Correlation of microscopic tumor extension with tumor microenvironment in esophageal cancer patients.

OBJECTIVE: In the era of image-guided adaptive radiotherapy, definition of the clinical target volume (CTV) is a challenge in various solid tumors, including esophageal cancer (EC). Many tumor microenvironmental factors, e.g., tumor cell proliferation or cancer stem cells, are hypothesized to be involved in microscopic tumor extension (MTE). Therefore, this study assessed the expression of FAK, ILK, CD44, HIF-1α, and Ki67 in EC patients after neoadjuvant radiochemotherapy followed by tumor resection (NRCHT+R) and correlated these markers with the MTE. METHODS: Formalin-fixed paraffin-embedded tumor resection specimens of ten EC patients were analyzed using multiplex immunofluorescence staining. Since gold fiducial markers had been endoscopically implanted at the proximal and distal tumor borders prior to NRCHT+R, correlation of the markers with the MTE was feasible. RESULTS: In tumor resection specimens of EC patients, the overall percentages of FAK+, CD44+, HIF-1α+, and Ki67+ cells were higher in tumor nests than in the tumor stroma, with the outcome for Ki67+ cells reaching statistical significance (p < 0.001). Conversely, expression of ILK+ cells was higher in tumor stroma, albeit not statistically significantly. In three patients, MTE beyond the fiducial markers was found, reaching up to 31 mm. CONCLUSION: Our findings indicate that the overall expression of FAK, HIF-1α, Ki67, and CD44 was higher in tumor nests, whereas that of ILK was higher in tumor stroma. Differences in the TME between patients with residual tumor cells in the original CTV compared to those without were not found. Thus, there is insufficient evidence that the TME influences the required CTV margin on an individual patient basis. TRIAL REGISTRATION NUMBER AND DATE: BO-EK-148042017 and BO-EK-177042022 on 20.06.2022, DRKS00011886, https://drks.de/search/de/trial/DRKS00011886 .

Towards a European prospective data registry for particle therapy.

The evidence for the value of particle therapy (PT) is still sparse. While randomized trials remain a cornerstone for robust comparisons with photon-based radiotherapy, data registries collecting real-world data can play a crucial role in building evidence for new developments. This Perspective describes how the European Particle Therapy Network (EPTN) is actively working on establishing a prospective data registry encompassing all patients undergoing PT in European centers. Several obstacles and hurdles are discussed, for instance harmonization of nomenclature and structure of technical and dosimetric data and data protection issues. A preferred approach is the adoption of a federated data registry model with transparent and agile governance to meet European requirements for data protection, transfer, and processing. Funding of the registry, especially for operation after the initial setup process, remains a major challenge.

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.

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.

A Phase 1 Study of the DNA-PK Inhibitor Peposertib in Combination With Radiation Therapy With or Without Cisplatin in Patients With Advanced Head and Neck Tumors.

PURPOSE: DNA-dependent protein kinase (DNA-PK) plays a key role in the repair of DNA double strand breaks via nonhomologous end joining. Inhibition of DNA-PK can enhance the effect of DNA double strand break inducing anticancer therapies. Peposertib (formerly "M3814") is an orally administered, potent, and selective small molecule DNA-PK inhibitor that has demonstrated radiosensitizing and antitumor activity in xenograft models and was well-tolerated in monotherapy. This phase 1 trial (National Clinical Trial 02516813) investigated the maximum tolerated dose, recommended phase 2 dose (RP2D), safety, and tolerability of peposertib in combination with palliative radiation therapy (RT) in patients with thoracic or head and neck tumors (arm A) and of peposertib in combination with cisplatin and curative-intent RT in patients with squamous cell carcinoma of the head and neck (arm B). METHODS AND MATERIALS: Patients received peposertib once daily in ascending dose cohorts as a tablet or capsule in combination with palliative RT (arm A) or in combination with intensity modulated curative-intent RT and cisplatin (arm B). RESULTS: The most frequently observed treatment-emergent adverse events were radiation skin injury, fatigue, and nausea in arm A (n = 34) and stomatitis, nausea, radiation skin injury, and dysgeusia in arm B (n = 11). Based on evaluations of dose-limiting toxicities, tolerability, and pharmacokinetic data, RP2D for arm A was declared as 200 mg peposertib tablet once daily in combination with RT. In arm B (n = 11), 50 mg peposertib was declared tolerable in combination with curative-intent RT and cisplatin. However, enrollment was discontinued because of insufficient exposure at that dose, and the RP2D was not formally declared. CONCLUSIONS: Peposertib in combination with palliative RT was well-tolerated up to doses of 200 mg once daily as tablet with each RT fraction. When combined with RT and cisplatin, a tolerable peposertib dose yielded insufficient exposure.

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.

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.

Comparison of 3D and 4D robustly optimized proton treatment plans for non-small cell lung cancer patients with tumour motion amplitudes larger than 5 mm.

Background and purpose: There is no consensus about an ideal robust optimization (RO) strategy for proton therapy of targets with large intrafractional motion. We investigated the plan robustness of 3D and different 4D RO strategies. Materials and methods: For eight non-small cell lung cancer patients with clinical target volume (CTV) motion >5 mm, different RO approaches were investigated: 3DRO considering the average CT (AvgCT) with a target density override, 4DRO considering three/all 4DCT phases, and 4DRO considering the AvgCT and three/all 4DCT phases. Robustness against setup/range errors, interplay effects based on breathing and machine log file data for deliveries with/without rescanning, and interfractional anatomical changes were analyzed for target coverage and OAR sparing. Results: All nominal plans fulfilled the clinical requirements with individual CTV coverage differences <2pp; 4DRO without AvgCT generated the most conformal dose distributions. Robustness against setup/range errors was best for 4DRO with AvgCT (18% more passed error scenarios than 3DRO). Interplay effects caused fraction-wise median CTV coverage loss of 3pp and missed maximum dose constraints for heart and esophagus in 18% of scenarios. CTV coverage and OAR sparing fulfilled requirements in all cases when accumulating four interplay scenarios. Interfractional changes caused less target misses for RO with AvgCT compared to 4DRO without AvgCT (≤42%/33% vs. ≥56%/44% failed single/accumulated scenarios). Conclusions: All RO strategies provided acceptable plans with equally low robustness against interplay effects demanding other mitigation than rescanning to ensure fraction-wise target coverage. 4DRO considering three phases and the AvgCT provided best compromise on planning effort and robustness.

Lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice: the LANTERN study.

BACKGROUND: The current management of lung cancer patients has reached a high level of complexity. Indeed, besides the traditional clinical variables (e.g., age, sex, TNM stage), new omics data have recently been introduced in clinical practice, thereby making more complex the decision-making process. With the advent of Artificial intelligence (AI) techniques, various omics datasets may be used to create more accurate predictive models paving the way for a better care in lung cancer patients. METHODS: The LANTERN study is a multi-center observational clinical trial involving a multidisciplinary consortium of five institutions from different European countries. The aim of this trial is to develop accurate several predictive models for lung cancer patients, through the creation of Digital Human Avatars (DHA), defined as digital representations of patients using various omics-based variables and integrating well-established clinical factors with genomic data, quantitative imaging data etc. A total of 600 lung cancer patients will be prospectively enrolled by the recruiting centers and multi-omics data will be collected. Data will then be modelled and parameterized in an experimental context of cutting-edge big data analysis. All data variables will be recorded according to a shared common ontology based on variable-specific domains in order to enhance their direct actionability. An exploratory analysis will then initiate the biomarker identification process. The second phase of the project will focus on creating multiple multivariate models trained though advanced machine learning (ML) and AI techniques for the specific areas of interest. Finally, the developed models will be validated in order to test their robustness, transferability and generalizability, leading to the development of the DHA. All the potential clinical and scientific stakeholders will be involved in the DHA development process. The main goals aim of LANTERN project are: i) To develop predictive models for lung cancer diagnosis and histological characterization; (ii) to set up personalized predictive models for individual-specific treatments; iii) to enable feedback data loops for preventive healthcare strategies and quality of life management. DISCUSSION: The LANTERN project will develop a predictive platform based on integration of multi-omics data. This will enhance the generation of important and valuable information assets, in order to identify new biomarkers that can be used for early detection, improved tumor diagnosis and personalization of treatment protocols. ETHICS COMMITTEE APPROVAL NUMBER: 5420 - 0002485/23 from Fondazione Policlinico Universitario Agostino Gemelli IRCCS - Università Cattolica del Sacro Cuore Ethics Committee. TRIAL REGISTRATION: clinicaltrial.gov - NCT05802771.

STereotactic Arrhythmia Radioablation (STAR): the Standardized Treatment and Outcome Platform for Stereotactic Therapy Of Re-entrant tachycardia by a Multidisciplinary consortium (STOPSTORM.eu) and review of current patterns of STAR practice in Europe.

The EU Horizon 2020 Framework-funded Standardized Treatment and Outcome Platform for Stereotactic Therapy Of Re-entrant tachycardia by a Multidisciplinary (STOPSTORM) consortium has been established as a large research network for investigating STereotactic Arrhythmia Radioablation (STAR) for ventricular tachycardia (VT). The aim is to provide a pooled treatment database to evaluate patterns of practice and outcomes of STAR and finally to harmonize STAR within Europe. The consortium comprises 31 clinical and research institutions. The project is divided into nine work packages (WPs): (i) observational cohort; (ii) standardization and harmonization of target delineation; (iii) harmonized prospective cohort; (iv) quality assurance (QA); (v) analysis and evaluation; (vi, ix) ethics and regulations; and (vii, viii) project coordination and dissemination. To provide a review of current clinical STAR practice in Europe, a comprehensive questionnaire was performed at project start. The STOPSTORM Institutions' experience in VT catheter ablation (83% ≥ 20 ann.) and stereotactic body radiotherapy (59% > 200 ann.) was adequate, and 84 STAR treatments were performed until project launch, while 8/22 centres already recruited VT patients in national clinical trials. The majority currently base their target definition on mapping during VT (96%) and/or pace mapping (75%), reduced voltage areas (63%), or late ventricular potentials (75%) during sinus rhythm. The majority currently apply a single-fraction dose of 25 Gy while planning techniques and dose prescription methods vary greatly. The current clinical STAR practice in the STOPSTORM consortium highlights potential areas of optimization and harmonization for substrate mapping, target delineation, motion management, dosimetry, and QA, which will be addressed in the various WPs.

Longitudinal and Multimodal Radiomics Models for Head and Neck Cancer Outcome Prediction.

Radiomics analysis provides a promising avenue towards the enabling of personalized radiotherapy. Most frequently, prognostic radiomics models are based on features extracted from medical images that are acquired before treatment. Here, we investigate whether combining data from multiple timepoints during treatment and from multiple imaging modalities can improve the predictive ability of radiomics models. We extracted radiomics features from computed tomography (CT) images acquired before treatment as well as two and three weeks after the start of radiochemotherapy for 55 patients with locally advanced head and neck squamous cell carcinoma (HNSCC). Additionally, we obtained features from FDG-PET images taken before treatment and three weeks after the start of therapy. Cox proportional hazards models were then built based on features of the different image modalities, treatment timepoints, and combinations thereof using two different feature selection methods in a five-fold cross-validation approach. Based on the cross-validation results, feature signatures were derived and their performance was independently validated. Discrimination regarding loco-regional control was assessed by the concordance index (C-index) and log-rank tests were performed to assess risk stratification. The best prognostic performance was obtained for timepoints during treatment for all modalities. Overall, CT was the best discriminating modality with an independent validation C-index of 0.78 for week two and weeks two and three combined. However, none of these models achieved statistically significant patient stratification. Models based on FDG-PET features from week three provided both satisfactory discrimination (C-index = 0.61 and 0.64) and statistically significant stratification (p=0.044 and p<0.001), but produced highly imbalanced risk groups. After independent validation on larger datasets, the value of (multimodal) radiomics models combining several imaging timepoints should be prospectively assessed for personalized treatment strategies.

Unchanged perfusion in normal-appearing white and grey matter of glioma patients nine months after proton beam irradiation.

PURPOSE: Radio(chemo)therapy is used as a standard treatment for glioma patients. The surrounding normal tissue is inevitably affected by the irradiation. The aim of this longitudinal study was to investigate perfusion alterations in the normal-appearing tissue after proton irradiation and assess the dose sensitivity of the normal tissue perfusion. METHODS: In 14 glioma patients, a sub-cohort of a prospective clinical trial (NCT02824731), perfusion changes in normal-appearing white matter (WM), grey matter (GM) and subcortical GM structures, i.e. caudate nucleus, hippocampus, amygdala, putamen, pallidum and thalamus, were evaluated before treatment and at three-monthly intervals after proton beam irradiation. The relative cerebral blood volume (rCBV) was assessed with dynamic susceptibility contrast MRI and analysed as the percentage ratio between follow-up and baseline image (ΔrCBV). Radiation-induced alterations were evaluated using Wilcoxon signed rank test. Dose and time correlations were investigated with univariate and multivariate linear regression models. RESULTS: No significant ΔrCBV changes were found in any normal-appearing WM and GM region after proton beam irradiation. A positive correlation with radiation dose was observed in the multivariate regression model applied to the combined ΔrCBV values of low (1-20 Gy), intermediate (21-40 Gy) and high (41-60 Gy) dose regions of GM (p < 0.001), while no time dependency was detected in any normal-appearing area. CONCLUSION: The perfusion in normal-appearing brain tissue remained unaltered after proton beam therapy. In further studies, a direct comparison with changes after photon therapy is recommended to confirm the different effect of proton therapy on the normal-appearing tissue.

Ultrasonic bone cement removal efficiency in total joint arthroplasty revision: A computer tomographic-based cadaver study.

Polymethylmethacrylate (PMMA) removal during septic total joint arthroplasty revision is associated with a high fracture and perforation risk. Ultrasonic cement removal is considered a bone-preserving technique. Currently, there is still a lack of sound data on efficacy as it is difficult to detect smaller residues with reasonable technical effort. However, incomplete removal is associated with the risk of biofilm coverage of the residue. Therefore, the study aimed to investigate the efficiency of ultrasonic-based PMMA removal in a human cadaver model. The femoral components of a total hip and a total knee prosthesis were implanted in two cadaver femoral canals by 3rd generation cement fixation technique. Implants were then removed. Cement mantle extraction was performed with the OSCAR-3-System ultrasonic system (Orthofix®). Quantitative analysis of cement residues was carried out with dual-energy and microcomputer tomography. With a 20 µm resolution, in vitro microcomputer tomography visualized tiniest PMMA residues. For clinical use, dual-energy computer tomography tissue decomposition with 0.75 mm resolution is suitable. With ultrasound, more than 99% of PMMA was removed. Seven hundred thirty-four residues with a mean volume of 0.40 ± 4.95 mm3 were identified with only 4 exceeding 1 cm in length in at least one axis. Ultrasonic cement removal of PMMA was almost complete and can therefore be considered a highly effective technique. For the first time, PMMA residues in the sub-millimetre range were detected by computer tomography. Clinical implications of the small remaining PMMA fraction on the eradication rate of periprosthetic joint infection warrants further investigations.

Orthotopic Glioblastoma Models for Evaluation of the Clinical Target Volume Concept.

In times of high-precision radiotherapy, the accurate and precise definition of the primary tumor localization and its microscopic spread is of enormous importance. In glioblastoma, the microscopic tumor extension is uncertain and, therefore, population-based margins for Clinical Target Volume (CTV) definition are clinically used, which could either be too small-leading to increased risk of loco-regional recurrences-or too large, thus, enhancing the probability of normal tissue toxicity. Therefore, the aim of this project is to investigate an individualized definition of the CTV in preclinical glioblastoma models based on specific biological tumor characteristics. The microscopic tumor extensions of two different orthotopic brain tumor models (U87MG_mCherry; G7_mCherry) were evaluated before and during fractionated radiotherapy and correlated with corresponding histological data. Representative tumor slices were analyzed using Matrix-Assisted Laser Desorption/Ionization (MALDI) and stained for putative stem-like cell markers as well as invasion markers. The edges of the tumor are clearly shown by the MALDI segmentation via unsupervised clustering of mass spectra and are consistent with the histologically defined border in H&E staining in both models. MALDI component analysis identified specific peaks as potential markers for normal brain tissue (e.g., 1339 m/z), whereas other peaks demarcated the tumors very well (e.g., 1562 m/z for U87MG_mCherry) irrespective of treatment. MMP14 staining revealed only a few positive cells, mainly in the tumor border, which could reflect the invasive front in both models. The results of this study indicate that MALDI information correlates with microscopic tumor spread in glioblastoma models. Therefore, an individualized CTV definition based on biological tumor characteristics seems possible, whereby the visualization of tumor volume and protein heterogeneity can be potentially used to define radiotherapy-sensitive and resistant areas.

Integrated radiogenomics analyses allow for subtype classification and improved outcome prognosis of patients with locally advanced HNSCC.

Patients with locally advanced head and neck squamous cell carcinoma (HNSCC) may benefit from personalised treatment, requiring biomarkers that characterize the tumour and predict treatment response. We integrate pre-treatment CT radiomics and whole-transcriptome data from a multicentre retrospective cohort of 206 patients with locally advanced HNSCC treated with primary radiochemotherapy to classify tumour molecular subtypes based on radiomics, develop surrogate radiomics signatures for gene-based signatures related to different biological tumour characteristics and evaluate the potential of combining radiomics features with full-transcriptome data for the prediction of loco-regional control (LRC). Using end-to-end machine-learning, we developed and validated a model to classify tumours of the atypical subtype (AUC [95% confidence interval] 0.69 [0.53-0.83]) based on CT imaging, observed that CT-based radiomics models have limited value as surrogates for six selected gene signatures (AUC < 0.60), and showed that combining a radiomics signature with a transcriptomics signature consisting of two metagenes representing the hedgehog pathway and E2F transcriptional targets improves the prognostic value for LRC compared to both individual sources (validation C-index [95% confidence interval], combined: 0.63 [0.55-0.73] vs radiomics: 0.60 [0.50-0.71] and transcriptomics: 0.59 [0.49-0.69]). These results underline the potential of multi-omics analyses to generate reliable biomarkers for future application in personalized oncology.

Immunohistochemical analyses of paraffin-embedded sections after primary surgery or trimodality treatment in esophageal carcinoma.

Background: The microscopic tumor extension before, during or after radiochemotherapy (RCHT) and its correlation with the tumor microenvironment (TME) are presently unknown. This information is, however, crucial in the era of image-guided, adaptive high-precision photon or particle therapy. Materials and methods: In this pilot study, we analyzed formalin-fixed paraffin-embedded (FFPE) tumor resection specimen from patients with histologically confirmed squamous cell carcinoma (SCC; n = 10) or adenocarcinoma (A; n = 10) of the esophagus, having undergone neoadjuvant radiochemotherapy followed by resection (NRCHT + R) or resection (R)]. FFPE tissue sections were analyzed by immunohistochemistry regarding tumor hypoxia (HIF-1α), proliferation (Ki67), immune status (PD1), cancer cell stemness (CXCR4), and p53 mutation status. Marker expression in HIF-1α subvolumes was part of a sub-analysis. Statistical analyses were performed using one-sided Mann-Whitney tests and Bland-Altman analysis. Results: In both SCC and AC patients, the overall percentages of positive tumor cells among the five TME markers, namely HIF-1α, Ki67, p53, CXCR4 and PD1 after NRCHT were lower than in the R cohort. However, only PD1 in SCC and Ki67 in AC showed significant association (Ki67: p = 0.03, PD1: p = 0.02). In the sub-analysis of hypoxic subvolumes among the AC patients, the percentage of positive tumor cells within hypoxic regions were statistically significantly lower in the NRCHT than in the R cohort across all the markers except for PD1. Conclusion: In this pilot study, we showed changes in the TME induced by NRCHT in both SCC and AC. These findings will be correlated with microscopic tumor extension measurements in a subsequent cohort of patients.

A systematic review of clinical studies on variable proton Relative Biological Effectiveness (RBE).

Recently, a number of clinical studies have explored links between possible Relative Biological Effectiveness (RBE) elevations and patient toxicities and/or image changes following proton therapy. Our objective was to perform a systematic review of such studies. We applied a "Problem [RBE], Intervention [Protons], Population [Patients], Outcome [Side effect]" search strategy to the PubMed database. From our search, we retrieved studies which: (a) performed novel voxel-wise analyses of patient effects versus physical dose and LET (n = 13), and (b) compared image changes between proton and photon cohorts with regard to proton RBE (n = 9). For each retrieved study, we extracted data regarding: primary tumour type; size of patient cohort; type of image change studied; image-registration method (deformable or rigid); LET calculation method, and statistical methodology. We compared and contrasted their methods in order to discuss the weight of clinical evidence for variable proton RBE. We concluded that clinical evidence for variable proton RBE remains statistically weak at present. Our principal recommendation is that proton centres and clinical trial teams collaborate to standardize follow-up protocols and statistical analysis methods, so that larger patient cohorts can ultimately be considered for RBE analyses.

Impact of Blood Parameters and Normal Tissue Dose on Treatment Outcome in Esophageal Cancer Patients Undergoing Neoadjuvant Radiochemotherapy.

Despite technological advances, normal tissue sparing in photon beam irradiation is still challenging. Since in esophageal cancer this may inflict damage on the lungs, heart and bone marrow, possibly impacting on outcome, the aim of this study was to investigate the association of normal tissue dose and blood parameters on the survival of patients having undergone neoadjuvant radiochemotherapy (RCTx) followed by surgery. This retrospective study included 125 patients irradiated to 40−41.4 Gy with photons or protons combined with concurrent chemotherapy. On initial and restaging 18F-FDG-PET/CT, the lungs and heart were contoured as organs at risk for which standardized uptake values (SUV) were evaluated. The mean radiation dose (Dmean) to the lungs and heart, the volume of the lungs receiving at least 20 Gy (V20Gy_lung) and various pre- and per-treatment blood parameters were included in the Cox regression analyses. Results: The median follow-up time was 19.8 months and median overall survival 37 months (95% confidence interval: 16−58.9 months). In multivariate analysis, higher radiation doses to the lungs and heart were statistically significantly associated with decreased overall survival (Dmean_lung: p < 0.001; V20Gy_lung: p < 0.002; Dmean_heart: p = 0.005). Neither the 18F-FDG-PET nor blood parameters were predictive for overall survival. In patients with locally advanced esophageal cancer treated with RCTx, the radiation dose to the heart and lungs was significantly associated with overall survival.

18F-Fluorodeoxyglucose Positron Emission Tomography of Head and Neck Cancer: Location and HPV Specific Parameters for Potential Treatment Individualization.

Purpose: 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is utilized for staging and treatment planning of head and neck squamous cell carcinomas (HNSCC). Some older publications on the prognostic relevance showed inconclusive results, most probably due to small study sizes. This study evaluates the prognostic and potentially predictive value of FDG-PET in a large multi-center analysis. Methods: Original analysis of individual FDG-PET and patient data from 16 international centers (8 institutional datasets, 8 public repositories) with 1104 patients. All patients received curative intent radiotherapy/chemoradiation (CRT) and pre-treatment FDG-PET imaging. Primary tumors were semi-automatically delineated for calculation of SUVmax, SUVmean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Cox regression analyses were performed for event-free survival (EFS), overall survival (OS), loco-regional control (LRC) and freedom from distant metastases (FFDM). Results: FDG-PET parameters were associated with patient outcome in the whole cohort regarding clinical endpoints (EFS, OS, LRC, FFDM), in uni- and multivariate Cox regression analyses. Several previously published cut-off values were successfully validated. Subgroup analyses identified tumor- and human papillomavirus (HPV) specific parameters. In HPV positive oropharynx cancer (OPC) SUVmax was well suited to identify patients with excellent LRC for organ preservation. Patients with SUVmax of 14 or less were unlikely to develop loco-regional recurrence after definitive CRT. In contrast FDG PET parameters deliver only limited prognostic information in laryngeal cancer. Conclusion: FDG-PET parameters bear considerable prognostic value in HNSCC and potential predictive value in subgroups of patients, especially regarding treatment de-intensification and organ-preservation. The potential predictive value needs further validation in appropriate control groups. Further research on advanced imaging approaches including radiomics or artificial intelligence methods should implement the identified cut-off values as benchmark routine imaging parameters.