Transfusions of packed red blood cells in surgery for liver cancer: predictor of impaired overall survival but not recurrence-free survival - impact of blood transfusions in liver surgery.

BACKGROUND: Liver surgery remains a cornerstone of potentially curative multimodal treatments for primary malignancies of the liver and hepatic metastases. Improving perioperative safety is a prerequisite in this context. Perioperative blood transfusions negatively influence postoperative recovery. This study aimed to identify risk factors for perioperative packed red blood cell (PRBC) transfusion and to elucidate its effect on postoperative outcomes. METHODS: This was an observational study of a prospective data collection. A monocentric, retrospective analysis of 1118 hepatectomies at the University Hospital Carl Gustav Carus between 2013 and 2020 was conducted to compare postoperative short- and long-term outcomes in patients undergoing curative intended liver resection of hepatic primary or secondary malignancies. The outcomes were compared between 356 patients (31.8%) who received PRBC transfusions during surgery or within 7 days after surgery and 762 patients (68.2%) who did not receive PRBC transfusions. RESULTS: Preoperative anemia could be observed in 45.0% of the whole cohort: 65.7% in the PRBC transfusion group and 35.3% in the nontransfused group. Postoperative complications were significantly more common in the PRBC transfusion group in association with prolonged lengths of hospital stay and increased 30-day mortality than in the nontransfused group. After adjustment for possible confounders, preexisting kidney failure, preoperative hemoglobin and albumin levels outside of the reference range, intraoperative plasma transfusions, and overall surgery time were recognized as negative predictors for perioperative PRBC transfusions. PRBC transfusion increased the risk of death by approximately 38.8% (hazard ratio, 1.388; 95% CI, 1.027-1.876; P = .033), whereas no influence on recurrence-free survival (RFS) was observed. CONCLUSION: PRBC transfusions were associated with postoperative morbidity and mortality after curative-intended surgery for liver cancers and represented an independent poor prognostic indicator for overall survival but not for RFS.

Dose and dose rate dependence of the tissue sparing effect at ultra-high dose rate studied for proton and electron beams using the zebrafish embryo model.

PURPOSE: A better characterization of the dependence of the tissue sparing effect at ultra-high dose rate (UHDR) on physical beam parameters (dose, dose rate, radiation quality) would be helpful towards a mechanistic understanding of the FLASH effect and for its broader clinical translation. To address this, a comprehensive study on the normal tissue sparing at UHDR using the zebrafish embryo (ZFE) model was conducted. METHODS: One-day-old ZFE were irradiated over a wide dose range (15-95 Gy) in three different beams (proton entrance channel, proton spread out Bragg peak and 30 MeV electrons) at UHDR and reference dose rate. After irradiation the ZFE were incubated for 4 days and then analyzed for four different biological endpoints (pericardial edema, curved spine, embryo length and eye diameter). RESULTS: Dose-effect curves were obtained and a sparing effect at UHDR was observed for all three beams. It was demonstrated that proton relative biological effectiveness and UHDR sparing are both relevant to predict the resulting dose response. Dose dependent FLASH modifying factors (FMF) for ZFE were found to be compatible with rodent data from the literature. It was found that the UHDR sparing effect saturates at doses above âˆ¼ 50 Gy with an FMF of âˆ¼ 0.7-0.8. A strong dose rate dependence of the tissue sparing effect in ZFE was observed. The magnitude of the maximum sparing effect was comparable for all studied biological endpoints. CONCLUSION: The ZFE model was shown to be a suitable pre-clinical high-throughput model for radiobiological studies on FLASH radiotherapy, providing results comparable to rodent models. This underlines the relevance of ZFE studies for FLASH radiotherapy research.

The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights.

Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.

Epigenetic Targeting to Overcome Radioresistance in Head and Neck Cancer.

(1) Background: The sensitivity of head and neck squamous cell carcinoma (HNSCC) to ionizing radiation, among others, is determined by the number of cells with high clonogenic potential and stem-like features. These cellular characteristics are dynamically regulated in response to treatment and may lead to an enrichment of radioresistant cells with a cancer stem cell (CSC) phenotype. Epigenetic mechanisms, particularly DNA and histone methylation, are key regulators of gene-specific transcription and cellular plasticity. Therefore, we hypothesized that specific epigenetic targeting may prevent irradiation-induced plasticity and may sensitize HNSCC cells to radiotherapy. (2) Methods: We compared the DNA methylome and intracellular concentrations of tricarboxylic acid cycle metabolites in radioresistant FaDu and Cal33 cell lines with their parental controls, as well as aldehyde dehydrogenase (ALDH)-positive CSCs with negative controls. Moreover, we conducted a screen of a chemical library targeting enzymes involved in epigenetic regulation in combination with irradiation and analyzed the clonogenic potential, sphere formation, and DNA repair capacity to identify compounds with both radiosensitizing and CSC-targeting potential. (3) Results: We identified the histone demethylase inhibitor GSK-J1, which targets UTX (KDM6A) and JMJD3 (KDM6B), leading to increased H3K27 trimethylation, heterochromatin formation, and gene silencing. The clonogenic survival assay after siRNA-mediated knock-down of both genes radiosensitized Cal33 and SAS cell lines. Moreover, high KDM6A expression in tissue sections of patients with HNSCC was associated with improved locoregional control after primary (n = 137) and post-operative (n = 187) radio/chemotherapy. Conversely, high KDM6B expression was a prognostic factor for reduced overall survival. (4) Conclusions: Within this study, we investigated cellular and molecular mechanisms underlying irradiation-induced cellular plasticity, a key inducer of radioresistance, with a focus on epigenetic alterations. We identified UTX (KDM6A) as a putative prognostic and therapeutic target for HNSCC patients treated with radiotherapy.

Radiomics for residual tumour detection and prognosis in newly diagnosed glioblastoma based on postoperative [11C] methionine PET and T1c-w MRI.

Personalized treatment strategies based on non-invasive biomarkers have potential to improve patient management in patients with newly diagnosed glioblastoma (GBM). The residual tumour burden after surgery in GBM patients is a prognostic imaging biomarker. However, in clinical patient management, its assessment is a manual and time-consuming process that is at risk of inter-rater variability. Furthermore, the prediction of patient outcome prior to radiotherapy may identify patient subgroups that could benefit from escalated radiotherapy doses. Therefore, in this study, we investigate the capabilities of traditional radiomics and 3D convolutional neural networks for automatic detection of the residual tumour status and to prognosticate time-to-recurrence (TTR) and overall survival (OS) in GBM using postoperative [11C] methionine positron emission tomography (MET-PET) and gadolinium-enhanced T1-w magnetic resonance imaging (MRI). On the independent test data, the 3D-DenseNet model based on MET-PET achieved the best performance for residual tumour detection, while the logistic regression model with conventional radiomics features performed best for T1c-w MRI (AUC: MET-PET 0.95, T1c-w MRI 0.78). For the prognosis of TTR and OS, the 3D-DenseNet model based on MET-PET integrated with age and MGMT status achieved the best performance (Concordance-Index: TTR 0.68, OS 0.65). In conclusion, we showed that both deep-learning and conventional radiomics have potential value for supporting image-based assessment and prognosis in GBM. After prospective validation, these models may be considered for treatment personalization.

ALDH1A1 drives prostate cancer metastases and radioresistance by interplay with AR- and RAR-dependent transcription.

Rationale: Current therapies for metastatic osseous disease frequently fail to provide a durable treatment response. To date, there are only limited therapeutic options for metastatic prostate cancer, the mechanisms that drive the survival of metastasis-initiating cells are poorly characterized, and reliable prognostic markers are missing. A high aldehyde dehydrogenase (ALDH) activity has been long considered a marker of cancer stem cells (CSC). Our study characterized a differential role of ALDH1A1 and ALDH1A3 genes as regulators of prostate cancer progression and metastatic growth. Methods: By genetic silencing of ALDH1A1 and ALDH1A3 in vitro, in xenografted zebrafish and murine models, and by comparative immunohistochemical analyses of benign, primary tumor, and metastatic specimens from patients with prostate cancer, we demonstrated that ALDH1A1 and ALDH1A3 maintain the CSC phenotype and radioresistance and regulate bone metastasis-initiating cells. We have validated ALDH1A1 and ALDH1A3 as potential biomarkers of clinical outcomes in the independent cohorts of patients with PCa. Furthermore, by RNAseq, chromatin immunoprecipitation (ChIP), and biostatistics analyses, we suggested the molecular mechanisms explaining the role of ALDH1A1 in PCa progression. Results: We found that aldehyde dehydrogenase protein ALDH1A1 positively regulates tumor cell survival in circulation, extravasation, and metastatic dissemination, whereas ALDH1A3 plays the opposite role. ALDH1A1 and ALDH1A3 are differentially expressed in metastatic tumors of patients with prostate cancer, and their expression levels oppositely correlate with clinical outcomes. Prostate cancer progression is associated with the increasing interplay of ALDH1A1 with androgen receptor (AR) and retinoid receptor (RAR) transcriptional programs. Polo-like kinase 3 (PLK3) was identified as a transcriptional target oppositely regulated by ALDH1A1 and ALDH1A3 genes in RAR and AR-dependent manner. PLK3 contributes to the control of prostate cancer cell proliferation, migration, DNA repair, and radioresistance. ALDH1A1 gain in prostate cancer bone metastases is associated with high PLK3 expression. Conclusion: This report provides the first evidence that ALDH1A1 and PLK3 could serve as biomarkers to predict metastatic dissemination and radiotherapy resistance in patients with prostate cancer and could be potential therapeutic targets to eliminate metastasis-initiating and radioresistant tumor cell populations.

Comparative analysis of radiomics and deep-learning algorithms for survival prediction in hepatocellular carcinoma.

To examine the comparative robustness of computed tomography (CT)-based conventional radiomics and deep-learning convolutional neural networks (CNN) to predict overall survival (OS) in HCC patients. Retrospectively, 114 HCC patients with pretherapeutic CT of the liver were randomized into a development (n = 85) and a validation (n = 29) cohort, including patients of all tumor stages and several applied therapies. In addition to clinical parameters, image annotations of the liver parenchyma and of tumor findings on CT were available. Cox-regression based on radiomics features and CNN models were established and combined with clinical parameters to predict OS. Model performance was assessed using the concordance index (C-index). Log-rank tests were used to test model-based patient stratification into high/low-risk groups. The clinical Cox-regression model achieved the best validation performance for OS (C-index [95% confidence interval (CI)] 0.74 [0.57-0.86]) with a significant difference between the risk groups (p = 0.03). In image analysis, the CNN models (lowest C-index [CI] 0.63 [0.39-0.83]; highest C-index [CI] 0.71 [0.49-0.88]) were superior to the corresponding radiomics models (lowest C-index [CI] 0.51 [0.30-0.73]; highest C-index [CI] 0.66 [0.48-0.79]). A significant risk stratification was not possible (p > 0.05). Under clinical conditions, CNN-algorithms demonstrate superior prognostic potential to predict OS in HCC patients compared to conventional radiomics approaches and could therefore provide important information in the clinical setting, especially when clinical data is limited.

Prediction of radiation pneumonitis using the effective α/ß of lungs and heart in NSCLC patients treated with proton beam therapy.

PURPOSE: Radiation pneumonitis (RP) remains a major complication in non-small cell lung cancer (NSCLC) patients undergoing radiochemotherapy (RCHT). Traditionally, the mean lung dose (MLD) and the volume of the total lung receiving at least 20 Gy (V20Gy) are used to predict RP in patients treated with normo-fractionated photon therapy. However, other models, including the actual dose-distribution in the lungs using the effective α/ß model or a combination of radiation doses to the lungs and heart, have been proposed for predicting RP. Moreover, the models established for photons may not hold for patients treated with passively-scattered proton therapy (PSPT). Therefore, we here tested and validated novel predictive parameters for RP in NSCLC patient treated with PSPT. METHODS: Data on the occurrence of RP, structure files and dose-volume histogram parameters for lungs and heart of 96 NSCLC patients, treated with PSPT and concurrent chemotherapy, was retrospectively retrieved from prospective clinical studies of two international centers. Data was randomly split into a training set (64 patients) and a validation set (32 patients). Statistical analyses were performed using binomial logistic regression. RESULTS: The biologically effective dose (BED) of the'lungs - GTV' significantly predicted RP ≥ grade 2 in the training-set using both a univariate model (p = 0.019, AUCtrain = 0.72) and a multivariate model in combination with the effective α/ß parameter of the heart (pBED = 0.006, [Formula: see text] = 0.043, AUCtrain = 0.74). However, these results did not hold in the validation-set (AUCval = 0.52 andAUCval = 0.50, respectively). Moreover, these models were found to neither outperform a model built with the MLD (p = 0.015, AUCtrain = 0.73, AUCval = 0.51), nor a multivariate model additionally including the V20Gy of the heart (pMLD = 0.039, pV20Gy,heart = 0.58, AUCtrain = 0.74, AUCval = 0.53). CONCLUSION: Using the effective α/ß parameter of the lungs and heart we achieved similar performance to commonly used models built for photon therapy, such as MLD, in predicting RP ≥ grade 2. Therefore, prediction models developed for photon RCHT still hold for patients treated with PSPT.

The value of subcutaneous xenografts for individualised radiotherapy in HNSCC: Robust gene signature correlates with radiotherapy outcome in patients and xenografts.

PURPOSE: To assess the robustness of prognostic biomarkers and molecular tumour subtypes developed for patients with head and neck squamous cell carcinoma (HNSCC) on cell-line derived HNSCC xenograft models, and to develop a novel biomarker signature by combining xenograft and patient datasets. MATERIALS AND METHODS: Mice bearing xenografts (n = 59) of ten HNSCC cell lines and a retrospective, multicentre patient cohort (n = 242) of the German Cancer Consortium-Radiation Oncology Group (DKTK-ROG) were included. All patients received postoperative radiochemotherapy (PORT-C). Gene expression analysis was conducted using GeneChip Human Transcriptome Arrays. Xenografts were stratified based on their molecular subtypes and previously established gene classifiers. The dose to control 50 % of tumours (TCD50) was compared between these groups. Using differential gene expression analyses combining xenograft and patient data, a gene signature was developed to define risk groups for the primary endpoint loco-regional control (LRC). RESULTS: Tumours of mesenchymal subtype were characterized by a higher TCD50 (xenografts, p < 0.001) and lower LRC (patients, p < 0.001) compared to the other subtypes. Similar to previously published patient data, hypoxia- and radioresistance-related gene signatures were associated with high TCD50 values. A 2-gene signature (FN1, SERPINE1) was developed that was prognostic for TCD50 (xenografts, p < 0.001) and for patient outcome in independent validation (LRC: p = 0.007). CONCLUSION: Genetic prognosticators of outcome for patients after PORT-C and subcutaneous xenografts after primary clinically relevant irradiation show similarity. The identified robust 2-gene signature may help to guide patient stratification, after prospective validation. Thus, xenografts remain a valuable resource for translational research towards the development of individualized radiotherapy.

The DRESDEN PLATFORM is a research hub for ultra-high dose rate radiobiology.

The recently observed FLASH effect describes the observation of normal tissue protection by ultra-high dose rates (UHDR), or dose delivery in a fraction of a second, at similar tumor-killing efficacy of conventional dose delivery and promises great benefits for radiotherapy patients. Dedicated studies are now necessary to define a robust set of dose application parameters for FLASH radiotherapy and to identify underlying mechanisms. These studies require particle accelerators with variable temporal dose application characteristics for numerous radiation qualities, equipped for preclinical radiobiological research. Here we present the DRESDEN PLATFORM, a research hub for ultra-high dose rate radiobiology. By uniting clinical and research accelerators with radiobiology infrastructure and know-how, the DRESDEN PLATFORM offers a unique environment for studying the FLASH effect. We introduce its experimental capabilities and demonstrate the platform's suitability for systematic investigation of FLASH by presenting results from a concerted in vivo radiobiology study with zebrafish embryos. The comparative pre-clinical study was conducted across one electron and two proton accelerator facilities, including an advanced laser-driven proton source applied for FLASH-relevant in vivo irradiations for the first time. The data show a protective effect of UHDR irradiation up to [Formula: see text] and suggests consistency of the protective effect even at escalated dose rates of [Formula: see text]. With the first clinical FLASH studies underway, research facilities like the DRESDEN PLATFORM, addressing the open questions surrounding FLASH, are essential to accelerate FLASH's translation into clinical practice.

Multitask Learning with Convolutional Neural Networks and Vision Transformers Can Improve Outcome Prediction for Head and Neck Cancer Patients.

Neural-network-based outcome predictions may enable further treatment personalization of patients with head and neck cancer. The development of neural networks can prove challenging when a limited number of cases is available. Therefore, we investigated whether multitask learning strategies, implemented through the simultaneous optimization of two distinct outcome objectives (multi-outcome) and combined with a tumor segmentation task, can lead to improved performance of convolutional neural networks (CNNs) and vision transformers (ViTs). Model training was conducted on two distinct multicenter datasets for the endpoints loco-regional control (LRC) and progression-free survival (PFS), respectively. The first dataset consisted of pre-treatment computed tomography (CT) imaging for 290 patients and the second dataset contained combined positron emission tomography (PET)/CT data of 224 patients. Discriminative performance was assessed by the concordance index (C-index). Risk stratification was evaluated using log-rank tests. Across both datasets, CNN and ViT model ensembles achieved similar results. Multitask approaches showed favorable performance in most investigations. Multi-outcome CNN models trained with segmentation loss were identified as the optimal strategy across cohorts. On the PET/CT dataset, an ensemble of multi-outcome CNNs trained with segmentation loss achieved the best discrimination (C-index: 0.29, 95% confidence interval (CI): 0.22-0.36) and successfully stratified patients into groups with low and high risk of disease progression (p=0.003). On the CT dataset, ensembles of multi-outcome CNNs and of single-outcome ViTs trained with segmentation loss performed best (C-index: 0.26 and 0.26, CI: 0.18-0.34 and 0.18-0.35, respectively), both with significant risk stratification for LRC in independent validation (p=0.002 and p=0.011). Further validation of the developed multitask-learning models is planned based on a prospective validation study, which has recently completed recruitment.

Prognostic biomarkers for the response to the radiosensitizer nimorazole combined with RCTx: a pre-clinical trial in HNSCC xenografts.

BACKGROUND: Tumor hypoxia is associated with resistance to radiotherapy and chemotherapy. In head and neck squamous cell carcinoma (HNSCC), nimorazole, an oxygen mimic, combined with radiotherapy (RT) enabled to improve loco-regional control (LRC) in some patients with hypoxic tumors but it is unknown whether this holds also for radiochemotherapy (RCTx). Here, we investigated the impact of nimorazole combined with RCTx in HNSCC xenografts and explored molecular biomarkers for its targeted use. METHODS: Irradiations were performed with 30 fractions in 6 weeks combined with weekly cisplatin. Nimorazole was applied before each fraction, beginning with the first or after ten fractions. Effect of RCTx with or without addition of nimorazole was quantified as permanent local control after irradiation. For histological evaluation and targeted gene expression analysis, tumors were excised untreated or after ten fractions. Using quantitative image analysis, micromilieu parameters were determined. RESULTS: Nimorazole combined with RCTx significantly improved permanent local control in two tumor models, and showed a potential improvement in two additional models. In these four models, pimonidazole hypoxic volume (pHV) was significantly reduced after ten fractions of RCTx alone. Our results suggest that nimorazole combined with RCTx might improve TCR compared to RCTx alone if hypoxia is decreased during the course of RCTx but further experiments are warranted to verify this association. Differential gene expression analysis revealed 12 genes as potential for RCTx response. When evaluated in patients with HNSCC who were treated with primary RCTx, these genes were predictive for LRC. CONCLUSIONS: Nimorazole combined with RCTx improved local tumor control in some but not in all HNSCC xenografts. We identified prognostic biomarkers with the potential for translation to patients with HNSCC.

Normo- or Hypo-Fractionated Photon or Proton Radiotherapy in the Management of Locally Advanced Unresectable Pancreatic Cancer: A Systematic Review.

LAPC is associated with a poor prognosis and requires a multimodal treatment approach. However, the role of radiation therapy in LAPC treatment remains controversial. This systematic review aimed to explore the role of proton and photon therapy, with varying radiation techniques and fractionation, in treatment outcomes and their respective toxicity profiles. METHODS: Clinical studies published from 2012 to 2022 were systematically reviewed using PubMed, MEDLINE (via PubMed) and Cochrane databases. Different radiotherapy-related data were extracted and analyzed. RESULTS: A total of 31 studies matched the inclusion criteria. Acute toxicity was less remarkable in stereotactic body radiotherapy (SBRT) compared to conventionally fractionated radiotherapy (CFRT), while in proton beam therapy (PBT) grade 3 or higher acute toxicity was observed more commonly with doses of 67.5 Gy (RBE) or higher. Late toxicity was not reported in most studies; therefore, comparison between groups was not possible. The range of median overall survival (OS) for the CFRT and SBRT groups was 9.3-22.9 months and 8.5-20 months, respectively. For the PBT group, the range of median OS was 18.4-22.3 months. CONCLUSION: CFRT and SBRT showed comparable survival outcomes with a more favorable acute toxicity profile for SBRT. PBT is a promising new treatment modality; however, additional clinical studies are needed to support its efficacy and safety.

Detectability of Anatomical Changes With Prompt-Gamma Imaging: First Systematic Evaluation of Clinical Application During Prostate-Cancer Proton Therapy.

PURPOSE: The development of online-adaptive proton therapy (PT) is essential to overcome limitations encountered by day-to-day variations of the patient's anatomy. Range verification could play an essential role in an online feedback loop for the detection of treatment deviations such as anatomical changes. Here, we present the results of the first systematic patient study regarding the detectability of anatomical changes by a prompt-gamma imaging (PGI) slit-camera system. METHODS AND MATERIALS: For 15 patients with prostate cancer, PGI measurements were performed during 105 fractions (201 fields) with in-room control computed tomography (CT)acquisitions. Field-wise doses on control CT scans were manually classified as whether showing relevant or non-relevant anatomical changes. This manual classification of the treatment fields was then used to establish an automatic field-wise ground truth based on spot-wise dosimetric range shifts, which were retrieved from integrated depth-dose (IDD) profiles. To determine the detection capability of anatomical changes with PGI, spot-wise PGI-based range shifts were initially compared with corresponding dosimetric IDD range shifts. As final endpoint, the agreement of a developed field-wise PGI classification model with the field-wise ground truth was determined. Therefore, the PGI model was optimized and tested for a subcohort of 131 and 70 treatment fields, respectively. RESULTS: The correlation between PGI and IDD range shifts was high, ρpearson = 0.67 (p < 0.01). Field-wise binary PGI classification resulted in an area under the curve of 0.72 and 0.80 for training and test cohorts, respectively. The model detected relevant anatomical changes in the independent test cohort, with a sensitivity and specificity of 74% and 79%, respectively. CONCLUSIONS: For the first time, evidence of the detection capability of anatomical changes in prostate-cancer PT from clinically acquired PGI data is shown. This emphasizes the benefit of PGI-based range verification and demonstrates its potential for online-adaptive PT.

Prediction of clinically relevant postoperative pancreatic fistula using radiomic features and preoperative data.

Clinically relevant postoperative pancreatic fistula (CR-POPF) can significantly affect the treatment course and outcome in pancreatic cancer patients. Preoperative prediction of CR-POPF can aid the surgical decision-making process and lead to better perioperative management of patients. In this retrospective study of 108 pancreatic head resection patients, we present risk models for the prediction of CR-POPF that use combinations of preoperative computed tomography (CT)-based radiomic features, mesh-based volumes of annotated intra- and peripancreatic structures and preoperative clinical data. The risk signatures were evaluated and analysed in detail by visualising feature expression maps and by comparing significant features to the established CR-POPF risk measures. Out of the risk models that were developed in this study, the combined radiomic and clinical signature performed best with an average area under receiver operating characteristic curve (AUC) of 0.86 and a balanced accuracy score of 0.76 on validation data. The following pre-operative features showed significant correlation with outcome in this signature ([Formula: see text]) - texture and morphology of the healthy pancreatic segment, intensity volume histogram-based feature of the pancreatic duct segment, morphology of the combined segment, and BMI. The predictions of this pre-operative signature showed strong correlation (Spearman correlation co-efficient, [Formula: see text]) with the intraoperative updated alternative fistula risk score (ua-FRS), which is the clinical gold standard for intraoperative CR-POPF risk stratification. These results indicate that the proposed combined radiomic and clinical signature developed solely based on preoperatively available clinical and routine imaging data can perform on par with the current state-of-the-art intraoperative models for CR-POPF risk stratification.

Profile of the multicenter cohort of the German Cancer Consortium's Clinical Communication Platform.

Treatment concepts in oncology are becoming increasingly personalized and diverse. Successively, changes in standards of care mandate continuous monitoring of patient pathways and clinical outcomes based on large, representative real-world data. The German Cancer Consortium's (DKTK) Clinical Communication Platform (CCP) provides such opportunity. Connecting fourteen university hospital-based cancer centers, the CCP relies on a federated IT-infrastructure sourcing data from facility-based cancer registry units and biobanks. Federated analyses resulted in a cohort of 600,915 patients, out of which 232,991 were incident since 2013 and for which a comprehensive documentation is available. Next to demographic data (i.e., age at diagnosis: 2.0% 0-20 years, 8.3% 21-40 years, 30.9% 41-60 years, 50.1% 61-80 years, 8.8% 81+ years; and gender: 45.2% female, 54.7% male, 0.1% other) and diagnoses (five most frequent tumor origins: 22,523 prostate, 18,409 breast, 15,575 lung, 13,964 skin/malignant melanoma, 9005 brain), the cohort dataset contains information about therapeutic interventions and response assessments and is connected to 287,883 liquid and tissue biosamples. Focusing on diagnoses and therapy-sequences, showcase analyses of diagnosis-specific sub-cohorts (pancreas, larynx, kidney, thyroid gland) demonstrate the analytical opportunities offered by the cohort's data. Due to its data granularity and size, the cohort is a potential catalyst for translational cancer research. It provides rapid access to comprehensive patient groups and may improve the understanding of the clinical course of various (even rare) malignancies. Therefore, the cohort may serve as a decisions-making tool for clinical trial design and contributes to the evaluation of scientific findings under real-world conditions.

DNA-Methylome-Based Tumor Hypoxia Classifier Identifies HPV-Negative Head and Neck Cancer Patients at Risk for Locoregional Recurrence after Primary Radiochemotherapy.

PURPOSE: Tumor hypoxia is a paradigmatic negative prognosticator of treatment resistance in head and neck squamous cell carcinoma (HNSCC). The lack of robust and reliable hypoxia classifiers limits the adaptation of stratified therapies. We hypothesized that the tumor DNA methylation landscape might indicate epigenetic reprogramming induced by chronic intratumoral hypoxia. EXPERIMENTAL DESIGN: A DNA-methylome-based tumor hypoxia classifier (Hypoxia-M) was trained in the TCGA (The Cancer Genome Atlas)-HNSCC cohort based on matched assignments using gene expression-based signatures of hypoxia (Hypoxia-GES). Hypoxia-M was validated in a multicenter DKTK-ROG trial consisting of human papillomavirus (HPV)-negative patients with HNSCC treated with primary radiochemotherapy (RCHT). RESULTS: Although hypoxia-GES failed to stratify patients in the DKTK-ROG, Hypoxia-M was independently prognostic for local recurrence (HR, 4.3; P = 0.001) and overall survival (HR, 2.34; P = 0.03) but not distant metastasis after RCHT in both cohorts. Hypoxia-M status was inversely associated with CD8 T-cell infiltration in both cohorts. Hypoxia-M was further prognostic in the TCGA-PanCancer cohort (HR, 1.83; P = 0.04), underscoring the breadth of this classifier for predicting tumor hypoxia status. CONCLUSIONS: Our findings highlight an unexplored avenue for DNA methylation-based classifiers as biomarkers of tumoral hypoxia for identifying high-risk features in patients with HNSCC tumors. See related commentary by Heft Neal and Brenner, p. 2954.

Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy.

PURPOSE: To develop a CT-based radiomic signature to predict biochemical recurrence (BCR) in prostate cancer patients after sRT guided by positron-emission tomography targeting prostate-specific membrane antigen (PSMA-PET). MATERIAL AND METHODS: Consecutive patients, who underwent 68Ga-PSMA11-PET/CT-guided sRT from three high-volume centers in Germany, were included in this retrospective multicenter study. Patients had PET-positive local recurrences and were treated with intensity-modulated sRT. Radiomic features were extracted from volumes of interests on CT guided by focal PSMA-PET uptakes. After preprocessing, clinical, radiomics, and combined clinical-radiomic models were developed combining different feature reduction techniques and Cox proportional hazard models within a nested cross validation approach. RESULTS: Among 99 patients, median interval until BCR was the radiomic models outperformed clinical models and combined clinical-radiomic models for prediction of BCR with a C-index of 0.71 compared to 0.53 and 0.63 in the test sets, respectively. In contrast to the other models, the radiomic model achieved significantly improved patient stratification in Kaplan-Meier analysis. The radiomic and clinical-radiomic model achieved a significantly better time-dependent net reclassification improvement index (0.392 and 0.762, respectively) compared to the clinical model. Decision curve analysis demonstrated a clinical net benefit for both models. Mean intensity was the most predictive radiomic feature. CONCLUSION: This is the first study to develop a PSMA-PET-guided CT-based radiomic model to predict BCR after sRT. The radiomic models outperformed clinical models and might contribute to guide personalized treatment decisions.