Optimal use of limited proton resources for liver cancer patients in combined proton-photon treatments.

Liver cancer patients may benefit from proton therapy through increase of the tumor control probability (TCP). However, proton therapy is a limited resource and may not be available for all patients. We consider combined proton-photon liver SBRT treatments (CPPT) where only some fractions are delivered with protons.

Patterns of lymph node involvement for oral cavity squamous cell carcinoma.

Aim: Whereas the prevalence of lymph node level (LNL) involvement in oral cavity squamous cell carcinomas (OCSCC) has been reported, the details of lymphatic progression patterns are insufficiently quantified. We investigate how the risk of metastases in each LNL depends on the involvement of adjacent LNLs, T-category, subsite, primary tumor lateralization, and other risk factors.

Methods: We retrospectively analyzed patients with newly diagnosed OCSCC from two institutions, totaling 348 patients.

Modelling the lymphatic metastatic progression pathways of OPSCC from multi-institutional datasets.

The elective clinical target volume (CTV-N) in oropharyngeal squamous cell carcinoma (OPSCC) is currently based mostly on the prevalence of lymph node metastases in different lymph node levels (LNLs) for a given primary tumor location. We present a probabilistic model for ipsilateral lymphatic spread that can quantify the microscopic nodal involvement risk based on an individual patient's T-category and clinical involvement of LNLs at diagnosis. We extend a previously published hidden Markov model (HMM), which models the LNLs (I, II, III, IV, V, and VII) as hidden binary random variables (RVs).

A new approach to combined proton-photon therapy for metastatic cancer patients.

Proton therapy is a limited resource and is typically not available to metastatic cancer patients. Combined proton-photon therapy (CPPT), where most fractions are delivered with photons and only few with protons, represents an approach to distribute proton resources over a larger patient population. In this study, we consider stereotactic radiotherapy of multiple brain or liver metastases, and develop an approach to optimally take advantage of a single proton fraction by optimizing the proton and photon dose contributions to each individual metastasis.

Quantification of deformable image registration uncertainties for dose accumulation on head and neck cancer proton treatments.

Purpose: Head and neck cancer (HNC) patients in radiotherapy require adaptive treatment plans due to anatomical changes. Deformable image registration (DIR) is used in adaptive radiotherapy, e.g.

An in-silico planning study of stereotactic body radiation therapy for polymetastatic patients with more than ten extra-cranial lesions.

Background And Purpose: Limited data is available about the feasibility of stereotactic body radiation therapy (SBRT) for treating more than five extra-cranial metastases, and almost no data for treating more than ten. The aim of this study was to investigate the feasibility of SBRT in this polymetatstatic setting.

Materials And Methods: Consecutive metastatic melanoma patients with more than ten extra-cranial metastases and a maximum lesion diameter below 11 cm were selected from a single-center prospective registry for this in-silico planning study.

A multi-centric dataset on patient-individual pathological lymph node involvement in head and neck squamous cell carcinoma.

Dataset: We provide a dataset on lymph node metastases in 968 patients with newly diagnosed head and neck squamous cell carcinoma (HNSCC). All patients received neck dissection and we report the number of metastatic versus investigated lymph nodes per lymph node level (LNL) for every individual patient. Additionally, clinicopathological factors including T-category, primary tumor subsite (ICD-O-3 code), age, and sex are reported for all patients.

Quantitative Study on the Breast Density and the Volume of the Mammary Gland According to the Patient's Age and Breast Quadrant.

Objectives: Breast density is considered an independent risk factor for the development of breast cancer. This study aimed to quantitatively assess the percent breast density (PBD) and the mammary glands volume (MGV) according to the patient's age and breast quadrant. We propose a regression model to estimate PBD and MGV as a function of the patient's age.

Treatment planning comparison for head and neck cancer between photon, proton, and combined proton-photon therapy - From a fixed beam line to an arc.

Background And Purpose: This study investigates whether combined proton-photon therapy (CPPT) improves treatment plan quality compared to single-modality intensity-modulated radiation therapy (IMRT) or intensity-modulated proton therapy (IMPT) for head and neck cancer (HNC) patients. Different proton beam arrangements for CPPT and IMPT are compared, which could be of specific interest concerning potential future upright-positioned treatments. Furthermore, it is evaluated if CPPT benefits remain under inter-fractional anatomical changes for HNC treatments.

Patient-specific quality assurance for deformable IMRT/IMPT dose accumulation: Proposition and validation of energy conservation based validation criterion.

Background: Deformable image registration (DIR)-based dose accumulation (DDA) is regularly used in adaptive radiotherapy research. However, the applicability and reliability of DDA for direct clinical usage are still being debated. One primary concern is the validity of DDA, particularly for scenarios with substantial anatomical changes, for which energy-conservation problems were observed in conceptual studies.

Spatiotemporal fractionation schemes for stereotactic radiosurgery of multiple brain metastases.

Background: Stereotactic radiosurgery (SRS) is an established treatment for patients with brain metastases (BMs). However, damage to the healthy brain may limit the tumor dose for patients with multiple lesions.

Purpose: In this study, we investigate the potential of spatiotemporal fractionation schemes to reduce the biological dose received by the healthy brain in SRS of multiple BMs, and also demonstrate a novel concept of spatiotemporal fractionation for polymetastatic cancer patients that faces less hurdles for clinical implementation.

Automatic and standardized quality assurance of digital mammography and tomosynthesis with deep convolutional neural networks.

Objectives: The aim of this study was to develop and validate a commercially available AI platform for the automatic determination of image quality in mammography and tomosynthesis considering a standardized set of features.

Materials And Methods: In this retrospective study, 11,733 mammograms and synthetic 2D reconstructions from tomosynthesis of 4200 patients from two institutions were analyzed by assessing the presence of seven features which impact image quality in regard to breast positioning. Deep learning was applied to train five dCNN models on features detecting the presence of anatomical landmarks and three dCNN models for localization features.

Radiation dose estimates based on Monte Carlo simulation for spiral breast computed tomography imaging in a large cohort of patients.

Background: Spiral breast computed tomography (BCT) equipped with a photon-counting detector (PCD) is a new radiological modality allowing for the compression-free acquisition of high-resolution 3-D datasets of the breast. Optimized dose exposu04170/re setups according to breast size were previously proposed but could not effectively be applied in a clinical environment due to ambiguity in measuring breast size.

Purpose: This study aims to report the standard radiation dose values in a large cohort of patients examined with BCT, and to provide a mathematical model to estimate radiation dose based on morphological features of the breast.

Volumetric memory network for interactive medical image segmentation.

Despite recent progress of automatic medical image segmentation techniques, fully automatic results usually fail to meet clinically acceptable accuracy, thus typically require further refinement. To this end, we propose a novel Volumetric Memory Network, dubbed as VMN, to enable segmentation of 3D medical images in an interactive manner. Provided by user hints on an arbitrary slice, a 2D interaction network is firstly employed to produce an initial 2D segmentation for the chosen slice.

Fully automated breast segmentation on spiral breast computed tomography images.

Introduction: The quantification of the amount of the glandular tissue and breast density is important to assess breast cancer risk. Novel photon-counting breast computed tomography (CT) technology has the potential to quantify them. For accurate analysis, a dedicated method to segment the breast components-the adipose and glandular tissue, skin, pectoralis muscle, skinfold section, rib, and implant-is required.

A novel stochastic optimization method for handling misalignments of proton and photon doses in combined treatments.

Combined proton-photon treatments, where most fractions are delivered with photons and only a few are delivered with protons, may represent a practical approach to optimally use limited proton resources. It has been shown that, when organs at risk (OARs) are located within or near the tumor, the optimal multi-modality treatment uses protons to hypofractionate parts of the target volume and photons to achieve near-uniform fractionation in dose-limiting healthy tissues, thus exploiting the fractionation effect. These plans may be sensitive to range and setup errors, especially misalignments between proton and photon doses.

Technical note: Optimal allocation of limited proton therapy resources using model-based patient selection.

Purpose: We consider the following scenario: A radiotherapy clinic has a limited number of proton therapy slots available each day to treat cancer patients of a given tumor site. The clinic's goal is to minimize the expected number of complications in the cohort of all patients of that tumor site treated at the clinic, and thereby maximize the benefit of its limited proton resources.

Methods: To address this problem, we extend the normal tissue complication probability (NTCP) model-based approach to proton therapy patient selection to the situation of limited resources at a given institution.

A dataset on patient-individual lymph node involvement in oropharyngeal squamous cell carcinoma.

Dataset: We provide a dataset on lymph node level (LNL) involvement in 287 patients with newly diagnosed oropharyngeal squamous cell carcinoma (OPSCC). For each patient, ipsilateral and contralateral LNL involvement for levels I to VII is reported together with clinicopathological factors including TNM-stage, primary tumor subsite, tumor lateralization, HPV status, sex, age, smoking status, and primary treatment. LNL involvement was assessed individually based on available diagnostic modalities (PET, MRI, CT, fine needle aspiration) by reviewing pathology and radiology reports together with the radiological images.

Radiation-induced lymphopenia does not impact treatment efficacy in a mouse tumor model.

Radiation-induced lymphopenia is a common occurrence in radiation oncology and an established negative prognostic factor, however the mechanisms underlying the relationship between lymphopenia and inferior survival remain elusive. The relevance of lymphocyte co-irradiation as critical normal tissue component at risk is an emerging topic of high clinical relevance, even more so in the context of potentially synergistic radiotherapy-immunotherapy combinations. The impact of the radiotherapy treatment volume on the lymphocytes of healthy and tumor-bearing mice was investigated in a novel mouse model of radiation-induced lymphopenia.

Combined proton-photon therapy for non-small cell lung cancer.

Purpose: Advanced non-small cell lung cancer (NSCLC) is still a challenging indication for conventional photon radiotherapy. Proton therapy has the potential to improve outcomes, but proton treatment slots remain a limited resource despite an increasing number of proton therapy facilities. This work investigates the potential benefits of optimally combined proton-photon therapy delivered using a fixed horizontal proton beam line in combination with a photon Linac, which could increase accessibility to proton therapy for such a patient cohort.

Fully automatic classification of automated breast ultrasound (ABUS) imaging according to BI-RADS using a deep convolutional neural network.

Purpose: The aim of this study was to develop and test a post-processing technique for detection and classification of lesions according to the BI-RADS atlas in automated breast ultrasound (ABUS) based on deep convolutional neural networks (dCNNs).

Methods And Materials: In this retrospective study, 645 ABUS datasets from 113 patients were included; 55 patients had lesions classified as high malignancy probability. Lesions were categorized in BI-RADS 2 (no suspicion of malignancy), BI-RADS 3 (probability of malignancy < 3%), and BI-RADS 4/5 (probability of malignancy > 3%).

Detailed patient-individual reporting of lymph node involvement in oropharyngeal squamous cell carcinoma with an online interface.

Purpose/objective: Whereas the prevalence of lymph node level (LNL) involvement in head & neck squamous cell carcinomas (HNSCC) has been reported, the details of lymphatic progression patterns are insufficiently quantified. In this study, we investigate how the risk of metastases in each LNL depends on the involvement of upstream LNLs, T-category, HPV status and other risk factors.

Materials/methods: We retrospectively analyzed patients with newly diagnosed oropharyngeal squamous cell carcinoma (OPSCC) treated at a single institution, resulting in a dataset of 287 patients.

Combined proton-photon treatment for breast cancer.

Proton therapy remains a limited resource due to gantry size and its cost. Recently, a new design without a gantry has been suggested. It may enable combined proton-photon therapy (CPPT) in conventional bunkers and allow the widespread use of protons.

Quantification of the spatial distribution of primary tumors in the lung to develop new prognostic biomarkers for locally advanced NSCLC.

The anatomical location and extent of primary lung tumors have shown prognostic value for overall survival (OS). However, its manual assessment is prone to interobserver variability. This study aims to use data driven identification of image characteristics for OS in locally advanced non-small cell lung cancer (NSCLC) patients.