Measuring resilience by cognitive diagnosis models and its prediction of 6-month quality of life in Be Resilient to Breast Cancer (BRBC)

Liang, Mu Zi; Chen, Peng; Molassiotis, Alex; Tang, Ying; Hu, Guang Yun; Sun, Zhe; Yu, Yuan Liang; Ye, Zeng Jie

doi:10.3389/fpsyt.2023.1102258

Cited by 14 publications

(2 citation statements)

References 45 publications

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“…Fourth, the sample sizes of the training and validation cohorts are relatively small, and the prediction model should be validated in a larger population with different cancer diagnoses. Fifth, measurement errors were not taken into consideration to calculate the QoL outcomes, and item response theory could be tried to achieve a better estimation [47][48][49][50].…”

Section: Limitationsmentioning

confidence: 99%

Brain Connectomics Improve the Prediction of High-Risk Depression Profiles in the First Year following Breast Cancer Diagnosis

Liang,

Chen,

Tang

et al. 2024

Depression and Anxiety

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Background. Prediction of high-risk depression trajectories in the first year following breast cancer diagnosis with fMRI-related brain connectomics is unclear. Methods. The Be Resilient to Breast Cancer (BRBC) study is a multicenter trial in which 189/232 participants (81.5%) completed baseline resting-state functional magnetic resonance imaging (rs-fMRI) and four sequential assessments of depression (T0-T3). The latent growth mixture model (LGMM) was utilized to differentiate depression profiles (high vs. low risk) and was followed by multivoxel pattern analysis (MVPA) to recognize distinct brain connectivity patterns. The incremental value of brain connectomics in the prediction model was also estimated. Results. Four depression profiles were recognized and classified into high-risk (delayed and chronic, 14.8% and 12.7%) and low-risk (resilient and recovery, 50.3% and 22.2%). Frontal medial cortex and frontal pole were identified as two important brain areas against the high-risk profile outcome. The prediction model achieved 16.82-76.21% in NRI and 12.63-50.74% in IDI when brain connectomics were included. Conclusion. Brain connectomics can optimize the prediction against high-risk depression profiles in the first year since breast cancer diagnoses.

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Section: Limitationsmentioning

confidence: 99%

Brain Connectomics Improve the Prediction of High-Risk Depression Profiles in the First Year following Breast Cancer Diagnosis

Liang,

Chen,

Tang

et al. 2024

Depression and Anxiety

Self Cite

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“…In addition, CDMs are also used in diagnosing physical health. Liang et al [82] employed G-DINA, in conjunction with constructed data and a Q-matrix, to predict six-month Quality of Life (QoL) in breast cancer. The results from CDMs provide valuable references for doctors to assess examinees' health conditions and support planning the treatments.…”

Section: Applications In Other Domainsmentioning

confidence: 99%

Modern Science Education in China

Wang

2021

Western Influences in the History of Science and Technology in Modern China

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Symbiotic radio (SR) has emerged as a spectrum-and energy-efficient paradigm to support massive Internet of Things (IoT) connections. Two multiple access schemes are proposed in this paper to facilitate the massive IoT connections using the cellular network based on the SR technique, namely, the simultaneous access (SA) scheme and the selection diversity access (SDA) scheme. In the SA scheme, the base station (BS) transmits information to the receiver while multiple IoT devices transmit their information simultaneously by passively backscattering the BS signal to the receiver, while in the SDA scheme, only the IoT device with the strongest backscatter link transmits information to the receiver.In both of the schemes, the receiver jointly decodes the information from the BS and the IoT devices.To evaluate the above two schemes, in this paper, we have derived the closed-form expressions of the ergodic rates and the outage probabilities for the cellular and IoT transmissions. Finally, numerical results are provided to verify the theoretical analysis and compare the two proposed multiple access schemes. When the number of IoT devices is small, the SDA scheme is more appealing since it can significantly reduce the computational complexity while achieving equivalent performance to the SA scheme. When the number of IoT devices is large, the SA scheme is preferable since it guarantees a significantly better rate performance and a lower outage probability.

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Heterogeneity in resilience patterns and its prediction of 1-year quality of life outcomes among patients with newly diagnosed cancer: An exploratory piecewise growth mixture model analysis

Liang¹,

Liu

Tang

et al. 2023

European Journal of Oncology Nursing

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Measuring resilience by cognitive diagnosis models and its prediction of 6-month quality of life in Be Resilient to Breast Cancer (BRBC)

Cited by 14 publications

References 45 publications

Brain Connectomics Improve the Prediction of High-Risk Depression Profiles in the First Year following Breast Cancer Diagnosis

Brain Connectomics Improve the Prediction of High-Risk Depression Profiles in the First Year following Breast Cancer Diagnosis

Modern Science Education in China

Heterogeneity in resilience patterns and its prediction of 1-year quality of life outcomes among patients with newly diagnosed cancer: An exploratory piecewise growth mixture model analysis

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