Evaluation of disease staging and chemotherapeutic response in non-small cell lung cancer from patient tumor-derived metabolomic data

Miller, Hunter A.; Yin, Xinmin; Smith, Susan; Hu, Xiaoling; Zhang, Xiang; Yan, Jun; Miller, Donald M.; Berkel, Victor H. van; Frieboes, Hermann B.

doi:10.1016/j.lungcan.2021.04.012

Cited by 29 publications

(19 citation statements)

References 56 publications

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“…The collaborative design of the APF-bowl&0.96Au submicroreactor was also critical to extracting the SMFs efficiently, enabling the revelation of metabolic differences during chemotherapy. In addition, the sample size of this study (243 samples) was comparable with that of previous literature (∼30–200 samples). ,, …”

Section: Resultssupporting

confidence: 81%

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Serum Metabolic Fingerprints on Bowl-Shaped Submicroreactor Chip for Chemotherapy Monitoring

et al. 2022

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Chemotherapy is a primary cancer treatment strategy, the monitoring of which is critical to enhancing the survival rate and quality of life of cancer patients. However, current chemotherapy monitoring mainly relies on imaging tools with inefficient sensitivity and radiation invasiveness. Herein, we develop the bowl-shaped submicroreactor chip of Au-loaded 3-aminophenol formaldehyde resin (denoted as APF-bowl&Au) with a specifically designed structure and Au loading content. The obtained APF-bowl&Au, used as the matrix of laser desorption/ionization mass spectrometry (LDI MS), possesses an enhanced localized electromagnetic field for strengthened small metabolite detection. The APF-bowl&Au enables the extraction of serum metabolic fingerprints (SMFs), and machine learning of the SMFs achieves chemotherapy monitoring of ovarian cancer with area-under-the-curve (AUC) of 0.81–0.98. Furthermore, a serum metabolic biomarker panel is preliminarily identified, exhibiting gradual changes as the chemotherapy cycles proceed. This work provides insights into the development of nanochips and contributes to a universal detection platform for chemotherapy monitoring.

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Section: Resultssupporting

confidence: 81%

“…In addition, the sample size of this study (243 samples) was comparable with that of previous literature (∼30–200 samples). 9 , 50 , 51 …”

Section: Resultsmentioning

confidence: 99%

Serum Metabolic Fingerprints on Bowl-Shaped Submicroreactor Chip for Chemotherapy Monitoring

et al. 2022

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“…Because the majority misjudgment carries a bigger weight than the minority, miss judgment is more likely to occur for the majority than for the minority. Classification algorithms that rely on traditional methods of doing things do not perform as well as they could [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Lung Cancer Prediction from Text Datasets Using Machine Learning

Kumar¹,

Harish²,

Ravi

et al. 2022

BioMed Research International

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Lung cancer is the major cause of cancer-related death in this generation, and it is expected to remain so for the foreseeable future. It is feasible to treat lung cancer if the symptoms of the disease are detected early. It is possible to construct a sustainable prototype model for the treatment of lung cancer using the current developments in computational intelligence without negatively impacting the environment. Because it will reduce the number of resources squandered as well as the amount of work necessary to complete manual tasks, it will save both time and money. To optimise the process of detection from the lung cancer dataset, a machine learning model based on support vector machines (SVMs) was used. Using an SVM classifier, lung cancer patients are classified based on their symptoms at the same time as the Python programming language is utilised to further the model implementation. The effectiveness of our SVM model was evaluated in terms of several different criteria. Several cancer datasets from the University of California, Irvine, library were utilised to evaluate the evaluated model. As a result of the favourable findings of this research, smart cities will be able to deliver better healthcare to their citizens. Patients with lung cancer can obtain real-time treatment in a cost-effective manner with the least amount of effort and latency from any location and at any time. The proposed model was compared with the existing SVM and SMOTE methods. The proposed method gets a 98.8% of accuracy rate when comparing the existing methods.

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“…Any measurements still missing were handled similar to baseline data. The pre-specified endpoint for this study was progression to ≥CPG3 disease which we have previously reported as a clinically meaningful exit criterion and consistent with NICE recommendations 10 . Progression to CPG3 + is possible through a rise in PSA, change in histological Grade Group (e.g., an increase from Grade Group 2 to Grade Group 3) or change in stage of tumour (e.g., T2 to T3) as shown in Supplementary Fig.…”

Section: Methodsmentioning

confidence: 62%

Developing machine learning algorithms for dynamic estimation of progression during active surveillance for prostate cancer

et al. 2022

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Active Surveillance (AS) for prostate cancer is a management option that continually monitors early disease and considers intervention if progression occurs. A robust method to incorporate “live” updates of progression risk during follow-up has hitherto been lacking. To address this, we developed a deep learning-based individualised longitudinal survival model using Dynamic-DeepHit-Lite (DDHL) that learns data-driven distribution of time-to-event outcomes. Further refining outputs, we used a reinforcement learning approach (Actor-Critic) for temporal predictive clustering (AC-TPC) to discover groups with similar time-to-event outcomes to support clinical utility. We applied these methods to data from 585 men on AS with longitudinal and comprehensive follow-up (median 4.4 years). Time-dependent C-indices and Brier scores were calculated and compared to Cox regression and landmarking methods. Both Cox and DDHL models including only baseline variables showed comparable C-indices but the DDHL model performance improved with additional follow-up data. With 3 years of data collection and 3 years follow-up the DDHL model had a C-index of 0.79 (±0.11) compared to 0.70 (±0.15) for landmarking Cox and 0.67 (±0.09) for baseline Cox only. Model calibration was good across all models tested. The AC-TPC method further discovered 4 distinct outcome-related temporal clusters with distinct progression trajectories. Those in the lowest risk cluster had negligible progression risk while those in the highest cluster had a 50% risk of progression by 5 years. In summary, we report a novel machine learning approach to inform personalised follow-up during active surveillance which improves predictive power with increasing data input over time.

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Evaluation of disease staging and chemotherapeutic response in non-small cell lung cancer from patient tumor-derived metabolomic data

Cited by 29 publications

References 56 publications

Serum Metabolic Fingerprints on Bowl-Shaped Submicroreactor Chip for Chemotherapy Monitoring

Serum Metabolic Fingerprints on Bowl-Shaped Submicroreactor Chip for Chemotherapy Monitoring

Lung Cancer Prediction from Text Datasets Using Machine Learning

Developing machine learning algorithms for dynamic estimation of progression during active surveillance for prostate cancer

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