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5 results on '"MATTHEW HUEMAN"'

1. Expanding TNM for lung cancer through machine learning

Author

Matthew Hueman, Huan Wang, Zhenqiu Liu, Donald Henson, Cuong Nguyen, Dean Park, Li Sheng, and Dechang Chen

Subjects
C‐index, lung cancer, machine learning, staging, survival, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background Expanding the tumor, lymph node, metastasis (TNM) staging system by accommodating new prognostic and predictive factors for cancer will improve patient stratification and survival prediction. Here, we introduce machine learning for incorporating additional prognostic factors into the conventional TNM for stratifying patients with lung cancer and evaluating survival. Methods Data were extracted from SEER. A total of 77 953 patients were analyzed using factors including primary tumor (T), regional lymph node (N), distant metastasis (M), age, and histology type. Ensemble algorithm for clustering cancer data (EACCD) and C‐index were applied to generate prognostic groups and expand the current staging system. Results With T, N, and M, EACCD stratified patients into 11 groups, resulting in a significantly higher accuracy in survival prediction than the 10 AJCC stages (C‐index = 0.7346 vs. 0.7247, increase in C‐index = 0.0099, 95% CI: 0.0091–0.0106, p‐value = 9.2 × 10−147). There nevertheless remained a strong association between the EACCD grouping and AJCC staging (rank correlation = 0.9289; p‐value = 6.7 × 10−22). A further analysis demonstrated that age and histological tumor could be integrated with the TNM. Data were stratified into 12 prognostic groups with an even higher prediction accuracy (C‐index = 0.7468 vs. 0.7247, increase in C‐index = 0.0221, 95% CI: 0.0212–0.0231, p‐value

Published
2021
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2. Expanding the TNM for cancers of the colon and rectum using machine learning: a demonstration

Author

Huan Wang, Matthew Hueman, Donald Henson, and Dechang Chen

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Objective The American Joint Committee on Cancer (AJCC) system for staging cancers of the colon and rectum includes depth of tumour penetration, number of positive lymph nodes and presence or absence of metastasis. Using machine learning, we demonstrate that these factors can be integrated with age, carcinoembryonic antigen (CEA) interpretation and tumour location, to form prognostic systems that expand the tumour, lymph node, metastasis (TNM) staging system.Methods Two datasets on colon and rectal cancers were extracted from the Surveillance, Epidemiology and End Results Programme of the National Cancer Institute. Dataset 1 included three factors (tumour, lymph nodes and metastasis). Dataset 2 contained six factors (tumour, lymph nodes, metastasis, age, CEA interpretation and tumour location). The Ensemble Algorithm for Clustering Cancer Data (EACCD) and the C-index were applied to generate prognostic groups.Results The EACCD prognostic system based on dataset 1 stratified patients into 10 risk groups, analogous to the 10 stages of the AJCC staging system. There was a strong inter-system association between EACCD grouping and AJCC staging (Spearman’s rank correlation=0.9046, p value=1.6×10−17). However, the EACCD system had a significantly higher survival prediction accuracy than the AJCC system (C-index=0.7802 and 0.7695, respectively for the EACCD system and AJCC system, p value=4.9×10−91). Adding age, or CEA interpretation, or location improved the prediction accuracy of the prognostic system-involving tumour, lymph nodes and metastasis. The EACCD prognostic system based on dataset 2 and all six factors stratified patients into 10 groups with the highest survival prediction accuracy (C-index=0.7914).Conclusions The EACCD can integrate multiple factors to stratify patients with colon or rectal cancer into risk groups that predict survival with a high accuracy.

Published
2019
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3. Enhanced toxicity to chemoradiation in a patient with Anti-Jo-1-antisynthetase syndrome

Author

Luca Valle, James Katz, Austin Duffy, Matthew Hueman, Hao-Wei Wang, Marybeth Hughes, Tristan Sissung, William Figg, and Deborah Citrin

Subjects
Rare Diseases, Clinical Research, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, General Medicine, Patient Safety, Autoimmune Disease, Cancer
Abstract

Appropriate counseling of patients with autoimmune connective tissue disorders (ACTDs) is often challenging for radiation oncologists, especially regarding anticipated side-effects of radiation treatment. These patients can have highly variable and unpredictable sequelae from radiation therapy, and the uncertainty builds when radiation is convoluted by the addition of concurrent chemotherapy. While many patients may experience a mild intensification of toxicity above what is expected, some patients experience much more severe toxicity. These patients become critical learning cases, enabling a better understanding of the delicate and complex ways in which radiation response is altered in the context of ACTDs while allowing other patients with similar ACTD profiles to benefit from past experience. Our report makes an important contribution to this space by describing a particularly severe case of toxicity that manifested in such a patient and the ensuing clinical decision-making. Comprehensive genotyping of classic pharmacokinetic and pharmacodynamic pathway genes (including mutations in DPD and CDA) did not reveal any signatures that might explain her enhanced toxicity and we demonstrate that severe toxicity can still manifest in the era of modern conformal radiation treatments for rectal cancer. We urge caution in the treatment of patients with rare ACTDs, but also emphasize that curative treatment should not be withheld in such patients. We conclude by advocating for the development and maintenance of a prospective multiinstitutional database of patients with ACTDs to help inform and improve future practice.

Published
2022

4. Circulating Cell-free DNA in Serum as a Marker for the Early Detection of Tumor Recurrence in Breast Cancer Patients

Author

ALAKESH BERA, ERIC RUSS, JOHN KARAIAN, ADAM LANDA, SURYA RADHAKRISHNAN, MADHAN SUBRAMANIAN, MATTHEW HUEMAN, HARVEY B. POLLARD, HAI HU, CRAIG D. SHRIVER, and MEERA SRIVASTAVA

Subjects
Research Article
Abstract

Background/Aim: Circulating cell-free DNA (cfDNA) isolated from serum by noninvasive procedures can serve as a potential biomarker for the early detection of many cancers. The aim of this study was to implement a simple, yet effective quantitative method for measuring the cfDNA in serum and to investigate the relationship between cfDNA and the occurrence of recurrence in breast cancer (BrCa) patients. Patients and Methods: A total of 240 cases were selected, which comprised different subtypes of BrCa patients and control individuals. We selected 20 serum samples from patients which showed recurrence after 4-7 years of disease-free survival. SYBR green was used as a reporter molecule to estimate the amount of cfDNA in these serum samples. Results: A global Wilcoxon analysis was performed to compare the cfDNA abundance between non-recurrent and recurrent patients. The amount of cfDNA was higher in recurrent patients (recurrent vs. non-recurrent ratio=1.3; p=0.03; AUC=0.76) compared to non-recurrent patients. The data between normal/healthy controls and non-recurrent patients indicated no significant differences (n=20 in each group, healthy to non-recurrent ratio=1.03; p=0.20; AUC=0.61). Conclusion: We implemented a straightforward one-step technique to measure the amount of cfDNA in serum, which can translate into a clinical diagnostic tool in the near future. The high levels of cfDNA in the serum of recurrent BrCa patients compared to non-recurrent BrCa patients indicates a possible uncovered role for circulating genetic information, which either contributes to the cancer recurrence phenomenon or at the very least, serves as an identifier for the potential of recurrence.

Published
2022

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