Clinical experience with adaptive MRI-guided pancreatic SBRT and the use of abdominal compression to reduce treatment volume.

Introduction: This work presents a method to treat stereotactic body radiation therapy (SBRT) for pancreatic cancer on a magnetic resonance-guided linear accelerator (MR-linac) using daily adaptation, real-time motion monitoring, and abdominal compression. Methods: The motion management and treatment planning process involves a magnetic resonance imaging (MRI) simulation with cine and 3D images, a computed tomography (CT) simulation with a breath-hold CT and a 4DCT, pre-treatment verification and planning MRI, and intrafraction MRI cine images. Results: The results from 26 patients were included in this work. Our motion management process results in consistent motion analysis on the CT simulation, MRI simulation, and each treatment fraction. The liver dome was found to be an overestimate of tumor superior/inferior (SI) motion for most patients. Adding compression reduced SI liver dome motion by 6.2 mm on average. Clinical outcomes are similar to those observed in the literature. Conclusions: In this work, we demonstrate how pancreatic SBRT can be successfully treated on an MR-linac using abdominal compression. This allows for an increased duty cycle compared to gating and/or breath-hold techniques.

Prostate-Specific Membrane Antigen (PSMA) Theranostics for Treatment of Oligometastatic Prostate Cancer.

Theranostics, a combination of therapy and diagnostics, is a field of personalized medicine involving the use of the same or similar radiopharmaceutical agents for the diagnosis and treatment of patients. Prostate-specific membrane antigen (PSMA) is a promising theranostic target for the treatment of prostate cancers. Diagnostic PSMA radiopharmaceuticals are currently used for staging and diagnosis of prostate cancers, and imaging can predict response to therapeutic PSMA radiopharmaceuticals. While mainly used in the setting of metastatic, castrate-resistant disease, clinical trials are investigating the use of PSMA-based therapy at earlier stages, including in hormone-sensitive or hormone-naïve prostate cancers, and in oligometastatic prostate cancers. This review explores the use of PSMA as a theranostic target and investigates the potential use of PSMA in earlier stage disease, including hormone-sensitive metastatic prostate cancer, and oligometastatic prostate cancer.

Safety and Efficacy of Stereotactic Body Radiation Therapy for Locoregional Recurrences After Prior Chemoradiation for Advanced Esophageal Carcinoma.

Purpose: This study aimed to investigate the feasibility of stereotactic body radiation therapy (SBRT) as salvage therapy for locally recurrent esophageal cancer. We hypothesized that SBRT would provide durable treated tumor control with minimal associated toxicity in patients with progressive disease after definitive radiation, chemotherapy, and surgical resection. Methods: This single-institution retrospective study assessed outcomes in patients who received SBRT for locoregional failure of esophageal cancer after initial curative-intent treatment. Only patients who had received neoadjuvant chemoradiation (≥41.4 Gy) for esophageal cancer were selected. Subsequent surgical resection was optional but institutional follow-up by an oncologist was required. The primary endpoints of this study were gastrointestinal and constitutional toxicity, scored with the Common Terminology Criteria for Adverse Events v5.0. A secondary outcome, treated-tumor control, was assessed with RECIST v1.1. Results: Nine patients (11 locoregional recurrences) treated with SBRT were reviewed, with a median follow-up time of 10.5 months. Most patients initially presented with T3 (88.9%), N1 (55.6%), moderately differentiated (66.7%) adenocarcinoma (88.9%), and had received a median 50.4 Gy delivered over 28 fractions with concurrent carboplatin/paclitaxel chemotherapy followed by surgical resection. Median time to recurrence was 16.3 months. Median total dose delivered by SBRT was 27.5 Gy (delivered in five fractions). Two patients experienced acute grade 1 fatigue and vomiting. No patient experienced grade 3 or higher toxicity. One patient experienced failure in the SBRT treatment field at 5.8 months after treatment and six patients developed distant failure. The median progression-free survival time for SBRT-treated tumors was 5.0 months, and median overall survival time was 12.9 months. Conclusions: This single-institution study demonstrated the feasibility of SBRT for locoregional recurrence of esophageal cancer with minimal treatment-related toxicity and high rates of treated tumor control. Prospective studies identifying ideal salvage SBRT candidates for locoregional failure as well as validating its safety are needed.

Deep segmentation networks predict survival of non-small cell lung cancer.

Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography/computed tomography (PET/CT) images have predictive power for NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neural networks (CNN) are rapidly emerging as a new method for cancer image analysis, with significantly enhanced predictive power compared to hand-crafted radiomics features. Here we show that CNNs trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value. In a retrospective study on pre-treatment PET-CT images of 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET and CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals. The U-Net algorithm has not seen any other clinical information (e.g. survival, age, smoking history, etc.) than the images and the corresponding tumor contours provided by physicians. In addition, we observed the same trend by validating the U-Net features against an extramural data set provided by Stanford Cancer Institute. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of metastasis and recurrence appear to match with the regions where the U-Net features identified patterns that predicted higher likelihoods of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination. For example, the deep learned PET/CT features can not only predict survival but also visualize high-risk regions within or adjacent to the primary tumor and hence potentially impact therapeutic outcomes by optimal selection of therapeutic strategy or first-line therapy adjustment.

Automated model-based quantitative analysis of phantoms with spherical inserts in FDG PET scans.

PURPOSE: Quality control plays an increasingly important role in quantitative PET imaging and is typically performed using phantoms. The purpose of this work was to develop and validate a fully automated analysis method for two common PET/CT quality assurance phantoms: the NEMA NU-2 IQ and SNMMI/CTN oncology phantom. The algorithm was designed to only utilize the PET scan to enable the analysis of phantoms with thin-walled inserts. METHODS: We introduce a model-based method for automated analysis of phantoms with spherical inserts. Models are first constructed for each type of phantom to be analyzed. A robust insert detection algorithm uses the model to locate all inserts inside the phantom. First, candidates for inserts are detected using a scale-space detection approach. Second, candidates are given an initial label using a score-based optimization algorithm. Third, a robust model fitting step aligns the phantom model to the initial labeling and fixes incorrect labels. Finally, the detected insert locations are refined and measurements are taken for each insert and several background regions. In addition, an approach for automated selection of NEMA and CTN phantom models is presented. The method was evaluated on a diverse set of 15 NEMA and 20 CTN phantom PET/CT scans. NEMA phantoms were filled with radioactive tracer solution at 9.7:1 activity ratio over background, and CTN phantoms were filled with 4:1 and 2:1 activity ratio over background. For quantitative evaluation, an independent reference standard was generated by two experts using PET/CT scans of the phantoms. In addition, the automated approach was compared against manual analysis, which represents the current clinical standard approach, of the PET phantom scans by four experts. RESULTS: The automated analysis method successfully detected and measured all inserts in all test phantom scans. It is a deterministic algorithm (zero variability), and the insert detection RMS error (i.e., bias) was 0.97, 1.12, and 1.48 mm for phantom activity ratios 9.7:1, 4:1, and 2:1, respectively. For all phantoms and at all contrast ratios, the average RMS error was found to be significantly lower for the proposed automated method compared to the manual analysis of the phantom scans. The uptake measurements produced by the automated method showed high correlation with the independent reference standard (R2 ≥ 0.9987). In addition, the average computing time for the automated method was 30.6 s and was found to be significantly lower (P ≪ 0.001) compared to manual analysis (mean: 247.8 s). CONCLUSIONS: The proposed automated approach was found to have less error when measured against the independent reference than the manual approach. It can be easily adapted to other phantoms with spherical inserts. In addition, it eliminates inter- and intraoperator variability in PET phantom analysis and is significantly more time efficient, and therefore, represents a promising approach to facilitate and simplify PET standardization and harmonization efforts.

Development of a radiobiological evaluation tool to assess the expected clinical impacts of contouring accuracy between manual and semi-automated segmentation algorithms.

RADEval is a tool developed to assess the expected clinical impact of contouring accuracy when comparing manual contouring and semi-automated segmentation. The RADEval tool, designed to process large scale datasets, imported a total of 2,760 segmentation datasets, along with a Simultaneous Truth and Performance Level Estimation (STAPLE) to act as ground truth tumor segmentations. Virtual dose-maps were created within RADEval and two different tumor control probability (TCP) values using a Logistic and a Poisson TCP models were calculated in RADEval using each STAPLE and each dose-map. RADEval also virtually generated a ring of normal tissue. To evaluate clinical impact, two different uncomplicated TCP (UTCP) values were calculated in RADEval by using two TCP-NTCP correlation parameters (δ = 0 and 1). NTCP values showed that semi-automatic segmentation resulted in lower NTCP with an average 1.5 - 1.6 % regardless of STAPLE design. This was true even though each normal tissue was created from each STAPLE (p <; 0.00001). TCP and UTCP presented no statistically significant differences (p ≥ 0.1884). The intra-operator standard deviations (SDs) for TCP, NTCP and UTCP were significantly lower for the semi-automatic segmentation method regardless of STAPLE design (p <; 0.0331). Both intra-and inter-operator SDs of TCP, NTCP and UTCP were significantly lower for semi-automatic segmentation for the STAPLE 1 design (p <;0.0331). RADEval was able to efficiently process 4,920 datasets of two STAPLE designs and successfully assess the expected clinical impact of contouring accuracy.

Semiautomated segmentation of head and neck cancers in 18F-FDG PET scans: A just-enough-interaction approach.

PURPOSE: The purpose of this work was to develop, validate, and compare a highly computer-aided method for the segmentation of hot lesions in head and neck 18F-FDG PET scans. METHODS: A semiautomated segmentation method was developed, which transforms the segmentation problem into a graph-based optimization problem. For this purpose, a graph structure around a user-provided approximate lesion centerpoint is constructed and a suitable cost function is derived based on local image statistics. To handle frequently occurring situations that are ambiguous (e.g., lesions adjacent to each other versus lesion with inhomogeneous uptake), several segmentation modes are introduced that adapt the behavior of the base algorithm accordingly. In addition, the authors present approaches for the efficient interactive local and global refinement of initial segmentations that are based on the "just-enough-interaction" principle. For method validation, 60 PET/CT scans from 59 different subjects with 230 head and neck lesions were utilized. All patients had squamous cell carcinoma of the head and neck. A detailed comparison with the current clinically relevant standard manual segmentation approach was performed based on 2760 segmentations produced by three experts. RESULTS: Segmentation accuracy measured by the Dice coefficient of the proposed semiautomated and standard manual segmentation approach was 0.766 and 0.764, respectively. This difference was not statistically significant (p = 0.2145). However, the intra- and interoperator standard deviations were significantly lower for the semiautomated method. In addition, the proposed method was found to be significantly faster and resulted in significantly higher intra- and interoperator segmentation agreement when compared to the manual segmentation approach. CONCLUSIONS: Lack of consistency in tumor definition is a critical barrier for radiation treatment targeting as well as for response assessment in clinical trials and in clinical oncology decision-making. The properties of the authors approach make it well suited for applications in image-guided radiation oncology, response assessment, or treatment outcome prediction.

Basal but not luminal mammary epithelial cells require PI3K/mTOR signaling for Ras-driven overgrowth.

The mammary ducts of humans and mice are comprised of two main mammary epithelial cell (MEC) subtypes: a surrounding layer of basal MECs and an inner layer of luminal MECs. Breast cancer subtypes show divergent clinical behavior that may reflect properties inherent in their MEC compartment of origin. How the response to a cancer-initiating genetic event is shaped by MEC subtype remains largely unexplored. Using the mouse mammary gland, we designed organotypic three-dimensional culture models that permit challenge of discrete MEC compartments with the same oncogenic insult. Mammary organoids were prepared from mice engineered for compartment-restricted coexpression of oncogenic H-RAS(G12V) together with a nuclear fluorescent reporter. Monitoring of H-RAS(G12V)-expressing MECs during extended live cell imaging permitted visualization of Ras-driven phenotypes via video microscopy. Challenging either basal or luminal MECs with H-RAS(G12V) drove MEC proliferation and survival, culminating in aberrant organoid overgrowth. In each compartment, Ras activation triggered modes of collective MEC migration and invasion that contrasted with physiologic modes used during growth factor-initiated branching morphogenesis. Although basal and luminal Ras activation produced similar overgrowth phenotypes, inhibitor studies revealed divergent use of Ras effector pathways. Blocking either the phosphoinositide 3-kinase or the mammalian target of rapamycin pathway completely suppressed Ras-driven invasion and overgrowth of basal MECs, but only modestly attenuated Ras-driven phenotypes in luminal MECs. We show that MEC subtype defines signaling pathway dependencies downstream of Ras. Thus, cells-of-origin may critically determine the drug sensitivity profiles of mammary neoplasia.