Elastofibroma Dorsi: Case Series of a Rare Chest Wall Pseudotumor.

BACKGROUND: Elastofibroma dorsi (EFD) is a pseudotumor of the thoracic wall that can be difficult to diagnose due to its rarity. Prompt recognition can limit unnecessary workup and expedite treatment. This study retrospectively analyzed patients with a diagnosis of EFD, discussing clinical presentations and surgical outcomes. METHODS: This is an IRB-approved single-center retrospective study of all patients with a diagnosis of elastofibroma at our institution between 2000 and 2022. RESULTS: Ten patients were identified to have a pathologic diagnosis of EFD since 2000, with half presenting in the last 5 years. Our cohort had an average age of 56.8 years and was 50% female. The average age of male subjects was younger than females, 49.6-64.0 years, respectively (p = 0.10). Eighty percent (8/10) of patients had unilateral EFDs and symptoms lasted 27.1 months on average prior to diagnosis. Surgical resection was performed on 66.67% (8/12) of masses, with 87.5% (7/8) of patients who underwent surgery reporting complete resolution of their symptoms and none reporting recurrence. CONCLUSIONS: Although EFD is a rare pseudotumor, its incidence may be increasing. As such, surgeons should be aware of the typical clinical presentation; specifically, a slow growing, predominantly unilateral, painful, subscapular mass with an inhomogeneous pattern on imaging. Originally thought to predominantly affect elderly women, our study shows that younger men may be at risk as well. If patients present with EFD, complete surgical resection should be performed to achieve favorable outcomes and resolution of symptoms.

Personalized Antibodies for Gastroesophageal Adenocarcinoma (PANGEA): A Phase II Study Evaluating an Individualized Treatment Strategy for Metastatic Disease.

The one-year and median overall survival (mOS) rates of advanced gastroesophageal adenocarcinomas (GEA) are ∼50% and <12 months, respectively. Baseline spatial and temporal molecular heterogeneity of targetable alterations may be a cause of failure of targeted/immunooncologic therapies. This heterogeneity, coupled with infrequent incidence of some biomarkers, has resulted in stalled therapeutic progress. We hypothesized that a personalized treatment strategy, applied at first diagnosis then serially over up to three treatment lines using monoclonal antibodies combined with optimally sequenced chemotherapy, could contend with these hurdles. This was tested using a novel clinical expansion-platform type II design with a survival primary endpoint. Of 68 patients by intention-to-treat, the one-year survival rate was 66% and mOS was 15.7 months, meeting the primary efficacy endpoint (one-sided P = 0.0024). First-line response rate (74%), disease control rate (99%), and median progression-free survival (8.2 months) were superior to historical controls. The PANGEA strategy led to improved outcomes warranting a larger randomized study. SIGNIFICANCE: This study highlights excellent outcomes achieved by individually optimizing chemotherapy, biomarker profiling, and matching of targeted therapies at baseline and over time for GEA. Testing a predefined treatment strategy resulted in improved outcomes versus historical controls. Therapeutic resistance observed in correlative analyses suggests that dual targeted inhibition may be beneficial.This article is highlighted in the In This Issue feature, p. 211.

Obtaining Knowledge in Pathology Reports Through a Natural Language Processing Approach With Classification, Named-Entity Recognition, and Relation-Extraction Heuristics.

PURPOSE: Robust institutional tumor banks depend on continuous sample curation or else subsequent biopsy or resection specimens are overlooked after initial enrollment. Curation automation is hindered by semistructured free-text clinical pathology notes, which complicate data abstraction. Our motivation is to develop a natural language processing method that dynamically identifies existing pathology specimen elements necessary for locating specimens for future use in a manner that can be re-implemented by other institutions. PATIENTS AND METHODS: Pathology reports from patients with gastroesophageal cancer enrolled in The University of Chicago GI oncology tumor bank were used to train and validate a novel composite natural language processing-based pipeline with a supervised machine learning classification step to separate notes into internal (primary review) and external (consultation) reports; a named-entity recognition step to obtain label (accession number), location, date, and sublabels (block identifiers); and a results proofreading step. RESULTS: We analyzed 188 pathology reports, including 82 internal reports and 106 external consult reports, and successfully extracted named entities grouped as sample information (label, date, location). Our approach identified up to 24 additional unique samples in external consult notes that could have been overlooked. Our classification model obtained 100% accuracy on the basis of 10-fold cross-validation. Precision, recall, and F1 for class-specific named-entity recognition models show strong performance. CONCLUSION: Through a combination of natural language processing and machine learning, we devised a re-implementable and automated approach that can accurately extract specimen attributes from semistructured pathology notes to dynamically populate a tumor registry.

Circulating Tumor DNA Sequencing Analysis of Gastroesophageal Adenocarcinoma.

PURPOSE: Gastroesophageal adenocarcinoma (GEA) has a poor prognosis and few therapeutic options. Utilizing a 73-gene plasma-based next-generation sequencing (NGS) cell-free circulating tumor DNA (ctDNA-NGS) test, we sought to evaluate the role of ctDNA-NGS in guiding clinical decision-making in GEA. EXPERIMENTAL DESIGN: We evaluated a large cohort (n = 2,140 tests; 1,630 patients) of ctDNA-NGS results (including 369 clinically annotated patients). Patients were assessed for genomic alteration (GA) distribution and correlation with clinicopathologic characteristics and outcomes. RESULTS: Treatment history, tumor site, and disease burden dictated tumor-DNA shedding and consequent ctDNA-NGS maximum somatic variant allele frequency. Patients with locally advanced disease having detectable ctDNA postoperatively experienced inferior median disease-free survival (P = 0.03). The genomic landscape was similar but not identical to tissue-NGS, reflecting temporospatial molecular heterogeneity, with some targetable GAs identified at higher frequency via ctDNA-NGS compared with previous primary tumor-NGS cohorts. Patients with known microsatellite instability-high (MSI-High) tumors were robustly detected with ctDNA-NGS. Predictive biomarker assessment was optimized by incorporating tissue-NGS and ctDNA-NGS assessment in a complementary manner. HER2 inhibition demonstrated a profound survival benefit in HER2-amplified patients by ctDNA-NGS and/or tissue-NGS (median overall survival, 26.3 vs. 7.4 months; P = 0.002), as did EGFR inhibition in EGFR-amplified patients (median overall survival, 21.1 vs. 14.4 months; P = 0.01). CONCLUSIONS: ctDNA-NGS characterized GEA molecular heterogeneity and rendered important prognostic and predictive information, complementary to tissue-NGS.See related commentary by Frankell and Smyth, p. 6893.