Radiomics Features at Multiparametric MRI Predict Disease-Free Survival in Patients With Locally Advanced Rectal Cancer

Cui, Yanfen; Wang, Guanghui; Ren, Jialiang; Hou, Lina; Li, Dandan; Wen, Qianfa; Xi, Yanfeng; Yang, Xiaotang

doi:10.1016/j.acra.2021.11.024

Cited by 19 publications

(11 citation statements)

References 32 publications

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“…This nomogram demonstrated superior performance compared to the clinical models, as evidenced by a higher C-index and positive NRI, as well as lower AIC. These ndings are consistent with previous studies [20,23,31]. There are two main reasons for this improvement.…”

Section: Discussionsupporting

confidence: 93%

“…Currently, several studies have attempted to build radiomic models to predict survival in patients with LARC [20][21][22][23][24]. These studies extracted texture parameters from different imaging modalities, including…”

Section: Discussionmentioning

confidence: 99%

“…The quantitative radiomic features are able to further re ect the biological characteristics of tumors and be utilized in the evaluation of therapeutic effects and prognosis [19]. Previous studies have shown the potential of radiomic analysis based on pre-therapy computed tomography (CT) or conventional magnetic resonance imaging (MRI) in predicting survival in LARC patients [20][21][22][23][24]. Nevertheless, there is a limited body of research investigating the value of post-therapy and delta radiomic features.…”

Section: Introductionmentioning

confidence: 99%

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MRI-based radiomic nomogram for predicting disease-free survival in patients with locally advanced rectal cancer

Wen,

Liu,

et al. 2024

Preprint

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Individual prognosis assessment is of paramount importance for treatment decision-making and active surveillance in cancer patients. We aimed to propose a radiomic model based on pre- and post-therapy MRI features for predicting disease-free survival (DFS) in locally advanced rectal cancer (LARC) following neoadjuvant chemoradiotherapy (nCRT) and subsequent surgical resection. This retrospective study included a total of 126 LARC patients, which were randomly assigned to a training set (n=84) and a validation set (n=42). All patients underwent pre- and post-nCRT MRI scans. Radiomic features were extracted from higher resolution T2 -weighted images. Pearson correlation analysis and ANOVA or Relief were utilized for identifying radiomic features associated with DFS. Pre-treatment, post-treatment, and delta radscores were constructed by machine learning algorithms. An individualized nomogram was developed based on significant radscores and clinical variables using multivariate Cox regression analysis. Predictive performance was evaluated by the C-index, calibration curve, and decision curve analysis. The results demonstrated that in the validation set, the clinical model including pre-surgery Carcinoembryonic Antigen (CEA), chemotherapy after radiotherapy, and pathological stage yielded a C-index of 0.755 (95% confidence interval [CI]: 0.630-0.880). While the optimal pre-, post-, and delta-radscores achieved C-indices of 0.724 (95%CI: 0.585-0.863), 0.701 (95%CI: 0.544-0.858), and 0.625 (95%CI: 0.439-0.811), respectively. The nomogram integrating pre-surgery CEA, pathological stage, alongside pre- and post-nCRT radscore, obtained the highest C-index of 0.833 (95%CI: 0.694-0.972). The calibration curve and decision curves exhibited good calibration and clinical usefulness of the nomogram. Furthermore, the nomogram categorized patients into high- and low-risk groups exhibiting distinct DFS (both P<0.0001). In summary, the nomogram incorporating pre- and post-therapy radscores and clinical factors could predict DFS in patients with LARC, which warrants further external validations.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MRI-based radiomic nomogram for predicting disease-free survival in patients with locally advanced rectal cancer

Wen,

Liu,

et al. 2024

Preprint

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“…With exceptionally high costs associated with the field, these automated AI predictive models can offer a more cost-effective and efficient approach to the clinical management of infertility. AI has been developed and used for the interpretation of semen analysis [28] , determination of sperm hyperactivation and DNA fragmentation [29,30] , objective sperm selection for assisted reproductive techniques [31] , imaging of reproductive structures [32,33] , and more. Less commonly used in sexual medicine so far has been the applications of AI in ED, presumably due to the lower costs associated with treatment, simpler diagnosis, and lower overall research funding than infertility.…”

Section: Ai Applications In the Field Of Sexual Medicine And Men's He...mentioning

confidence: 99%

Shaping the future of men's sexual health: How artificial intelligence can assist in the management and treatment of erectile dysfunction

Sanchez,

Slovacek,

Wang

2024

UroPrecision

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Artificial intelligence (AI) is a complex combination of multidisciplinary machines and systems that can replicate human‐like cognitive tasks to execute capabilities such as pattern recognition, decision‐making, and problem‐solving. Dating back to the 2000s, AI has been utilized in the medical field, however the interest in this subject has sharply increased over the past several years. Erectile dysfunction (ED) is an increasingly pervasive issue as men age, affecting up to 150 million men worldwide. In the field of men's health, AI has been employed to assist physicians in the evaluation and management of ED. This article aims to summarize the ways in which AI has been utilized in the management of ED, as well as the considerations that must be made when implementing this technology. AI can be utilized for virtual health assistance to protect patient privacy and increase access to care. Augmented reality can aid surgeons in real‐time during operations, as well as be utilized to prepare physicians for situations that they may encounter in the operating room. Pharmaceutical companies can benefit from AI in the interpretation of data, analysis of chemical compounds and in drug development. Additionally, AI can be used to assist patients in post‐procedure recovery in the form of rehabilitation and post‐treatment monitoring. While the utilization of AI in men's health is an exciting venture, there are tremendous ethical and practical considerations that have limited its use in the management of ED.

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“…Previous studies have suggested its potential to improve patient management and clinical decision-making by uncovering disease characteristics that may be invisible to human eyes [18,19]. In RC, radiomics has attained impressive performance in different oncological scenarios, including evaluating tumor biological behaviors [20], assessing treatment response [21,22], and predicting prognosis [23]. Despite these advances, radiomics features tend to be affected by anisotropic resolution and low voxel statistics in current medical imaging [24].…”

Section: Introductionmentioning

confidence: 99%

Deep-learning-based 3D super-resolution MRI radiomics model: superior predictive performance in preoperative T-staging of rectal cancer

2022

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Objectives To investigate the feasibility and efficacy of a deep-learning (DL)-based three-dimensional (3D) super-resolution (SR) MRI radiomics model for preoperative T-staging prediction in rectal cancer (RC). Methods Seven hundred six eligible RC patients (T1/2 = 287, T3/4 = 419) were retrospectively enrolled in this study and chronologically allocated into a training cohort (n = 565) and a validation cohort (n = 141). We conducted a deep-transfer-learning network on high-resolution (HR) T2-weighted imaging (T2WI) to enhance the z-resolution of the images and acquired the preoperative SRT2WI. The radiomics models named modelHRT2 and modelSRT2 were respectively constructed with high-dimensional quantitative features extracted from manually segmented volume of interests of HRT2WI and SRT2WI through the Least Absolute Shrinkage and Selection Operator method. The performances of the models were evaluated by ROC, calibration, and decision curves. Results ModelSRT2 outperformed modelHRT2 (AUC 0.869, sensitivity 71.1%, specificity 93.1%, and accuracy 83.3% vs. AUC 0.810, sensitivity 89.5%, specificity 70.1%, and accuracy 77.3%) in distinguishing T1/2 and T3/4 RC with significant difference (p < 0.05). Both radiomics models achieved higher AUCs than the expert radiologists (0.685, 95% confidence interval 0.595–0.775, p < 0.05). The calibration curves confirmed high goodness of fit, and the decision curve analysis revealed the clinical value. Conclusions ModelSRT2 yielded superior predictive performance in preoperative RC T-staging by comparison with modelHRT2 and expert radiologists’ visual assessments. Key Points • For the first time, DL-based 3D SR images were applied in radiomics analysis for clinical utility. • Compared with the visual assessment of expert radiologists and the conventional radiomics model based on HRT2WI, the SR radiomics model showed a more favorable capability in helping clinicians assess the invasion depth of RC preoperatively. • This is the largest radiomics study for T-staging prediction in RC.

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Radiomics Features at Multiparametric MRI Predict Disease-Free Survival in Patients With Locally Advanced Rectal Cancer

Cited by 19 publications

References 32 publications

MRI-based radiomic nomogram for predicting disease-free survival in patients with locally advanced rectal cancer

MRI-based radiomic nomogram for predicting disease-free survival in patients with locally advanced rectal cancer

Shaping the future of men's sexual health: How artificial intelligence can assist in the management and treatment of erectile dysfunction

Deep-learning-based 3D super-resolution MRI radiomics model: superior predictive performance in preoperative T-staging of rectal cancer

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