deaths due to advanced schistosomiasis, with the highest number of deaths seen in 2018 (25 deaths), and the highest mortality was seen among patients with the ascites type (41.2%). There were 47 (37.3%), 40 (59.5%) and 4 (23.5%) cases with grade III liver parenchyma among patients with splenomegaly, ascites, and colonic proliferation types, respectively, and there was a significant difference in the grading of III liver parenchyma among three types of patients (H = 12.092, P < 0.05), with more severe liver parenchyma injuries seen among patients with the ascites type than among those with splenomegaly and colonic proliferation type (Z = 24.262 and 44.738, both Padjusted values < 0.05).

CONCLUSIONS

There have been newly reported advanced schistosomiasis cases in Sichuan Province during recent years, and patients with the ascites type should be given a high priority among advanced schistosomiasis cases in Sichuan Province. Intensified clue surveys are needed for early identification and treatment of advanced schistosomiasis cases, so as to increase the survival rate and improve the quality of life.

Neutral ceramidase regulates breast cancer progression by metabolic programming of TREM2-associated macrophages

The tumor microenvironment is reprogrammed by cancer cells and participates in all stages of tumor progression. Neutral ceramidase is a key regulator of ceramide, the central intermediate in sphingolipid metabolism. The contribution of neutral ceramidase to the reprogramming of the tumor microenvironment is not well understood. Here, we find that deletion of neutral ceramidase in multiple breast cancer models in female mice accelerates tumor growth. Our result show that Ly6C+CD39+ tumor-infiltrating CD8 T cells are enriched in the tumor microenvironment and display an exhausted phenotype. Deletion of myeloid neutral ceramidase in vivo and in vitro induces exhaustion in tumor-infiltrating Ly6C+CD39+CD8+ T cells. Mechanistically, myeloid neutral ceramidase is required for the generation of lipid droplets and for the induction of lipolysis, which generate fatty acids for fatty-acid oxidation and orchestrate macrophage metabolism. Metabolite ceramide leads to reprogramming of macrophages toward immune suppressive TREM2+ tumor associated macrophages, which promote CD8 T cells exhaustion.

Itaconate promotes hepatocellular carcinoma progression by epigenetic induction of CD8+ T-cell exhaustion

Itaconate is a well-known immunomodulatory metabolite; however, its role in hepatocellular carcinoma (HCC) remains unclear. Here, we find that macrophage-derived itaconate promotes HCC by epigenetic induction of Eomesodermin (EOMES)-mediated CD8+ T-cell exhaustion. Our results show that the knockout of immune-responsive gene 1 (IRG1), responsible for itaconate production, suppresses HCC progression. Irg1 knockout leads to a decreased proportion of PD-1+ and TIM-3+ CD8+ T cells. Deletion or adoptive transfer of CD8+ T cells shows that IRG1-promoted tumorigenesis depends on CD8+ T-cell exhaustion. Mechanistically, itaconate upregulates PD-1 and TIM-3 expression levels by promoting succinate-dependent H3K4me3 of the Eomes promoter. Finally, ibuprofen is found to inhibit HCC progression by targeting IRG1/itaconate-dependent tumor immunoevasion, and high IRG1 expression in macrophages predicts poor prognosis in HCC patients. Taken together, our results uncover an epigenetic link between itaconate and HCC and suggest that targeting IRG1 or itaconate might be a promising strategy for HCC treatment.

ENO1 promotes liver carcinogenesis through YAP1-dependent arachidonic acid metabolism

Enolase 1 (ENO1) is a glycolytic enzyme that plays essential roles in various pathological activities including cancer development. However, the mechanisms underlying ENO1-contributed tumorigenesis are not well explained. Here, we uncover that ENO1, as an RNA-binding protein, binds to the cytosine-uracil-guanine-rich elements of YAP1 messenger RNA to promote its translation. ENO1 and YAP1 positively regulate alternative arachidonic acid (AA) metabolism by inverse regulation of PLCB1 and HPGD (15-hydroxyprostaglandin dehydrogenase). The YAP1/PLCB1/HPGD axis-mediated activation of AA metabolism and subsequent accumulation of prostaglandin E2 (PGE2) are responsible for ENO1-mediated cancer progression, which can be retarded by aspirin. Finally, aberrant activation of ENO1/YAP1/PLCB1 and decreased HPGD expression in clinical hepatocellular carcinoma samples indicate a potential correlation between ENO1-regulated AA metabolism and cancer development. These findings underline a new function of ENO1 in regulating AA metabolism and tumorigenesis, suggesting a therapeutic potential for aspirin in patients with liver cancer with aberrant expression of ENO1 or YAP1.

Prevalence and influencing factors of depressive and anxiety symptoms among hospital-based healthcare workers during the surge period of the COVID-19 pandemic in the Chinese mainland: a multicenter cross-sectional study

BACKGROUND

From November 2022 to February 2023, the Chinese mainland experienced a surge in COVID-19 infection and hospitalization, and the hospital-based healthcare workers (HCWs) might suffer serious psychological crisis during this period. This study aims to assess the depressive and anxiety symptoms among HCWs during the surge of COVID-19 pandemic and to provide possible reference on protecting mental health of HCWs in future infectious disease outbreaks.

METHODS

A multicenter cross-sectional study was carried out among hospital-based HCWs in the Chinese mainland from 5 January to 9 February 2023. The PHQ-9 (nine-item Patient Health Questionnaire) and GAD-7 (seven-item Generalized Anxiety Disorder Questionnaire) were used to measure depressive and anxiety symptoms. Ordinal logistic regression analysis was performed to identify influencing factors.

RESULTS

A total of 6522 hospital-based HCWs in the Chinse mainland were included in this survey. The prevalence of depressive symptoms among the HCWs was 70.75%, and anxiety symptoms was 47.87%. The HCWs who perceived higher risk of COVID-19 infection and those who had higher work intensity were more likely to experience depressive and anxiety symptoms. Additionally, higher levels of mindfulness, resilience and perceived social support were negatively associated with depressive and anxiety symptoms.

CONCLUSION

This study revealed that a high proportion of HCWs in the Chinese mainland suffered from mental health disturbances during the surge of the COVID-19 pandemic. Resilience, mindfulness and perceived social support are important protective factors of HCWs' mental health. Tailored interventions, such as mindfulness practice, should be implemented to alleviate psychological symptoms of HCWs during the COVID-19 pandemic or other similar events in the future.

Multivariable model of postoperative delirium in cardiac surgery patients: proteomic and demographic contributions

Background

Delirium following cardiac surgery is common, morbid, and costly, but may be prevented with risk stratification and targeted intervention. Preoperative protein signatures may identify patients at increased risk for worse postoperative outcomes, including delirium. In this study, we aimed to identify plasma protein biomarkers and develop a predictive model for postoperative delirium in older patients undergoing cardiac surgery, while also uncovering possible pathophysiological mechanisms.

Methods

SOMAscan analysis of 1,305 proteins in the plasma from 57 older adults undergoing cardiac surgery requiring cardiopulmonary bypass was conducted to define delirium-specific protein signatures at baseline (PREOP) and postoperative day 2 (POD2). Selected proteins were validated in 115 patients using the ELLA multiplex immunoassay platform. Proteins were combined with clinical and demographic variables to build multivariable models that estimate the risk of postoperative delirium and bring light to the underlying pathophysiology.

Results

A total of 115 and 85 proteins from SOMAscan analyses were found altered in delirious patients at PREOP and POD2, respectively (p<0.05). Using four criteria including associations with surgery, delirium, and biological plausibility, 12 biomarker candidates (Tukey's fold change (|tFC|)>1.4, Benjamini-Hochberg (BH)-p<0.01) were selected for ELLA multiplex validation. Eight proteins were significantly altered at PREOP, and seven proteins at POD2 (p<0.05), in patients who developed postoperative delirium compared to non-delirious patients. Statistical analyses of model fit resulted in the selection of a combination of age, sex, and three proteins (angiopoietin-2 (ANGPT2); C-C motif chemokine 5 (CCL5); and metalloproteinase inhibitor 1 (TIMP1); AUC=0.829) as the best performing predictive model for delirium at PREOP. The delirium-associated proteins identified as biomarker candidates are involved with inflammation, glial dysfunction, vascularization, and hemostasis, highlighting the multifactorial pathophysiology of delirium.

Conclusion

Our study proposes a model of postoperative delirium that includes a combination of older age, female sex, and altered levels of three proteins. Our results support the identification of patients at higher risk of developing postoperative delirium after cardiac surgery and provide insights on the underlying pathophysiology. ClinicalTrials.gov ( NCT02546765 ).

Overall Survival Prediction in Stereotactic Radiosurgery Patients with Glioblastoma Via a Deep-Learning Approach

PURPOSE/OBJECTIVE(S)

Accurate and automated early survival prediction is critical for glioblastoma (GBM) patients as their poor prognosis requires timely treatment decision-making. We have developed a deep learning (DL)-based GBM overall survival (OS) prediction model based on a multi-institutional public dataset using only pre-operative basic structural multi-parametric magnetic resonance images (MRIs). The purpose of this study is to evaluate this DL-based OS prediction model with an institutional stereotactic radiosurgery (SRS) clinical trial dataset.

MATERIALS/METHODS

The task of this study is to classify GBM patients into 3 OS classes: long-survivors (>15 months), mid-survivors (between 10 and 15 months) and short-survivors (< 10 months). The proposed OS prediction model is an ensemble of a ResNet-based classifier and a K-NN classifier. The ResNet-based classifier is trained in a Siamese fashion to explore inter-class differences. During testing, training sample features are implemented with a K-NN classifier to ensemble with the ResNet-based classifier. A public dataset from Medical Image Computing and Computer Assisted Intervention (MICCAI) Brain Tumor Segmentation (BraTS) challenge 2020 (235 patients) were used for model establishing and initial validation. Then the validated model was evaluated on 19 GBM patients from an institutional SRS clinical trial. Each data entry consists of pre-operative basic structural multi-parametric MRIs and survival days, as well as patient ages for BraTS data and basic clinical characteristics for institutional data. GBM sub-regions, including contrast-enhancing tumor, peri-tumoral edema, and necrotic/non-enhancing tumor core, were segmented in the multi-parametric MRIs by an in-house DL model for both datasets. The OS prediction model was trained on 90% of the segmented BraTS data and validated on the rest 10%, then further evaluated on the institutional data. The model performance was assessed by prediction accuracy (ACC) and the area under the curve (AUC).

RESULTS

For this 3-class OS classification task, our DL-based prediction model achieved an ACC of 65.22% and an AUC of 0.81 on the BraTS dataset compared with the top-ranked result from the BraTS challenge 2020 (Rank 1st: ACC 61.7%), and an ACC of 52.63% and an AUC of 0.69 on the institutional dataset. Further analysis of the institutional dataset found that the predicted OS class had a statistically significant correlation with treatment volume (p = 0.012) and age (p = 0.006), which matches the analysis that the patients' ground truth OS class is statistical significantly correlated with treatment volume (p = 0.045).

CONCLUSION

Our DL-based OS prediction model for GBM using basic structural multi-parametric pre-operative MRIs has demonstrated promising performance in both public and institutional dataset with minimal manual processing requirements. This OS prediction model can be potentially applied to assist timely clinical decision-making.

Cardiac Sparing with Volumetric Modulated Arc Therapy Enabled Total Body Irradiation (CS VMAT-TBI)

PURPOSE/OBJECTIVE(S)

Volumetric modulated arc therapy (VMAT) enabled total body irradiation (TBI) has replaced conventional TBI in our institution given the improved treatment accuracy, patient comfort, and dose modulation ability. The risk of cardiovascular disease is several folds higher among transplant patients who receive TBI, likely related to dose to the heart. We hypothesize that a cardiac-sparing (CS) VMAT-TBI technique is feasible and can meaningfully reduce dose to the heart while still adequately covering nearby lymphatic tissue.

MATERIALS/METHODS

VMAT-TBI is delivered via multi-isocentric external beams in a frame-based setup. Heart is contoured as per published guidelines. A lymph node contour, which includes tonsils, neck nodal stations, mediastinal, abdominal, retroperitoneal, and pelvic nodes is created. Coverage of the lymph node contour is prioritized over organ-sparing during inverse optimization; with a goal of V90% greater than 99.5% and mean dose less than 800 cGy for the lymph nodes and heart, respectively. An IRB-approved retrospective review was performed with mean heart dose collected for all patients treated with CS VMAT-TBI and compared to a representative cohort of five patients treated with VMAT-TBI without cardiac sparing.

RESULTS

Thirty-one patients were treated with CS VMAT-TBI between 2020-2022 with a median follow up time of 11.5 months. Mean heart dose was 796 ± 71 cGy in the CS VMAT-TBI compared to 1247 ± 29 cGy in the VMAT-TBI group without cardiac sparing (p < 0.001). Of those treated with CS VMAT-TBI, three patients relapsed; one relapse occurred in bone marrow only, one relapse occurred in bone marrow and cervical, thoracic, and intra-abdominal lymphoid tissues, and one patient was simulated but never received induction therapy due to overt progression. 100-day relapse-free survival and overall survival were 82.5% and 86.2%, respectively. Median survival time has not been met.

CONCLUSION

Cardiac sparing is feasible in VMAT-TBI and is associated with significant decrease in mean heart dose of ∼450 cGy. This is estimated to confer a 33.3% decreased absolute risk for lifetime major coronary events compared to patients treated with VMAT-TBI without cardiac sparing. Although limited by short follow-up time, there does not appear to be a significant risk for early relapse despite de-escalating cardiac tissue, likely due to prioritizing coverage of lymph nodes. Prospective clinical studies are needed to further validate cardiac and other organ at risk sparing VMAT-TBI techniques.

An Online AI-Powered Interactive Histological Image Annotation Platform for Analyzing Intestinal Regenerating Crypts in Post-Irradiated Mice

PURPOSE/OBJECTIVE(S)

The goal of this project is to build an online AI-powered interactive annotation platform to accurately and efficiently annotate intestinal regenerating crypts in histological images of mice after abdominal irradiation.

MATERIALS/METHODS

The proposed platform is developed by the seamless integration of a front-end web client and a back-end server. Such client/server design allows the users to access the platform without software installation on local computers. Our front-end client is developed with SvelteJS + WebGL technology stack, allowing access from any common web browsers and enabling user interaction, such as image importing/visualization, interactive crypt annotating, and annotation saving/deleting. The back-end server is responsible for executing the tasks requested from the web client, for instance, image pre-processing, AI-based crypts automatic identification, and database management. The image preprocessing is designed to extract a single cross section image using morphological operations because multiple hematoxylin and eosin (H&E) stained jejunum cross sections from post-irradiated mice are scanned within one slide. The auto-crypt identification is powered by a trained and validated AI engine U-Net, classifying image grid tiles into two groups with and without regenerating crypts. The database is implemented with the self-contained SQLite to support recording and indexing the annotated grid tiles with regenerating crypts. The workflow for crypt analysis on this interactive platform has 5 steps: 1) manually import a whole H&E slide image; 2) auto-preprocess the slide by extracting single cross-section images; 3) auto-identify regenerating crypts with an AI engine; 4) interactively annotate (add, delete, modify) auto-identified crypt markers; 5) save and/or output the annotation to the database or the local drive.

RESULTS

The performance of the developed interactive crypt analysis platform was evaluated in aspects of accuracy and efficiency. The AI-powered crypt auto-identification accuracy was assessed by computing the mean absolute error (MAE) on crypt number per cross section between manual and auto annotation using a testing dataset containing 80 cross sections. It achieved an MAE of 3.5±4.8 crypts per cross section, and 81.25% of the cross sections have no more than 5 crypts difference. The efficiency was assessed under two conditions with the server on the cloud and a local computer. It took about 2-3 minutes to finish the entire workflow on the cloud, while 1-2 minutes on the local by saving ∼1 minute on image uploading.

CONCLUSION

The developed web client/server platform enables online automatic identification and interactive annotation of mice crypts in minutes. It is a convenient tool that allows accurate and efficient crypt analysis and can be extended for other histologic image analyses.

Green Tea Derivative (-)-Epigallocatechin-3-Gallate (EGCG) for Prevention of Acute Radiation-Induced Intestinal Injury: A Prospective Phase II Clinical Study in Pelvic Cancer Patients

PURPOSE/OBJECTIVE(S)

Radiation-induced intestinal injury (RIII) remains the most common dose-limiting toxicity following radiotherapy (RT) for pelvic malignancies. However, few efficient and safe methods for the prevention of RIII are available in the clinical practice. Our previous study proved that the green tea polyphenol (-)-epigallocatechin-3-gallate (EGCG) efficiently attenuates radiation-induced intestinal injury (RIII) in animal level. Therefore, this prospective phase II clinical study (ChiCTR2100053703) evaluated the efficacy of EGCG in the prevention of RIII.

MATERIALS/METHODS

Cervical or endometrial cancer patients who received adjuvant or radical RT in our department were enrolled, 400mg EGCG was taken daily, and RIII was evaluated weekly according to the RTOG criteria.

RESULTS

Between February 2022 to January 2023, 37 patients were enrolled (30 were cervical cancer and the other 7 were endometrial cancer), and were followed-up regularly. Among them, the majority patients (75.7%, 28 patients) developed grade 0 or 1 RIII, for which no medical intervention was required. Meanwhile, the occurrence of grade 2 RIII was 24.3% (9 patients), which was significantly lower than that in the historical controls (usually 60% to 80%). Besides that, no patients developed grade 3 or worse RIII.

CONCLUSION

In this phase II clinical trial, compared to historical controls, the prophylactic use of EGCG significantly reduced the incidence and severity of RIII in patients receiving pelvic RT. Therefore, EGCG has the potential to become a novel medical countermeasure for the prevention of RIII for pelvic cancer patients.

Multi-Institutional Phase II Trial Using Dose Escalated Five Fraction Stereotactic Partial Breast Irradiation (S-PBI) with GammaPodTM for Early-Stage Breast Cancer

PURPOSE/OBJECTIVE(S)

We report on our early experience of a multi-institutional phase II study of dose escalated five fraction stereotactic partial breast irradiation (S-PBI) for early-stage breast cancer after partial mastectomy using the GammaPodTM stereotactic radiation system.

MATERIALS/METHODS

Patient eligibility included DCIS or invasive epithelial histologies, AJCC clinical stage 0, I, or II with tumor size < 3 cm, and negative margins. Prior safety of Phase I dose escalation has been reported. Dose was 40 Gy delivered in 5 fractions to the CTV, and minimum dose 30 Gy in 5 fractions to the PTV. CTV margin was 1 cm and PTV margin 3 mm. For PTV cavities larger than 100cc, dose was reduced to 35Gy in 5 fractions to the CTV and 30 Gy in 5 fractions to the PTV. Primary endpoint of the study is to determine the 3-year patient global cosmesis score (4-point scale excellent, good, fair, or poor) and adverse cosmesis using a dose escalated approach with smaller PTV margins than conventional methods. Both patients and physicians completed baseline and subsequent cosmesis outcome questionnaires. Treatment related toxicity was graded using the NCI version 4.0 and RTOG/EORTC late radiation scale.

RESULTS

From 3/2019-10/2021, 74 patients were treated respectively. Of these, 38 were treated to 40Gy and 36 were treated to 35 Gy. Median follow up (f/u) was 24 months (mo), range (r) 3-39mo. Median age was 63 years (r 43-77). Histology included 28 DCIS, and 46 invasive carcinomas. 45/46 invasive tumors were ER+. 60/74 (81%) patients received endocrine therapy, and 7/74 patient received chemotherapy. There were 221 acute grade 1 toxicities, and 28 Grade 2 toxicities. No grade 3 or higher acute toxicities were reported (< 90 days). The most common Grade 2 toxicities were radiation dermatitis (10), breast pain (8), blister (4), skin infection (2), nipple discharge (2), and fatigue (2). In the late period, there were 54 Grade 1 late toxicities, 4 Grade 2 late toxicities, and no Grade 3 or higher late toxicities. Grade 2 toxicities included fibrosis (2), and pain (2). Two patients developed grade 1 asymptomatic nonpalpable fat necrosis both diagnosed at 12 months after radiation treatments. The most common grade 1 late toxicities were breast pain (14), hyperpigmentation (8), fibrosis (10), and fatigue (5). Physicians scored cosmesis excellent or good 70/73 (95.8%), 58/60 (96.7%), 36/36 (100%),17/17(100%) respectively at baseline, 12 months, 24 months, and 36months post SBRT, while patients scored the same periods 62/71 (83.7%), 53/59 (89.8%), 33/36 (91.6%), 17/18 (94.4%). There have been no reports of disease recurrences.

CONCLUSION

Results at 24-month median follow-up, of our dose escalated stereotactic partial breast 5 fraction regimen, has low acute and late toxicity, while maintaining high proportion of excellent/good cosmetic outcomes. Continued analysis of all cohorts is in progress.

CLINICAL TRIALS

gov identifier is NCT03581136.

Deep Learning-Based Pipeline for Automatic Identification of Intestinal Regenerating Crypts in Mouse Histological Images

PURPOSE/OBJECTIVE(S)

A classical approach for evaluating normal tissue radiation response is to count the number of intestinal regenerating crypts in mouse histological images acquired after abdominal radiation. However, manual counting is time-consuming and subject to inter-observer variations. The goal of this study is to build a deep learning-based pipeline for automatically identifying intestinal regenerating crypts to facilitate high-throughput studies.

MATERIALS/METHODS

Sixty-six healthy C57BL/6 female mice underwent 16 MeV whole abdominal electron irradiation. The small bowel was collected from each mouse 4 days post-irradiation, and 9 jejunal cross-sections from each were processed together in a single slide. The slides were stained with hematoxylin and eosin (H&E) and subsequently scanned (x20), providing one electronic histological image per mouse. Regenerating crypts, consisting of more than 10 basophilic crypt epithelial cells, were manually identified using point annotations in histological images. The pipeline was built to take the input of the image containing 9 cross sections and automatically identify the regenerating crypts on each cross section. It mainly consists of two components, cross section segmentation using intensity thresholding and morphological operations and crypt identification using a UNet. The dataset was randomly split into 46, 10, and 10 slide images for UNet training, validation, and testing. Each slide image was split into grid tiles with a voxel size of 200 × 200, and 40 × 40 square masks were placed with centers at manual point annotations on tiles with regenerating crypts. 5203/5198 tiles (w/wo crypt mask) were extracted to train UNet by minimizing dice loss. The mask probability map generated by the UNet was post-processed to identify the crypt position. Postprocessing hyperparameters were tuned using the validation dataset. The model accuracy was evaluated using the testing dataset by computing the mean absolute error (MAE) of the crypt number averaged across all cross sections.

RESULTS

The number of regenerating crypts on testing cross sections ranges from 1 to 63. The testing cross-section-wise MAE achieved by the platform is 3.5±4.8 crypts. 81.25% of testing cross sections have absolute number differences less than or equal to 5 crypts.

CONCLUSION

Our established deep learning-based pipeline can accurately count the number of regenerating crypts in mouse intestinal histological images. We have integrated it into an online platform that enables automatic crypt identification and allows users to interactively modify auto-identified crypt annotations. The acquired annotations from the platform will be used to finetune the deep learning model to achieve better identification performance.

Equivalent Dose Estimation in FLASH Irradiation with a Deep Learning Approach

PURPOSE/OBJECTIVE(S)

Ultra-high dose rate (FLASH) irradiation has been reported to provide decreased normal tissue toxicity without compromising tumor control compared with conventional (CONV) irradiation. However, a comprehensive understanding of the FLASH biological effect requires precise quantification of radiobiology. The study is to explore whether deep learning (DL) can tackle the task. As a proof of concept, we investigate a DL model for estimating FLASH dose to its equivalent CONV dose.

MATERIALS/METHODS

Healthy C57Bl/6 female mice underwent FLASH (200Gy/s; n = 43) or CONV (0.12Gy/s; n = 41) whole abdominal irradiation using ∼16 MeV electron beams with a dose escalation scheme of 5 groups (n = 8 or 9) at 1Gy increments: 12-16Gy FLASH, 11-15Gy CONV. 4 days post-irradiation, 9 jejunum cross-sections per mouse were H&E stained for histological analysis. Each cross-section image was processed to remove lumen background and oversampled into multiple large-scale and small-scale patches along jejunal circumference. In CONV dataset, we randomly selected the data of 32 mice (80%) for model training and the rest (20%) for model validation. A ResNet101-based DL model, pre-trained with an unsupervised contrastive learning scheme, was retrained with only CONV training set to estimate corresponding CONV dose. For comparison, a crypt counting (CC) approach was implemented by manually counting the number of regenerating crypts on each cross-section image. An exponential function of dose vs crypt number was fitted with the CONV training set and used for dose estimation on the testing set. Mean squared error (MSE) was used to assess the accuracy of DL and CC approaches in estimating dose levels in CONV irradiation. The validated DL model was applied to the FLASH set to project FLASH dose into corresponding CONV dose that results in equivalent biological response.

RESULTS

The CONV dose estimated by DL and CC approaches and DL-estimated FLASH equivalent dose were summarized in Table 1. The DL model achieved an MSE of 0.21 Gy2 on CONV testing set compared with 0.32 Gy2 of the CC approach. FLASH equivalent dose estimated by DL model for 12, 13, 14, 15 and 16Gy were 12.16±0.40, 12.53±0.32, 12.72±0.24, 12.85±0.20 and 13.04±0.27 Sv, respectively.

CONCLUSION

Our proposed DL model can accurately estimate the CONV dose based on histological images. The DL predictions of FLASH dataset demonstrate that FLASH may reduce normal tissue toxicity with a lower equivalent dose, especially at high irradiated dose levels. Our study indicates that deep learning can be potentially used to assess the equivalent dose of FLASH irradiation to normal tissue.

Assessment of Cardiac Radiation Dose in the Co-60 Prone Based Stereotactic Partial Breast Irradiation (CP-sPBI) Using the Distance from the Heart to the Planning Treatment Volume as a Surrogate Marker

PURPOSE/OBJECTIVE(S)

Irradiation of the breast has shown to provide sharp dose gradients using Co-60 prone based stereotactic partial breast irradiation (CP-sPBI), a contemporary device for stereotactic radiotherapy for breast cancer (BC) for accelerated partial breast irradiation (APBI). In addition, the precise setup of CP-sPBI permits a small planning treatment volume (PTV) margin of 3 mm creating a greater distance from PTV to organs at risk. However, to date the factors that influence dose gradients and subsequent cardiac doses of ionizing radiation using CP-sPBI have not been well-studied. Here we evaluate distance of the heart to the lumpectomy PTV cavity and how this effects cardiac dose.

MATERIALS/METHODS

A retrospective database of 113 consecutive patients treated by CP-sPBI for APBI from March 2019 to February 2023 who were treated with 30 Gy in 5 fractions were queried for analysis. The minimum distance from the heart to the PTV (hP) was measured in either the axial or sagittal view. A group of 28 patient cases were randomly selected to achieve an even distribution of 28 cases with hP < 2.75 cm and hP ≥ 2.75 cm to compare cardiac toxicities based on hP. Descriptive analyses were performed to evaluate various cardiac dosimetric parameters based on laterality of BC and hP, using the student's t test.

RESULTS

The mean (range) hP was 4.58 cm (0.80-12.23) for all cases. The subgroup analyses of 28 patient cases with cardiac parameters showed the heart mean (range) dose of 1.20 Gy (0.01-2.11). The mean and max heart dose to the left-sided BC were similar to those to the right-sided BC (mean dose: 1.20 vs. 1.19 Gy; P = 0.97 and max dose: 10.47 vs. 5.66 Gy; P = 0.06). An inverse correlation between hP and mean heart dose was shown with the correlation coefficient of -0.81. Using a cutoff of 2.75 cm hP, the differences between hP < 2.75 and hP ≥ 2.75 cm for all cardiac dosimetric evaluations were all statistically significant, including mean (1.67 vs. 0.79 Gy; p<0.01) and maximal heart dose (14.48 vs. 4.11 Gy; p<0.01) CONCLUSION: CP-sPBI treatment delivery system was able to achieve acceptable clinically relevant heart dosimetric parameters when delivering 5 fraction APBI with a mean heart dose of 1.20 Gy for all locations of PTV cavity volume in the breast. Due to CP-sPBIs excellent dose fall-off characteristics, APBI using CP-SPBI showed clinically acceptable cardiac dosimetric parameters, particularly for PTVs located > 2.75 cm from the heart.

Financial Toxicity and Patient Experience Outcomes on a Multi-Institutional Phase I Single Fraction Stereotactic Partial Breast Irradiation Protocol for Early-Stage Breast Cancer

PURPOSE/OBJECTIVE(S)

Given the demonstrated financial toxicity (FT) of radiation treatment on breast cancer patients shown in both conventional and our recent 5 fraction stereotactic APBI (S-PBI) study, we assessed the FT, as well as patient-reported utility, quality-of-life and patient experience measures, on patients treated in our phase I single fraction S-PBI trial.

MATERIALS/METHODS

A phase I single fraction dose escalation trial of S-PBI for early-stage breast cancer was conducted. Women with in-situ or stage I-II (AJCC 6) invasive breast cancer following breast conserving surgery were treated with S-PBI in 1 fraction to a total dose of 22.5, 26.5 or 30 Gy (Clinical trials.gov ID NCT02685332). At one month follow-up, patients were asked to complete our novel "Patient Perspective Cost and Convenience of Care Questionnaire". Patients also completed the EQ-5D-5L, including the visual analogue scale of overall health (VAS), at enrollment, 6, 12-, 24-, 36-, and 48-month follow-up.

RESULTS

Of 29 patients enrolled and treated, questionnaire data was available for all patients. Our trial encompassed a wide range of annual household incomes, education, and employment status. Overall, 44.8% (n = 13/29) of patients reported that radiation treatment presented a financial burden. Interestingly, no demographic information, such as patient race, marital status, education, household income, or employment during treatment predicted perceived FT. Patients reporting FT trended towards younger age (median 64 vs 70.5) and having a cancer related co-pay similar to our 5 fraction S-PBI FT trial; however, due to the small size of this study, this did not reach significance (p = 0.24 and 0.10, respectively). VAS and utility scores were calculated per the EQ-5D-5L and remained unchanged from baseline through 4-year follow-up. Likewise, there was no difference in the utility or VAS between patients who reported FT and those who did not. Interestingly, while patient reported cosmesis was similar for all patients at enrollment, patients who reported FT noted significantly worse cosmesis scores (fair/poor vs good/excellent) at 6 month and 2-year follow-ups (p = 0.01 and 0.04, respectively). Finally, patients were surveyed on treatment related disruption to their daily activities and enjoyment of life. The median values were 0 (scale 0-10, with 0 being no disruption) regardless of perceived FT. Patients were also uniformly satisfied with treatment time with a median score of 10 (scale 0-10, 10 being most satisfied).

CONCLUSION

Here, we show that despite using SPBI in a single fraction, nearly half of the patients treated still reported FT of treatment. Importantly, single fraction S-PBI has no negative impact on patient VAS or utility scores, and all patients were uniformly satisfied with treatment time without significant disruption to their life.

Predicting Neurocognitive Decline in Multiple Brain Metastases Patients Undergoing Distributed Stereotactic Radiosurgery

PURPOSE/OBJECTIVE(S)

Stereotactic radiosurgery (SRS) is the standard of care for treating a limited number (<3) of brain metastasis (BMs), which offers reduced neurotoxicity compared to whole brain radiotherapy (WBRT). Contemporary advancements in SRS made it possible to also commonly treat multiple (>4) BMs (mBMs). Emphasizing the value of preserving quality of life (QoL) after SRS, there is an urgent need for a systematic study of potential neurocognitive decline in patients receiving SRS treatment for mBMs. The purpose of this study is to use routine MRIs to predict neurocognitive decline for patients treated with distributed SRS, allowing for timely and effective treatment strategy design.

MATERIALS/METHODS

This study uses data from an institutional phase I/II clinical trial to determine the neurocognitive decline in patients with (>6) mBMs treated with distributed SRS. In the first 12 months post-SRS, participants are followed and evaluated with routine MRIs and the Hopkins Verbal Learning Test-Revised (HVLT-R) at 2 to 3-month intervals. Changes in HVLT-Delayed Recall scores between two visits are used to define neurocognitive decline. For each visit, an in-house deep learning model segments 66 cortical and 55 subcortical brain regions of interest (ROIs) from the T1 structural MRI and extracts 253 ROI features, including the surface area and thickness of cortical ROIs, and the volume of all ROIS. The difference in ROI features between two visits, together with other clinical factors (e.g., prescription, number of BMs, etc.), is considered as one sample. The study included 22 subjects with 91 visits, resulting in 171 samples with neurocognitive decline labels. The entire sample set is split into 10 folds on patient level for cross validation. In each fold, feature engineering is conducted to remove redundancy and to select the most-important features. The top 20% most frequently selected features are applied with Support Vector Machine to predict the neurocognitive decline label of each sample.

RESULTS

As a preliminary result, the proposed method achieves an accuracy of 76%, with an area under the curve (AUC) of 0.75, sensitivity of 0.65 and specificity of 0.83 for predicting neurocognitive decline in mBMs SRS patients using only routine T1 MRIs. The volume of lateral occipital complex, the thickness of inferior parietal lobe and postcentral gyrus, and the surface area of lateral orbitofrontal cortex and pars triangularis are identified as the 5 most important features for this task.

CONCLUSION

Our method shows promising findings for post-SRS neurocognitive decline prediction solely based on routine baseline and follow-up MRIs. In addition, it can identify critical brain ROIs associated with the post-SRS cognitive function. This method has the potential to assist treatment planning strategy to help preserve patients' QoL.

A Comprehensive Deep Learning Framework for Automatic Target Volumes Segmentation in Post-Operative Stereotactic Partial Breast Irradiation (S-PBI)

PURPOSE/OBJECTIVE(S)

In S-PBI, accurate delineation of post-surgical tumor bed volume (TBV) and clinical target volume (CTV) are crucial tasks to achieve effective radiotherapy outcomes. However, manual contouring is labor intensive, time consuming, and largely relies on the experience of clinicians. We aimed to propose a deep learning (DL) approach which mimics physicians' contouring practice to accurately segment target volumes in post-operative breast CT images.

MATERIALS/METHODS

Our approach incorporated domain knowledge into a 3D U-Net based DL model for breast target volumes (TBV and CTV) delineation. Our TBV segmentation approach was inspired by the marker-guidance procedure in manual delineation, where the visual clues provided by the markers assist physicians in defining TBV. For this purpose, a distance-transformation coupled with a Gaussian filter was adopted to convert markers' locations on the CT images to saliency maps. Subsequently, the CT images and the corresponding saliency maps formed a two-channel input for the segmentation model. For CTV segmentation, TBV was incorporated as an input in addition to the CT images, guiding the model to encode the location-related image features. The architecture allowed the network to emulate the oncologist's manual delineation where CTV is derived from TBV via a margin expansion, followed by correcting the extensions for anatomical barriers of tumor invasion (e.g., skin, chest wall). We retrospectively collected 175 prone CT images from 35 post-operative breast cancer patients who received 5-fraction partial breast irradiation (PBI) regimen on a Co-60 prone based S-PBI unit. The 35 patients were randomly split into 25, 5, and 5 for model training, validation, and testing respectively.

RESULTS

We evaluated the performance of the developed DL model on the testing dataset by comparing the predicted volumes with the manually delineated contours (ground truth) using Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), and average symmetric surface distance (ASD). For TBV segmentation, our model achieved mean (standard deviation) of 0.76 (±2.7), 6.76 (±1.83) mm, and 1.9 (±0.66) mm for DSC, HD95, and ASD respectively. For CTV segmentation, our model achieved 0.94 (±0.02), 2.46 (±0.5) mm, and 0.53 (±0.14) mm for DSC, HD95, and ASD respectively. The proposed auto-segmentation approach generated TBV and CTV masks in ∼11 seconds per CT volume, implying significantly improved efficiency compared to manual contouring.

CONCLUSION

We developed a comprehensive DL framework mimicking clinical contouring practice for auto-segmentation of target volumes in S-PBI. The results demonstrated high levels of agreement between the predicted contours and physicians' manual contours. The approach is promising for improving the efficiency and accuracy of the on-line treatment planning workflow, such as adaptive based S-PBI.

Prospective Clinical Adoption of Artificial Intelligence for Organ Contouring in Head and Neck Radiation Treatment Planning

PURPOSE/OBJECTIVE(S)

Patients that undergo head and neck (H&N) radiation therapy (RT) require laborious delineation of organs-at-risk (OARs) on computed tomography (CT) scans in a treatment planning system (TPS) to minimize radiation to normal tissue. This task can be completed rapidly and accurately with recently developed artificial intelligence-based semantic segmentation models. The current study aims to deploy and evaluate a strategy for improving clinical practice with this technology.

MATERIALS/METHODS

Deep learning models were trained and tested with CT scans and OAR contours from previous H&N RT cases at our clinic. Two medical physicists vetted the models and selected a 2.5D U-Net for further implementation. The model was embedded in a dedicated server at the hospital, programmed to read H&N CT scans staged for import into the TPS, generate auto-contours, and write them into a TPS-compatible format made available alongside the scan. In the pilot implementation, the auto-contouring service was utilized for more than 60 cases, prospectively. The auto-contours were quantitatively evaluated against the treatment-approved contours to determine how much modification was performed by the clinical team.

RESULTS

The 2.5D U-Net selected for clinical integration segments 21 OARs in less than 3 minutes per scan. Across all the prospective cases, the mean Dice score and mean 95th percentile Hausdorff distance (mm) between the auto-contour and treatment-approved contour for each of the 21 OARs were as follows, respectively: brainstem (0.93, 1.94), optic chiasm (0.70, 2.96), left cochlea (0.69, 2.37), right cochlea (0.68, 2.44), esophagus (0.88, 2.46), left globe (0.93, 1.50), right globe (0.93, 1.63), glottis (0.91, 2.13), larynx (0.93, 2.76), mandible (0.90, 4.86), left optic nerve (0.78, 1.64), right optic nerve (0.82, 1.65), oral cavity (0.86, 8.46), left parotid gland (0.91, 2.78), right parotid gland (0.91, 2.39), pharynx (0.85, 2.39), spinal cord (0.87, 2.27), left submandibular gland (0.85, 3.46), right submandibular gland (0.83, 3.69), left temporal lobe (0.94, 2.20), and right temporal lobe (0.95, 2.09). The auto-contours for the optic chiasm, optic nerves, cochleas, and submandibular glands differed substantially from the final contours, a finding corroborated by the clinical team; the rest were clinically acceptable with minor or no edits necessary.

CONCLUSION

The proposed strategy provides a sophisticated starting point for treatment planning that has garnered overall favorable feedback from the participating radiation oncologists and dosimetrists. Consequently, the technique is being extended to other treatment sites.

[Efficacy of non-surgical comprehensive treatment for locally advanced hypopharyngeal carcinoma with cervical esophagus invasion]

Objective: To analyze the treatment effects and side effects of non-surgical comprehensive treatment for locally advanced hypopharyngeal carcinoma invading cervical esophagus. Methods: A retrospective analysis was performed on sixty-six patients with locally advanced hypopharyngeal carcinoma invade the esophagus. These patients were treated in the Department of Otolaryngology, Head and Neck Surgery of Chinese People's Liberation Army General Hospital between January 2011 and May 2022, including sixty-five males and one female, aged 43-71 years. Treatment regimen consisted of induction chemotherapy and concurrent chemoradiothrapy and epidermal growth factor receptor (EGFR)-targeted therapy, three of these cases were treated with programmed cell death 1 (PD-1) immunotherapy. The Kaplan-Meier method was used for survival analysis. Side effects were evaluated with the established CTCAE (Common Terminology Criteria for Adverse Events) 5.0 criteria. The factors affecting prognosis were analyzed by Cox multivariate regression analysis. Results: Sixty-four (97.0%, 64/66) patients completed the radiotherapy and chemotherapy plan. The most common grade three side effects were radioactive oropharyngeal mucositis (89.1%, 57/64) and leukopenia (23.4%, 15/64). Five (7.8%, 5/64) patients showed grade three hoarseness; two patients (3.1%, 2/64) suffered from grade three swallowing dysfunction and required feeding tube and intravenous nutrition; the remaining patients(89.1%) retained good vocal and swallowing functions. The overall survival (OS) of all patients was 81.5% after one year, 54.0% after three years, and 39.9% after five years; the progression-free survival (PFS) was 78.3% after one year, 54.9% after three years, and 42.6% after five years; local control rate (LCR) was 80.9% after one year, 62.5% after three years, and 52.0% after five years. T4a patients showed better OS, PFS and LCR than T4b patients, with statistically significant differences (χ2=8.10, 8.27, and 6.64, respectively, all P<0.05). Cox multivariate regression analysis showed that lymph node metastasis was an independent factor affecting prognosis (χ2=10.21, P<0.05). Conclusion: Non-surgical comprehensive treatment can provide with another option of radical treatment for locally advanced hypopharyngeal carcinoma with cervical esophagus invasion, offering the patients higher rate of larynx and esophageal preservation with tolerable side effects.

Methionine restriction promotes cGAS activation and chromatin untethering through demethylation to enhance antitumor immunity

Cyclic GMP-AMP synthase (cGAS) is the major sensor for cytosolic DNA and activates type I interferon signaling and plays an essential role in antitumor immunity. However, it remains unclear whether the cGAS-mediated antitumor activity is affected by nutrient status. Here, our study reports that methionine deprivation enhances cGAS activity by blocking its methylation, which is catalyzed by methyltransferase SUV39H1. We further show that methylation enhances the chromatin sequestration of cGAS in a UHRF1-dependent manner. Blocking cGAS methylation enhances cGAS-mediated antitumor immunity and suppresses colorectal tumorigenesis. Clinically, cGAS methylation in human cancers correlates with poor prognosis. Thus, our results indicate that nutrient stress promotes cGAS activation via reversible methylation, and suggest a potential therapeutic strategy for targeting cGAS methylation in cancer treatment.

Evidence for Band Renormalizations in Strong-Coupling Superconducting Alkali-Fulleride Films

There has been a long-standing debate about the mechanism of the unusual superconductivity in alkali-intercalated fullerides. In this Letter, using high-resolution angle-resolved photoemission spectroscopy, we systematically investigate the electronic structures of superconducting K_{3}C_{60} thin films. We observe a dispersive energy band crossing the Fermi level with the occupied bandwidth of about 130 meV. The measured band structure shows prominent quasiparticle kinks and a replica band involving the Jahn-Teller active phonon modes, which reflects strong electron-phonon coupling in the system. The electron-phonon coupling constant is estimated to be about 1.2, which dominates the quasiparticle mass renormalization. Moreover, we observe an isotropic nodeless superconducting gap beyond the mean-field estimation (2Δ/k_{B}T_{c}≈5). Both the large electron-phonon coupling constant and large reduced superconducting gap suggest a strong-coupling superconductivity in K_{3}C_{60}, while the electronic correlation effect is suggested by the observation of a waterfall-like band dispersion and the small bandwidth compared with the effective Coulomb interaction. Our results not only directly visualize the crucial band structure but also provide important insights into the mechanism of the unusual superconductivity of fulleride compounds.

Imaging diagnosis of intravenous leiomyomatosis: an institutional experience

AIM

To review and summarise the clinical and imaging characteristics of intravenous leiomyomatosis (IVL), a rare smooth muscle tumour originating from the uterus.

MATERIALS AND METHODS

Twenty-seven patients with a histopathological diagnosis of IVL who underwent surgery were reviewed retrospectively. All patients underwent pelvic ultrasonography, inferior vena cava (IVC) ultrasonography, and echocardiography before surgery. Computed tomography (CT) with contrast enhancement was performed in patients with extrapelvic IVL. Some patients underwent pelvic magnetic resonance imaging (MRI).

RESULTS

Mean age was 44.81 years. Clinical symptoms were non-specific. IVL was intrapelvic in seven patients and extrapelvic in 20. Preoperative pelvic ultrasonography missed the diagnosis in 85.7% of patients with intrapelvic IVL. Pelvic MRI was useful to evaluate the parauterine vessels. Incidence of cardiac involvement was 59.26%. Echocardiography showed a highly mobile sessile mass in the right atrium with moderate-to-low echogenicity that originates from the IVC. Ninety per cent of extrapelvic lesions showed unilateral growth. The most common growth pattern was via the right uterine vein-internal iliac vein-IVC pathway.

CONCLUSION

The clinical symptoms of IVL are non-specific. For patients with intrapelvic IVL, early diagnosis is difficult. Pelvic ultrasound should focus on the parauterine vessels, the iliac and ovarian veins should be explored carefully. MRI has obvious advantages in evaluating parauterine vessel involvement, which is helpful for early diagnosis. For patients with extrapelvic IVL, CT should be performed before surgery as part of a comprehensive evaluation. IVC ultrasonography and echocardiography are recommended when IVL is highly suspected.