Presentation, Prognostic Factors and Patterns of Failure in Adult Rhabdomyosarcoma

Purpose: The purpose of our study is to retrospectively review our institutional experience with adult rhabdomyosarcoma (RMS) to determine presentation, prognostic factors and patterns of failure in this disease

Phase 1b/2a trial of the superoxide dismutase mimetic GC4419 to reduce chemoradiotherapy-induced oral mucositis in patients with oral cavity or oropharyngeal carcinoma

PURPOSE: To assess the safety of the superoxide dismutase mimetic GC4419 in combination with radiation and concurrent cisplatin for patients with oral cavity or oropharyngeal cancer (OCC) and to assess the potential of GC4419 to reduce severe oral mucositis (OM). PATIENTS AND METHODS: Patients with locally advanced OCC treated with definitive or postoperative intensity modulated radiation therapy (IMRT) plus cisplatin received GC4419 by 60-minute intravenous infusion, ending \u3c60 minutes before IMRT, Monday through Friday for 3 to 7 weeks, in a dose and duration escalation study. Oral mucositis was assessed twice weekly during and weekly after IMRT. RESULTS: A total of 46 patients received GC4419 in 11 separate dosing and duration cohorts: dose escalation occurred in 5 cohorts receiving 15 to 112 mg/d over 3 weeks (n=20), duration escalation in 3 cohorts receiving 112 mg/d over 4 to 6 weeks (n=12), and then 3 additional cohorts receiving 30 or 90 mg/d over 6 to 7 weeks (n=14). A maximum tolerated dose was not reached. One dose-limiting toxicity (grade 3 gastroenteritis and vomiting with hyponatremia) occurred in each of 2 separate cohorts at 112 mg. Nausea/vomiting and facial paresthesia during infusion seemed to be GC4419 dose-related. Severe OM occurred through 60 Gy in 4 of 14 patients (29%) dosed for 6 to 7 weeks, with median duration of only 2.5 days. CONCLUSIONS: The safety of GC4419 concurrently with chemoradiation for OCC was acceptable. Toxicities included nausea/vomiting and paresthesia. Doses of 30 and 90 mg/d administered for 7 weeks were selected for further study. In an exploratory analysis, severe OM seemed less frequent and briefer than expected

Clinical Trial Design and Development Work Group within the Quantitative Imaging Network

The Clinical Trial Design and Development Working Group within the Quantitative Imaging Network focuses on providing support for the development, validation, and harmonization of quantitative imaging (QI) methods and tools for use in cancer clinical trials. In the past 10 years, the Group has been working in several areas to identify challenges and opportunities in clinical trials involving QI and radiation oncology. The Group has been working with Quantitative Imaging Network members and the Quantitative Imaging Biomarkers Alliance leadership to develop guidelines for standardizing the reporting of quantitative imaging. As a validation platform, the Group led a multireader study to test a semi-automated positron emission tomography quantification software. Clinical translation of QI tools cannot be possible without a continuing dialogue with clinical users. This article also highlights the outreach activities extended to cooperative groups and other organizations that promote the use of QI tools to support clinical decisions

Estimating survival in patients with gastrointestinal cancers and brain metastases: An update of the graded prognostic assessment for gastrointestinal cancers (GI-GPA).

BackgroundPatients with gastrointestinal cancers and brain metastases (BM) represent a unique and heterogeneous population. Our group previously published the Diagnosis-Specific Graded Prognostic Assessment (DS-GPA) for patients with GI cancers (GI-GPA) (1985-2007, n = 209). The purpose of this study is to update the GI-GPA based on a larger contemporary database.MethodsAn IRB-approved consortium database analysis was performed using a multi-institutional (18), multi-national (3) cohort of 792 patients with gastrointestinal (GI) cancers, with newly-diagnosed BM diagnosed between 1/1/2006 and 12/31/2017. Survival was measured from date of first treatment for BM. Multiple Cox regression was used to select and weight prognostic factors in proportion to their hazard ratios. These factors were incorporated into the updated GI-GPA.ResultsMedian survival (MS) varied widely by primary site and other prognostic factors. Four significant factors (KPS, age, extracranial metastases and number of BM) were used to formulate the updated GI-GPA. Overall MS for this cohort remains poor; 8 months. MS by GPA was 3, 7, 11 and 17 months for GPA 0-1, 1.5-2, 2.5-3.0 and 3.5-4.0, respectively. >30% present in the worst prognostic group (GI-GPA of ≤1.0).ConclusionsBrain metastases are not uncommon in GI cancer patients and MS varies widely among them. This updated GI-GPA index improves our ability to estimate survival for these patients and will be useful for therapy selection, end-of-life decision-making and stratification for future clinical trials. A user-friendly, free, on-line app to calculate the GPA score and estimate survival for an individual patient is available at brainmetgpa.com

Predictive power of deep-learning segmentation based prognostication model in non-small cell lung cancer

PurposeThe study aims to create a model to predict survival outcomes for non-small cell lung cancer (NSCLC) after treatment with stereotactic body radiotherapy (SBRT) using deep-learning segmentation based prognostication (DESEP).MethodsThe DESEP model was trained using imaging from 108 patients with NSCLC with various clinical stages and treatment histories. The model generated predictions based on unsupervised features learned by a deep-segmentation network from computed tomography imaging to categorize patients into high and low risk groups for overall survival (DESEP-predicted-OS), disease specific survival (DESEP-predicted-DSS), and local progression free survival (DESEP-predicted-LPFS). Serial assessments were also performed using auto-segmentation based volumetric RECISTv1.1 and computer-based unidimensional RECISTv1.1 patients was performed.ResultsThere was a concordance between the DESEP-predicted-LPFS risk category and manually calculated RECISTv1.1 (φ=0.544, p=0.001). Neither the auto-segmentation based volumetric RECISTv1.1 nor the computer-based unidimensional RECISTv1.1 correlated with manual RECISTv1.1 (p=0.081 and p=0.144, respectively). While manual RECISTv1.1 correlated with LPFS (HR=6.97,3.51-13.85, c=0.70, p<0.001), it could not provide insight regarding DSS (p=0.942) or OS (p=0.662). In contrast, the DESEP-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p<0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p<0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p<0.001). The promising results of the DESEP model were reproduced for the independent, external datasets of Stanford University, classifying survival and ‘dead’ group in their Kaplan-Meyer curves (p = 0.019).ConclusionDeep-learning segmentation based prognostication can predict LPFS as well as OS, and DSS after SBRT for NSCLC. It can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients receiving SBRT.SummaryWhile current standard of care, manual RECISTv1.1 correlated with local progression free survival (LPFS) (HR=6.97,3.51-13.85, c=0.70, p<0.001), it could not provide insight regarding disease specific survival (DSS) (p=0.942) or overall survival (OS) (p=0.662). In contrast, the deep-learning segmentation based prognostication (DESEP)-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p<0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p<0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p<0.001). DESEP can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients

Phase IIb, Randomized, Double-Blind Trial of GC4419 Versus Placebo to Reduce Severe Oral Mucositis Due to Concurrent Radiotherapy and Cisplatin For Head and Neck Cancer

PURPOSE: Oral mucositis (OM) remains a common, debilitating toxicity of radiation therapy (RT) for head and neck cancer. The goal of this phase IIb, multi-institutional, randomized, double-blind trial was to compare the efficacy and safety of GC4419, a superoxide dismutase mimetic, with placebo to reduce the duration, incidence, and severity of severe OM (SOM). PATIENTS AND METHODS: A total of 223 patients (from 44 institutions) with locally advanced oral cavity or oropharynx cancer planned to be treated with definitive or postoperative intensity-modulated RT (IMRT; 60 to 72 Gy [≥ 50 Gy to two or more oral sites]) plus cisplatin (weekly or every 3 weeks) were randomly assigned to receive 30 mg (n = 73) or 90 mg (n = 76) of GC4419 or to receive placebo (n = 74) by 60-minute intravenous administration before each IMRT fraction. WHO grade of OM was assessed biweekly during IMRT and then weekly for up to 8 weeks after IMRT. The primary endpoint was duration of SOM tested for each active dose level versus placebo (intent-to-treat population, two-sided α of .05). The National Cancer Institute Common Terminology Criteria for Adverse Events, version 4.03, was used for adverse event grading. RESULTS: Baseline patient and tumor characteristics as well as treatment delivery were balanced. With 90 mg GC4419 versus placebo, SOM duration was significantly reduced (P = .024; median, 1.5 v 19 days). SOM incidence (43% v 65%; P = .009) and severity (grade 4 incidence, 16% v 30%; P = .045) also were improved. Intermediate improvements were seen with the 30-mg dose. Safety was comparable across arms, with no significant GC4419-specific toxicity nor increase of known toxicities of IMRT plus cisplatin. The 2-year follow-up for tumor outcomes is ongoing. CONCLUSION: GC4419 at a dose of 90 mg produced a significant, clinically meaningful reduction of SOM duration, incidence, and severity with acceptable safety

Estrogen/progesterone Receptor and HER2 Discordance Between Primary Tumor and Brain Metastases in Breast Cancer and Its Effect on Treatment and Survival

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

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing