Best of ASCO - 2014 Annual Meeting



Attend this session at the
2019 ASCO Annual Meeting!

Session: Lung Cancer—Non-Small Cell Metastatic

Type: Poster Session

Time: Sunday June 2, 8:00 AM to 11:00 AM

Location: Hall A

ml-RECIST: Machine learning to estimate RECIST in patients with NSCLC treated with PD-(L)1 blockade.

Metastatic Non-Small Cell Lung Cancer

Lung Cancer—Non-Small Cell Metastatic

2019 ASCO Annual Meeting

Abstract No:

Poster Board Number:
Poster Session (Board #375)

J Clin Oncol 37, 2019 (suppl; abstr 9052)

Author(s): Kathryn Cecilia Arbour, Luu Anh Tuan, Hira Rizvi, Adam Yala, Matthew David Hellmann, Regina Barzilay; Memorial Sloan Kettering Cancer Center, New York, NY; Massachusetts Institute of Technology, Cambridge, MA; Memorial Sloan Kettering Cancer Center and Weill Cornell Medical College, New York, NY

Abstract Disclosures


Background: Real-world evidence (RWE) is increasingly important for discovery and may be an opportunity for regulatory approval. Effective use of RWE relies on determining treatment-specific outcomes, such as overall response rate (ORR) and progression-free survival (PFS), which are challenging to accurately evaluate retrospectively and at scale. We hypothesized the use of machine learning of text radiology reports from patients with NSCLC treated with PD-1 blockade could be used to train a model that estimates RECIST-defined outcomes. Methods: 2753 imaging reports from 453 patients with advanced NSCLC treated with PD-1 blockade were collected and separated into independent training (80%, n = 362) and validation (20%, n = 92) cohorts. Reports were limited to interval of PD-1 blockade. RECIST reads performed by thoracic radiologists on all patients served as “gold standard” to determine ORR, occurrence of, and date of progression. Baseline reports were compared to all follow up reports to determine machine-learning RECIST (ml-RECIST). A four layers neural-network model for classification was proposed to solve the three above tasks. Results: In the training cohort, ml-RECIST best estimated ORR by RECIST (accuracy CR/PR 84%, SD 82%, POD 91%). ml-RECIST estimated PFS by RECIST accurately predicting progression occurred at any time (86%) and exact progression date (65%). Date of progression was closely correlated (Pearson’s r coefficient = 0.91, 95% CI:0.89-0.94, p < 0.001) in patients determined to have progressed by both methods. Similar accuracy of ml-RECIST was observed in the validation cohort (accuracy CR/PR 84%, SD 80%, POD 90%; progression occurred 86%, progression date 72%). Accuracy was consistent when RECIST reads were performed prospectively as part of clinical trials vs retrospectively for standard of care treatment (e.g. CR/PR 82% vs 88%, respectively). ml-RECIST-defined response similarly determined improvement in overall survival compared to RECIST (HR = 0.19, p < 0.001 vs HR = 0.26, p < 0.001 respectively). Conclusions: Machine learning-RECIST ("ml-RECIST") accurately estimates outcomes using imaging text reports. ml-RECIST may be tool to determine outcomes expeditiously and at scale for use in RWE studies, enabling more useful and reliable applications of large clinical databases.

Other Abstracts in this Sub-Category:


1. Association of STK11/LKB1 genomic alterations with lack of benefit from the addition of pembrolizumab to platinum doublet chemotherapy in non-squamous non-small cell lung cancer.

Meeting: 2019 ASCO Annual Meeting Abstract No: 102 First Author: Ferdinandos Skoulidis
Category: Lung Cancer—Non-Small Cell Metastatic - Metastatic Non-Small Cell Lung Cancer


2. Real-world outcomes of patients with advanced non-small cell lung cancer (aNSCLC) and autoimmune disease (AD) receiving immune checkpoint inhibitors (ICIs).

Meeting: 2019 ASCO Annual Meeting Abstract No: 110 First Author: Sean Khozin
Category: Lung Cancer—Non-Small Cell Metastatic - Metastatic Non-Small Cell Lung Cancer


3. RELAY: A multinational, double-blind, randomized Phase 3 study of erlotinib (ERL) in combination with ramucirumab (RAM) or placebo (PL) in previously untreated patients with epidermal growth factor receptor mutation-positive (EGFRm) metastatic non-small cell lung cancer (NSCLC).

Meeting: 2019 ASCO Annual Meeting Abstract No: 9000 First Author: Kazuhiko Nakagawa
Category: Lung Cancer—Non-Small Cell Metastatic - Metastatic Non-Small Cell Lung Cancer