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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. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the use and efficiency of 3‐D deep learning, fully convolutional networks (DFCN) for simultaneous tumor cosegmentation on dual‐modality nonsmall cell lung cancer (NSCLC) and positron emission tomography (PET)‐computed tomography (CT) images. Methods: We used DFCN cosegmentation for NSCLC tumors in PET‐CT images, considering both the CT and PET information. The proposed DFCN‐based cosegmentation method consists of two coupled three‐dimensional (3D)‐UNets with an encoder‐decoder architecture, which can communicate with the other in order to share complementary information between PET and CT. The weighted average sensitivity and positive predictive values denoted as Scores, dice similarity coefficients (DSCs), and the average symmetric surface distances were used to assess the performance of the proposed approach on 60 pairs of PET/CTs. A Simultaneous Truth and Performance Level Estimation Algorithm (STAPLE) of 3 expert physicians' delineations were used as a reference. The proposed DFCN framework was compared to 3 graph‐based cosegmentation methods. Results: Strong agreement was observed when using the STAPLE references for the proposed DFCN cosegmentation on the PET‐CT images. The average DSCs on CT and PET are 0.861 ± 0.037 and 0.828 ± 0.087, respectively, using DFCN, compared to 0.638 ± 0.165 and 0.643 ± 0.141, respectively, when using the graph‐based cosegmentation method. The proposed DFCN cosegmentation using both PET and CT also outperforms the deep learning method using either PET or CT alone. Conclusions: The proposed DFCN cosegmentation is able to outperform existing graph‐based segmentation methods. The proposed DFCN cosegmentation shows promise for further integration with quantitative multimodality imaging tools in clinical trials. [ABSTRACT FROM AUTHOR]

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-RASG12V together with a nuclear fluorescent reporter. Monitoring of H-RASG12V-expressing MECs during extended live cell imaging permitted visualization of Rasdriven phenotypes via video microscopy. Challenging either basal or luminal MECs with H-RASG12V 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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., Competing Interests: JS-A reports honorarium and research funding from Elekta unrelated to this work. DH discloses a consulting relationship with Elekta and research funding from Elekta unrelated to this work. JC reports a research grant with Elekta unrelated to this work. KP discloses a consulting relationship with Siemens unrelated to this work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ferris, George, Plichta, Caster, Hyer, Smith and St-Aubin.)

Purpose: Conventional wisdom has rendered patients with brain metastases ineligible for clinical trials for fear that poor survival could mask the benefit of otherwise promising treatments. Our group previously published the diagnosis-specific Graded Prognostic Assessment (GPA). Updates with larger contemporary cohorts using molecular markers and newly identified prognostic factors have been published. The purposes of this work are to present all the updated indices in a single report to guide treatment choice, stratify research, and define an eligibility quotient to expand eligibility., Methods: A multi-institutional database of 6,984 patients with newly diagnosed brain metastases underwent multivariable analyses of prognostic factors and treatments associated with survival for each primary site. Significant factors were used to define the updated GPA. GPAs of 4.0 and 0.0 correlate with the best and worst prognoses, respectively., Results: Significant prognostic factors varied by diagnosis and new prognostic factors were identified. Those factors were incorporated into the updated GPA with robust separation ( P < .01) between subgroups. Survival has improved, but varies widely by GPA for patients with non-small-cell lung, breast, melanoma, GI, and renal cancer with brain metastases from 7-47 months, 3-36 months, 5-34 months, 3-17 months, and 4-35 months, respectively., Conclusion: Median survival varies widely and our ability to estimate survival for patients with brain metastases has improved. The updated GPA (available free at brainmetgpa.com) provides an accurate tool with which to estimate survival, individualize treatment, and stratify clinical trials. Instead of excluding patients with brain metastases, enrollment should be encouraged and those trials should be stratified by the GPA to ensure those trials make appropriate comparisons. Furthermore, we recommend the expansion of eligibility to allow for the enrollment of patients with previously treated brain metastases who have a 50% or greater probability of an additional year of survival (eligibility quotient > 0.50).

Background: Breast cancer treatment is based on estrogen receptors (ERs), progesterone receptors (PRs), and human epidermal growth factor receptor 2 (HER2). At the time of metastasis, receptor status can be discordant from that at initial diagnosis. The purpose of this study was to determine the incidence of discordance and its effect on survival and subsequent treatment in patients with breast cancer brain metastases (BCBM)., Methods: A retrospective database of 316 patients who underwent craniotomy for BCBM between 2006 and 2017 was created. Discordance was considered present if the ER, PR, or HER2 status differed between the primary tumor and the BCBM., Results: The overall receptor discordance rate was 132/316 (42%), and the subtype discordance rate was 100/316 (32%). Hormone receptors (HR, either ER or PR) were gained in 40/160 (25%) patients with HR-negative primary tumors. HER2 was gained in 22/173 (13%) patients with HER2-negative primary tumors. Subsequent treatment was not adjusted for most patients who gained receptors-nonetheless, median survival (MS) improved but did not reach statistical significance (HR, 17-28 mo, P = 0.12; HER2, 15-19 mo, P = 0.39). MS for patients who lost receptors was worse (HR, 27-18 mo, P = 0.02; HER2, 30-18 mo, P = 0.08)., Conclusions: Receptor discordance between primary tumor and BCBM is common, adversely affects survival if receptors are lost, and represents a missed opportunity for use of effective treatments if receptors are gained. Receptor analysis of BCBM is indicated when clinically appropriate. Treatment should be adjusted accordingly., Key Points: 1. Receptor discordance alters subtype in 32% of BCBM patients.2. The frequency of receptor gain for HR and HER2 was 25% and 13%, respectively.3. If receptors are lost, survival suffers. If receptors are gained, consider targeted treatment., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Purpose: Brain metastases are a common sequelae of breast cancer. Survival varies widely based on diagnosis-specific prognostic factors (PF). We previously published a prognostic index (Graded Prognostic Assessment [GPA]) for patients with breast cancer with brain metastases (BCBM), based on cohort A (1985-2007, n = 642), then updated it, reporting the effect of tumor subtype in cohort B (1993-2010, n = 400). The purpose of this study is to update the Breast GPA with a larger contemporary cohort (C) and compare treatment and survival across the 3 cohorts., Methods and Materials: A multi-institutional (19), multinational (3), retrospective database of 2473 patients with breast cancer with newly diagnosed brain metastases (BCBM) diagnosed from January 1, 2006, to December 31, 2017, was created and compared with prior cohorts. Associations of PF and treatment with survival were analyzed. Kaplan-Meier survival estimates were compared with log-rank tests. PF were weighted and the Breast GPA was updated such that a GPA of 0 and 4.0 correlate with the worst and best prognoses, respectively., Results: Median survival (MS) for cohorts A, B, and C improved over time (from 11, to 14 to 16 months, respectively; P < .01), despite the subtype distribution becoming less favorable. PF significant for survival were tumor subtype, Karnofsky Performance Status, age, number of BCBMs, and extracranial metastases (all P < .01). MS for GPA 0 to 1.0, 1.5-2.0, 2.5-3.0, and 3.5-4.0 was 6, 13, 24, and 36 months, respectively. Between cohorts B and C, the proportion of human epidermal receptor 2 + subtype decreased from 31% to 18% (P < .01) and MS in this subtype increased from 18 to 25 months (P < .01)., Conclusions: MS has improved modestly but varies widely by diagnosis-specific PF. New PF are identified and incorporated into an updated Breast GPA (free online calculator available at brainmetgpa.com). The Breast GPA facilitates clinical decision-making and will be useful for stratification of future clinical trials. Furthermore, these data suggest human epidermal receptor 2-targeted therapies improve clinical outcomes in some patients with BCBM., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Positron emission tomography and computed tomography (PET-CT) plays a critically important role in modern cancer therapy. In this paper, we focus on automated tumor delineation on PET-CT image pairs. Inspired by co-segmentation model, we develop a novel 3D image co-matting technique making use of the inner-modality information of PET and CT for matting. The obtained co-matting results are then incorporated in the graph-cut based PET-CT co-segmentation framework. Our comparative experiments on 32 PET-CT scan pairs of lung cancer patients demonstrate that the proposed 3D image co-matting technique can significantly improve the quality of cost images for the co-segmentation, resulting in highly accurate tumor segmentation on both PET and CT scan pairs.

Positron emission tomography and computed tomography (PET-CT) dual-modality imaging provides critical diagnostic information in modern cancer diagnosis and therapy. Automated accurate tumor delineation is essentially important in computer-assisted tumor reading and interpretation based on PET-CT. In this paper, we propose a novel approach for the segmentation of lung tumors that combines the powerful fully convolutional networks (FCN) based semantic segmentation framework (3D-UNet) and the graph cut based co-segmentation model. First, two separate deep UNets are trained on PET and CT, separately, to learn high level discriminative features to generate tumor/non-tumor masks and probability maps for PET and CT images. Then, the two probability maps on PET and CT are further simultaneously employed in a graph cut based co-segmentation model to produce the final tumor segmentation results. Comparative experiments on 32 PET-CT scans of lung cancer patients demonstrate the effectiveness of our method.

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.