10 research outputs found

    Screening for extranodal extension in HPV-associated oropharyngeal carcinoma: Evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial

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    Background: Pretreatment identification of pathological extranodal extension (ENE) would guide therapy de-escalation strategies for in human papillomavirus (HPV)-associated oropharyngeal carcinoma but is diagnostically challenging. ECOG-ACRIN Cancer Research Group E3311 was a multicentre trial wherein patients with HPV-associated oropharyngeal carcinoma were treated surgically and assigned to a pathological risk-based adjuvant strategy of observation, radiation, or concurrent chemoradiation. Despite protocol exclusion of patients with overt radiographic ENE, more than 30% had pathological ENE and required postoperative chemoradiation. We aimed to evaluate a CT-based deep learning algorithm for prediction of ENE in E3311, a diagnostically challenging cohort wherein algorithm use would be impactful in guiding decision-making. Methods: For this retrospective evaluation of deep learning algorithm performance, we obtained pretreatment CTs and corresponding surgical pathology reports from the multicentre, randomised de-escalation trial E3311. All enrolled patients on E3311 required pretreatment and diagnostic head and neck imaging; patients with radiographically overt ENE were excluded per study protocol. The lymph node with largest short-axis diameter and up to two additional nodes were segmented on each scan and annotated for ENE per pathology reports. Deep learning algorithm performance for ENE prediction was compared with four board-certified head and neck radiologists. The primary endpoint was the area under the curve (AUC) of the receiver operating characteristic. Findings: From 178 collected scans, 313 nodes were annotated: 71 (23%) with ENE in general, 39 (13%) with ENE larger than 1 mm ENE. The deep learning algorithm AUC for ENE classification was 0·86 (95% CI 0·82–0·90), outperforming all readers (p\u3c0·0001 for each). Among radiologists, there was high variability in specificity (43–86%) and sensitivity (45–96%) with poor inter-reader agreement (Îș 0·32). Matching the algorithm specificity to that of the reader with highest AUC (R2, false positive rate 22%) yielded improved sensitivity to 75% (+13%). Setting the algorithm false positive rate to 30% yielded 90% sensitivity. The algorithm showed improved performance compared with radiologists for ENE larger than 1 mm (p\u3e\u3c0·0001) and in nodes with short-axis diameter 1 cm or larger. Interpretation The deep learning algorithm outperformed experts in predicting pathological ENE on a challenging cohort of patients with HPV-associated oropharyngeal carcinoma from a randomised clinical trial. Deep learning algorithms should be evaluated prospectively as a treatment selection tool. Funding ECOG-ACRIN Cancer Research Group and the National Cancer Institute of the US National Institutes of Health.\u3e\u3c0·0001 for each). Among radiologists, there was high variability in specificity (43–86%) and sensitivity (45–96%) with poor inter-reader agreement (Îș 0·32). Matching the algorithm specificity to that of the reader with highest AUC (R2, false positive rate 22%) yielded improved sensitivity to 75% (+13%). Setting the algorithm false positive rate to 30% yielded 90% sensitivity. The algorithm showed improved performance compared with radiologists for ENE larger than 1 mm (p\u3c0·0001) and in nodes with short-axis diameter 1 cm or larger. Interpretation The deep learning algorithm outperformed experts in predicting pathological ENE on a challenging cohort of patients with HPV-associated oropharyngeal carcinoma from a randomised clinical trial. Deep learning algorithms should be evaluated prospectively as a treatment selection tool. Funding ECOG-ACRIN Cancer Research Group and the National Cancer Institute of the US National Institutes of Health.\u3e\u3c0·0001) and in nodes with short-axis diameter 1 cm or larger. Interpretation: The deep learning algorithm outperformed experts in predicting pathological ENE on a challenging cohort of patients with HPV-associated oropharyngeal carcinoma from a randomised clinical trial. Deep learning algorithms should be evaluated prospectively as a treatment selection tool. Funding: ECOG-ACRIN Cancer Research Group and the National Cancer Institute of the US National Institutes of Health

    Screening for extranodal extension in HPV-associated oropharyngeal carcinoma:evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial

    No full text
    Background: Pretreatment identification of pathological extranodal extension (ENE) would guide therapy de-escalation strategies for in human papillomavirus (HPV)-associated oropharyngeal carcinoma but is diagnostically challenging. ECOG-ACRIN Cancer Research Group E3311 was a multicentre trial wherein patients with HPV-associated oropharyngeal carcinoma were treated surgically and assigned to a pathological risk-based adjuvant strategy of observation, radiation, or concurrent chemoradiation. Despite protocol exclusion of patients with overt radiographic ENE, more than 30% had pathological ENE and required postoperative chemoradiation. We aimed to evaluate a CT-based deep learning algorithm for prediction of ENE in E3311, a diagnostically challenging cohort wherein algorithm use would be impactful in guiding decision-making. Methods: For this retrospective evaluation of deep learning algorithm performance, we obtained pretreatment CTs and corresponding surgical pathology reports from the multicentre, randomised de-escalation trial E3311. All enrolled patients on E3311 required pretreatment and diagnostic head and neck imaging; patients with radiographically overt ENE were excluded per study protocol. The lymph node with largest short-axis diameter and up to two additional nodes were segmented on each scan and annotated for ENE per pathology reports. Deep learning algorithm performance for ENE prediction was compared with four board-certified head and neck radiologists. The primary endpoint was the area under the curve (AUC) of the receiver operating characteristic. Findings: From 178 collected scans, 313 nodes were annotated: 71 (23%) with ENE in general, 39 (13%) with ENE larger than 1 mm ENE. The deep learning algorithm AUC for ENE classification was 0·86 (95% CI 0·82–0·90), outperforming all readers (p<0·0001 for each). Among radiologists, there was high variability in specificity (43–86%) and sensitivity (45–96%) with poor inter-reader agreement (? 0·32). Matching the algorithm specificity to that of the reader with highest AUC (R2, false positive rate 22%) yielded improved sensitivity to 75% (+ 13%). Setting the algorithm false positive rate to 30% yielded 90% sensitivity. The algorithm showed improved performance compared with radiologists for ENE larger than 1 mm (p<0·0001) and in nodes with short-axis diameter 1 cm or larger. Interpretation: The deep learning algorithm outperformed experts in predicting pathological ENE on a challenging cohort of patients with HPV-associated oropharyngeal carcinoma from a randomised clinical trial. Deep learning algorithms should be evaluated prospectively as a treatment selection tool. Funding: ECOG-ACRIN Cancer Research Group and the National Cancer Institute of the US National Institutes of Health

    Prediction of life-threatening and disabling bleeding in patients with AML receiving intensive induction chemotherapy

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    Bleeding in patients with acute myeloid leukemia (AML) receiving intensive induction chemotherapy is multifactorial and contributes to early death. We sought to define the incidence and risk factors of grade 4 bleeding to support strategies for risk mitigation. Bleeding events were retrospectively assessed between day-14 and day 160 of induction treatment according to the World Health Organization (WHO) bleeding assessment scale, which includes grade 4 bleeding as fatal, life-threatening, retinal with visual impairment, or involving the central nervous system. Predictors were considered pretreatment or prior to grade 4 bleeding. Using multivariable competing-risk regression analysis with grade 4 bleeding as the primary outcome, we identified risk factors in the development cohort (n=341), which were tested in an independent cohort (n=143). Grade 4 bleeding occurred in 5.9% and 9.8% of patients in the development and validation cohort, respectively. Risk factors that were independently associated with grade 4 bleeding included baseline platelet count #40x109/L compared with .40x109/L, and baseline international normalized ratio of prothrombin time (PT-INR) .1.5 or 1.3 . 1.5 compared with #1.3. These variables were allocated points, which allowed for stratification of patients with low- and high-risk for grade 4 bleeding. Cumulative incidence of grade 4 bleeding at day160 was significantly higher among patients with high- vs low-risk (development: 31±7% vs 2±1%; P<.001; validation: 25±9% vs 7±2%; P=.008). In both cohorts, high bleeding risk was associated with disseminated intravascular coagulation (DIC) and proliferative disease. We developed and validated a simple risk model for grade 4 bleeding, which enables the development of rational risk mitigation strategies to improve early mortality of intensive induction treatment

    A dominant-negative effect drives selection of TP53 missense mutations in myeloid malignancies

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    TP53, which encodes the tumor suppressor p53, is the most frequently mutated gene in human cancer. The selective pressures shaping its mutational spectrum, dominated by missense mutations, are enigmatic, and neomorphic gain-of-function (GOF) activities have been implicated. We used CRISPR-Cas9 to generate isogenic human leukemia cell lines of the most common TP53 missense mutations. Functional, DNA-binding, and transcriptional analyses revealed loss of function but no GOF effects. Comprehensive mutational scanning of p53 single-amino acid variants demonstrated that missense variants in the DNA-binding domain exert a dominant-negative effect (DNE). In mice, the DNE of p53 missense variants confers a selective advantage to hematopoietic cells on DNA damage. Analysis of clinical outcomes in patients with acute myeloid leukemia showed no evidence of GOF for TP53 missense mutations. Thus, a DNE is the primary unit of selection for TP53 missense mutations in myeloid malignancies

    A novel surgeon credentialing and quality assurance process using transoral surgery for oropharyngeal cancer in ECOG-ACRIN Cancer Research Group Trial E3311.

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    PURPOSE: Understanding the role of transoral surgery in oropharyngeal cancer (OPC) requires prospective, randomized multi-institutional data. Meticulous evaluation of surgeon expertise and surgical quality assurance (QA) will be critical to the validity of such trials. We describe a novel surgeon credentialing and QA process developed to support the ECOG-ACRIN Cancer Research Group E3311 (E3311) and report outcomes related to QA. PATIENTS AND METHODS: E3311 was a phase II randomized clinical trial of transoral surgery followed by low- or standard-dose, risk-adjusted post-operative therapy with stage III-IVa (AJCC 7th edition) HPV-associated OPC. In order to be credentialed to accrue to this trial, surgeons were required to demonstrate active hospital credentials and technique-specific surgical expertise with ≄20 cases of transoral resection for OPC. In addition, 10 paired operative and surgical pathology reports from the preceding 24 months were reviewed by an expert panel. Ongoing QA required RESULTS: 120 surgeons trained in transoral minimally invasive surgery applied for credentialing for E3311 and after peer-review, 87 (73%) were approved from 59 centers. During QA on E3311, positive final pathologic margins were reported in 19 (3.8%) patients. Grade III/IV and grade V oropharyngeal bleeding was reported in 29 (5.9%) and 1 (0.2%) of patients. CONCLUSIONS: We provide proof of concept that a comprehensive credentialing process can support multicenter transoral head and neck surgical oncology trials, with low incidence of positive margins and *grade III/V oropharyngeal bleeding
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