scholarly journals Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lin Lu ◽  
Laurent Dercle ◽  
Binsheng Zhao ◽  
Lawrence H. Schwartz
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Metastatic Colorectal Cancer ◽  
Treatment Response ◽  
Morphological Change ◽  
Tumor Size ◽  
Tumor Response ◽  
Treatment Decision ◽  
Response Assessment ◽  
Size Change

AbstractIn current clinical practice, tumor response assessment is usually based on tumor size change on serial computerized tomography (CT) scan images. However, evaluation of tumor response to anti-vascular endothelial growth factor therapies in metastatic colorectal cancer (mCRC) is limited because morphological change in tumor may occur earlier than tumor size change. Here we present an analysis utilizing a deep learning (DL) network to characterize tumor morphological change for response assessment in mCRC patients. We retrospectively analyzed 1,028 mCRC patients who were prospectively included in the VELOUR trial (NCT00561470). We found that DL network was able to predict early on-treatment response in mCRC and showed better performance than its size-based counterpart with C-Index: 0.649 (95% CI: 0.619,0.679) vs. 0.627 (95% CI: 0.567,0.638), p = 0.009, z-test. The integration of DL network with size-based methodology could further improve the prediction performance to C-Index: 0.694 (95% CI: 0.661,0.720), which was superior to size/DL-based-only models (all p < 0.001, z-test). Our study suggests that DL network could provide a noninvasive mean for quantitative and comprehensive characterization of tumor morphological change, which may potentially benefit personalized early on-treatment decision making.

Comparison of tumor response assessment methods in patients with metastatic colorectal cancer after locoregional therapy

2016 ◽  
Vol 113 (4) ◽  
pp. 443-448 ◽  
Author(s):  
Olaguoke Akinwande ◽  
Prejesh Philips ◽  
Charles R. Scoggins ◽  
Lawrence Kelly ◽  
Cliff Tatum ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Metastatic Colorectal Cancer ◽  
Tumor Response ◽  
Response Assessment ◽  
Assessment Methods ◽  
Locoregional Therapy

Urinary measurement of circulating tumor DNA for treatment monitoring and prognosis of metastatic colorectal cancer patients

2018 ◽  
Vol 57 (2) ◽  
pp. 268-275 ◽  
Author(s):  
Tao Song ◽  
Fei Mao ◽  
Li Shi ◽  
Xuemei Xu ◽  
Zirong Wu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Metastatic Colorectal Cancer ◽  
Treatment Response ◽  
Clinical Relevance ◽  
Growth Factor Receptor ◽  
Circulating Tumor Dna ◽  
Treatment Monitoring ◽  
Total Dna ◽  
Urine Specimens ◽  
Good Agreement

Abstract Background Solid tumor tissue testing is the gold standard for molecular-based assays for metastatic colorectal cancer (mCRC). This poses challenges during treatment monitoring. Total DNA derived from urine specimens offers clear advantages to track the disease dynamics. Our study aims to evaluate the sensitivity for total DNA recovered from urine and its clinical relevance to mCRC. Methods KRAS mutations in urine specimens were examined in 150 mCRC patients. Baseline concordance was established to determined clinical relevance. The total DNA quantities were also prospectively examined in serial samplings during treatment. Results Analysis of the genetic mutations showed good agreement for baseline samples. Matched tumor and urine specimens’ molecular profiles were observed to have 90% concordance. Comparing with healthy volunteers, we established a cutoff of 8.15 ng that demonstrated elevated total DNA levels was associated with mCRC patients (sensitivity: 90.7%; specificity: 82.0%). For patients treated with chemotherapy or anti-epidermal growth factor receptor inhibitors, DNA quantity mirrored early treatment response. Survival analysis showed that patients with sustained elevated quantities of KRAS mutations had poorer outcome. Conclusions Total urine DNA offers a viable complement for mutation profiling in mCRC patients, given the good agreement with matched tumor samples. Our study also established that this is specific based on the results from healthy individuals. Serial monitoring of total DNA levels allowed early prediction to treatment response and was effective to identify high risk patients. This is potentially useful to complement current disease management.

Methylated DNA markers for monitoring palliative chemotherapy response in advanced colorectal cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e15511-e15511
Author(s):  
Mojun Zhu ◽  
Douglas W. Mahoney ◽  
Kelli Burger ◽  
Patrick H. Foote ◽  
Karen A. Doering ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Treatment Response ◽  
Systemic Therapy ◽  
Small Sample Size ◽  
High Sensitivity ◽  
Response Assessment ◽  
Small Sample ◽  
Chemotherapy Response ◽  
Continuous Variables ◽  
Methylated Dna

e15511 Background: Aberrantly methylated DNA marker (MDM) candidates are strongly associated with primary colorectal cancer (CRC) before treatment and detect CRC recurrence with high sensitivity when assayed from plasma. The relationship of these MDMs in association to chemotherapy treatment response is unknown. Methods: In a prospective cohort of patients receiving systemic therapy for advanced CRC, peripheral blood was collected serially during restaging visits. 15 patients were retrospectively identified to have partial response (PR), stable disease (SD) and progressive disease (PD) to treatment (n=5 for each group) based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Using paired samples from each patient before and after response assessment, we analyzed 11 MDMs ( GRIN2D, ZNF671, ANKRD13B, QKI, VAV3, JAM3, SFMBT2, CHST2, ZNF568, FER1L4 and CNNM1) to assess correlation with treatment response. Cell-free DNA was extracted and bisulfite treated before MDMs were quantified by target enrichment long-probe quantitative-amplified signal assay and normalized to a methylated sequence of B3GALT6. Continuous variables are summarized as a median with corresponding interquartile ranges (IQR) and comparisons between subgroups were based on the Wilcox Rank Sums test. Results: The median interval between pre- and post-response assessment visits was 69 days (IQR: 63-83 days) and the level of tumor burden at pre-assessment was similar across all response types (Table 1). Patients with PD had higher levels of methylated GRIN2D, ZNF671 and ANKRD13B than those with PR or SD at baseline and may offer additional prognostic value over CEA which was similar in the PR and PD groups before treatment (Table 1). Elevation of pre-assessment MDMs preceded radiographic evidence of disease progression by 82 days (IQR 69-83 days). Conclusions: Three MDMs, GRIN2D, ZNF671 and ANKRD13B, were found to reflect treatment response (PD vs. PR + SD) as shown in the table. Although this pilot study was limited by a small sample size, it demonstrated the feasibility of using plasma-based MDMs in monitoring treatment response to systemic therapy for advanced CRC and should be compared to CEA in a larger study.[Table: see text]

scholarly journals Evaluation of Continuous Tumor-Size–Based End Points as Surrogates for Overall Survival in Randomized Clinical Trials in Metastatic Colorectal Cancer

2019 ◽  
Vol 2 (9) ◽  
pp. e1911750
Author(s):  
Tomasz Burzykowski ◽  
Elisabeth Coart ◽  
Everardo D. Saad ◽  
Qian Shi ◽  
Dirkje W. Sommeijer ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Clinical Trials ◽  
Metastatic Colorectal Cancer ◽  
Tumor Size ◽  
Randomized Clinical Trials ◽  
End Points

scholarly journals Variability in Assessing Treatment Response: Metastatic Colorectal Cancer as a Paradigm

2014 ◽  
Vol 20 (13) ◽  
pp. 3560-3568 ◽  
Author(s):  
Binsheng Zhao ◽  
Shing M. Lee ◽  
Hyun-Ju Lee ◽  
Yongqiang Tan ◽  
Jing Qi ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Metastatic Colorectal Cancer ◽  
Treatment Response

Bladder cancer treatment response assessment using deep learning in CT with transfer learning

2017 ◽  
Author(s):  
Kenny H. Cha ◽  
Lubomir M. Hadjiiski ◽  
Heang-Ping Chan ◽  
Ravi K. Samala ◽  
Richard H. Cohan ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Deep Learning ◽  
Cancer Treatment ◽  
Treatment Response ◽  
Transfer Learning ◽  
Response Assessment ◽  
Treatment Response Assessment
Sign in / Sign up

Export Citation Format

Share Document