Multi-omics predictive model based on clinical, radiomic and genomic features for predicting the response of limited-stage small cell lung cancer to definitive chemoradiotherapy.
Ontology highlight
ABSTRACT:
SUBMITTER: Li L
PROVIDER: S-EPMC10775182 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
Publications
Multi-omics predictive model based on clinical, radiomic and genomic features for predicting the response of limited-stage small cell lung cancer to definitive chemoradiotherapy.
Li Li L Duan Jinghao J Gao Yongsheng Y Yin Ying Y Yang Fengchang F Tang Wenjie W Song Xiaoyu X Hu Tao T Cui Jinfeng J Yu Jinming J Yuan Shuanghu S
Clinical and translational medicine 20240101 1
Similar Datasets
Project description:BackgroundThe prognosis of SCLC is poor and difficult to predict. The aim of this study was to explore whether a model based on radiomics and clinical features could predict the prognosis of patients with limited-stage small cell lung cancer (LS-SCLC).MethodsSimulated positioning CT images and clinical features were retrospectively collected from 200 patients with histological diagnosis of LS-SCLC admitted between 2013 and 2021, which were randomly divided into the training (n = 140) and testing (n = 60) groups. Radiomics features were extracted from simulated positioning CT images, and the t-test and the least absolute shrinkage and selection operator (LASSO) were used to screen radiomics features. We then constructed radiomic score (RadScore) based on the filtered radiomics features. Clinical factors were analyzed using the Kaplan-Meier method. The Cox proportional hazards model was used for further analyses of possible prognostic features and clinical factors to build three models including a radiomic model, a clinical model, and a combined model including clinical factors and RadScore. When a model has prognostic predictive value (AUC > 0.7) in both train and test groups, a nomogram will be created. The performance of three models was evaluated using area under the receiver operating characteristic curve (AUC) and Kaplan-Meier analysis.ResultsA total of 1037 features were extracted from simulated positioning CT images which were contrast enhanced CT of the chest. The combined model showed the best prediction, with very poor AUC for the radiomic model and the clinical model. The combined model of OS included 4 clinical features and RadScore, with AUCs of 0.71 and 0.70 in the training and test groups. The combined model of PFS included 4 clinical features and RadScore, with AUCs of 0.72 and 0.71 in the training and test groups. T stages, ProGRP and smoke status were the independent variables for OS in the combined model, whereas T stages, ProGRP and prophylactic cranial irradiation (PCI) were the independent factors for PFS. There was a statistically significant difference between the low- and high-risk groups in the combined model of OS (training group, p < 0.0001; testing group, p = 0.0269) and PFS (training group, p < 0.0001; testing group, p < 0.0001).ConclusionCombined models involved RadScore and clinical factors can predict prognosis in LS-SCLC and show better performance than individual radiomics and clinical models.
| S-EPMC10838455 | biostudies-literature
Project description:ObjectivesRepeatability is crucial for ensuring the generalizability and clinical utility of radiomics-based prognostic models. This study aims to investigate the repeatability of radiomic feature (RF) and its impact on the cross-institutional generalizability of the prognostic model for predicting local recurrence-free survival (LRFS) and overall survival (OS) in esophageal squamous cell cancer (ESCC) receiving definitive (chemo) radiotherapy (dCRT).MethodsNine hundred and twelve patients from two hospitals were included as training and external validation sets, respectively. Image perturbations were applied to contrast-enhanced computed tomography to generate perturbed images. Six thousand five hundred ten RFs from different feature types, bin widths, and filters were extracted from the original and perturbed images separately to evaluate RF repeatability by intraclass correlation coefficient (ICC). The high-repeatable and low-repeatable RF groups grouped by the median ICC were further analyzed separately by feature selection and multivariate Cox proportional hazards regression model for predicting LRFS and OS.ResultsFirst-order statistical features were more repeatable than texture features (median ICC: 0.70 vs 0.42-0.62). RFs from LoG had better repeatability than that of wavelet (median ICC: 0.70-0.84 vs 0.14-0.64). Features with smaller bin widths had higher repeatability (median ICC of 8-128: 0.65-0.47). For both LRFS and OS, the performance of the models based on high- and low-repeatable RFs remained stable in the training set with similar C-index (LRFS: 0.65 vs 0.67, p = 0.958; OS: 0.64 vs 0.65, p = 0.651), while the performance of the model based on the low-repeatable group was significantly lower than that based on the high-repeatable group in the external validation set (LRFS: 0.61 vs 0.67, p = 0.013; OS: 0.56 vs 0.63, p = 0.013).ConclusionsApplying high-repeatable RFs in modeling could safeguard the cross-institutional generalizability of the prognostic model in ESCC.Critical relevance statementThe exploration of repeatable RFs in different diseases and different types of imaging is conducive to promoting the proper use of radiomics in clinical research.Key pointsThe repeatability of RFs impacts the generalizability of the radiomic model. The high-repeatable RFs safeguard the cross-institutional generalizability of the model. Smaller bin width helps improve the repeatability of RFs.
| S-EPMC11458848 | biostudies-literature
Project description:Definitive chemoradiotherapy (dCRT) is reflecting a treatment standard in oesophageal cancer. For irresectable localised tumours and for inoperable patients, dCRT can change the treatment intent from palliative to curative. In patients with squamous cell carcinoma (SCC), in particular in those of cervical location, dCRT is a proper alternative for treatment that may include radical surgery. Patients with localised locoregional recurrence after primary surgery can survive for long-term after salvage CRT.
| S-EPMC5538992 | biostudies-other
Project description:This study was designed to evaluate the predictive performance of 18F-fluorodeoxyglucose positron emission tomography (PET)-based radiomic features for local control of esophageal cancer treated with concurrent chemoradiotherapy (CRT). For each of the 30 patients enrolled, 440 radiomic features were extracted from both pre-CRT and mid-CRT PET images. The top 25 features with the highest areas under the receiver operating characteristic curve for identifying local control status were selected as discriminative features. Four machine-learning methods, random forest (RF), support vector machine, logistic regression, and extreme learning machine, were used to build predictive models with clinical features, radiomic features or a combination of both. An RF model incorporating both clinical and radiomic features achieved the best predictive performance, with an accuracy of 93.3%, a specificity of 95.7%, and a sensitivity of 85.7%. Based on risk scores of local failure predicted by this model, the 2-year local control rate and PFS rate were 100.0% (95% CI 100.0-100.0%) and 52.2% (31.8-72.6%) in the low-risk group and 14.3% (0.0-40.2%) and 0.0% (0.0-40.2%) in the high-risk group, respectively. This model may have the potential to stratify patients with different risks of local failure after CRT for esophageal cancer, which may facilitate the delivery of personalized treatment.
| S-EPMC6028651 | biostudies-literature
Project description:OBJECTIVE:Definitive chemoradiotherapy (CRT) remains the most commonly used treatment for locally advanced esophageal squamous cell carcinoma (SCC), because of perceptions that esophagectomy offers an unclear survival advantage. We compare recurrence, overall survival (OS), and disease-free survival (DFS) in patients treated with definitive CRT or neoadjuvant CRT followed by surgery (trimodality). METHODS:This was a retrospective cohort study of patients with stage II and III SCC of the middle and distal esophagus in patients who completed CRT. Treatment groups were matched (1:1) on covariates using a propensity score-matching approach. The effect of trimodality treatment, compared with definitive CRT, on OS, DFS, and site-specific recurrence was evaluated as a time-dependent variable and analyzed using Cox regression with a gamma frailty term for matched units. RESULTS:We included 232 patients treated between 2000 and 2016: 124 (53%) with definitive CRT and 108 (47%) with trimodality. Trimodality was used less frequently over time (61% before 2009 and 29% after 2009; P < .0001). After matching, each group contained 56 patients. Median OS and DFS were 3.1 and 1.8 years for trimodality versus 2.3 and 1.0 years for CRT. Surgery was independently associated with improved OS (hazard ratio, 0.57; 95% confidence interval, 0.34-0.97; P = .039) and DFS (hazard ratio, 0.51; 95% confidence interval, 0.32-0.83; P = .007). CONCLUSIONS:CRT followed by surgery might decrease local recurrence and increase DFS and OS in patients with esophageal SCC. Until better tools to select patients with pathological complete response are available, surgery should remain an integral component of the treatment of locally advanced esophageal SCC.
| S-EPMC5960990 | biostudies-literature
Project description:In the U.S., the prevalence of small cell lung cancer (SCLC) is declining, probably reflecting the decreasing prevalence of tobacco use. However, a significant number of patients will receive a diagnosis of SCLC, and approximately 40% of patients with SCLC will have limited-stage (LS) disease, which is potentially curable with the combination of chemotherapy and radiation therapy. The standard therapy for LS-SCLC is concurrent chemoradiotherapy, and the 5-year survival rate observed in clinical trials is approximately 25%. The standard chemotherapy remains cisplatin and etoposide, but carboplatin is frequently used in patients who cannot tolerate or have a contraindication to cisplatin. Substantial improvements in survival have been made through improvements in radiation therapy. Concurrent chemoradiotherapy is the preferred therapy for patients who are appropriate candidates. The optimal timing of concurrent chemoradiotherapy is during the first or second cycle, based on data from meta-analyses. The optimal radiation schedule and dose remain topics of debate, but 1.5 Gy twice daily to a total of 45 Gy and 1.8-2.0 Gy daily to a total dose of 60-70 Gy are commonly used treatments. For patients who obtain a near complete or complete response, prophylactic cranial radiation reduces the incidence of brain metastases and improves overall survival. The ongoing Radiation Therapy Oncology Group and Cancer and Leukemia Group B and the European and Canadian phase III trials will investigate different radiation treatment paradigms for patients with LS-SCLC, and completion of these trials is critical.
| S-EPMC3227940 | biostudies-literature
Project description:Background and purposeDisease recurrence and distant metastases (DM) are major concerns for oropharyngeal cancer (OPC) patients receiving definitive chemo-radiotherapy. Here, we investigated whether pre-treatment primary tumor positron emission tomography (PET) features could predict progression-free survival (PFS) or DM.Methods and materialsPrimary tumors were delineated on pre-treatment PET scans for patients treated between 2005 and 2018 using gradient-based segmentation. Radiomic image features were extracted, along with SUV metrics. Features with zero variance and strong correlation to tumor volume, stage, p16 status, age or smoking were excluded. A random forest model was used to identify features associated with PFS. Kaplan-Meier methods, Cox regression and logistic regression with receiver operating characteristics (ROC) and 5-fold cross-validated areas-under-the-curve (CV-AUCs) were used.ResultsA total of 114 patients were included. With median follow-up 40 months (range: 3-138 months), 14 patients had local recurrence, 21 had DM and 38 died. Two-year actuarial local control, distant control, PFS and overall survival was 89%, 84%, 70% and 84%, respectively. The wavelet_LHL_GLDZM_LILDE feature slightly improved PFS prediction compared to clinical features alone (CV-AUC 0.73 vs. 0.71). Age > 65 years (HR = 2.64 (95%CI: 1.36-5.2), p = 0.004) and p16-negative disease (HR = 3.38 (95%CI: 1.72-6.66), p < 0.001) were associated with poor PFS. A binary radiomic classifier strongly predicted DM with multivariable HR = 3.27 (95%CI: 1.15-9.31), p = 0.027, specifically for patients with p16-negative disease with 2-year DM-free survival 83% for low-risk vs. 38% for high-risk patients (p = 0.004).ConclusionsA radiomics signature strongly associated with DM risk could provide a tool for improved risk stratification, potentially adding adjuvant immunotherapy for high-risk patients.
| S-EPMC8867118 | biostudies-literature
Project description:Many studies are devoted to the design of radiomic models for a prediction task. When no effective model is found, it is often difficult to know whether the radiomic features do not include information relevant to the task or because of insufficient data. We propose a downsampling method to answer that question when considering a classification task into two groups. Using two large patient cohorts, several experimental configurations involving different numbers of patients were created. Univariate or multivariate radiomic models were designed from each configuration. Their performance as reflected by the Youden index (YI) and Area Under the receiver operating characteristic Curve (AUC) was compared to the stable performance obtained with the highest number of patients. A downsampling method is described to predict the YI and AUC achievable with a large number of patients. Using the multivariate models involving machine learning, YI and AUC increased with the number of patients while they decreased for univariate models. The downsampling method better estimated YI and AUC obtained with the largest number of patients than the YI and AUC obtained using the number of available patients and identifies the lack of information relevant to the classification task when no such information exists.
| S-EPMC6883070 | biostudies-literature
Project description:BackgroundRectal cancer is a major mortality cause in the United States (US), and its treatment is based on individual risk factors for recurrence in each patient. In patients with rectal cancer, accurate assessment of response to chemoradiotherapy has increased in importance as the variety of treatment options has grown. In this scenario, a controversial non-operative approach may be considered in some patients for whom complete tumor regression is believed to have occurred. The recommended treatment for locally advanced rectal cancer (LARC, T3-4 ± N+) is total mesorectal excision (TME) after neoadjuvant chemoradiotherapy (nCRT). Magnetic resonance imaging (MRI) has become a standard technique for local staging of rectal cancer (tumor, lymph node, and circumferential resection margin [CRM] staging), in both the US and Europe, and it is getting widely used for restaging purposes.AimIn our study, we aimed to use an MRI radiomic model to identify features linked to the different responses of chemoradiotherapy of rectal cancer before surgery, and whether these features are helpful to understand the effectiveness of the treatments.MethodsWe retrospectively evaluated adult patients diagnosed with LARC who were subjected to at least 2 MRI examinations in 10-12 weeks at our hospital, before and after nCRT. The MRI acquisition protocol for the 2 exams included T2 sequence and apparent diffusion coefficient (ADC) map. The patients were divided into 2 groups according to the treatment response: complete or good responders (Group 1) and incomplete or poor responders (Group 2). MRI images were segmented, and quantitative features were extracted and compared between the two groups. Features that showed significant differences (SF) were then included in a LASSO regression method to build a radiomic-based predictive model.ResultsWe included 38 patients (26 males and 12 females), who are classified from T2 and T4 stages in the rectal cancer TNM. After the nCRT, the patients were divided into Group 1 (13 patients), complete or good responders, and Group 2 (25 patients), incomplete or poor responders. Analysis at baseline generated the following significant features for the Mann-Whitney test (out of a total of 107) for each sequence. Also, the analysis at the end of the follow-up yielded a high number of significant features for the Mann-Whitney test (out of a total of 107) for each image. Features selected by the LASSO regression method for each image analyzed; ROC curves relative to each model are represented.ConclusionWe developed an MRI-based radiomic model that is able to differentiate and predict between responders and non-responders who went through nCRT for rectal cancer. This approach might identify early lesions with high surgical potential from lesions potentially resolving after medical treatment.
| S-EPMC10417449 | biostudies-literature
Project description:The combination of toripalimab (an anti-PD-1 antibody) with definitive chemoradiotherapy (CRT) demonstrated encouraging efficacy against locally advanced esophageal squamous cell carcinoma (ESCC) in the EC-CRT-001 phase II trial (NCT04005170). The primary endpoint of this trial was the clinical complete response rate (cCR), and the secondary endpoints included overall survival (OS), progression-free survival (PFS), duration of response, and quality of life. The exploratory analyses of EC-CRT-001 include exploring the role of circulating tumor DNA (ctDNA) and blood-based tumor mutational burden (bTMB) in predicting the response and survival. In total, 118 blood and 35 tissue samples from 42 enrolled patients were included in the analyses. We found that ctDNA-negative patients achieved a higher cCR compared to those with detectable ctDNA during CRT (83%, 19/23 vs. 39%, 7/18; p = 0.008) or post-CRT (78%, 21/27 vs. 30%, 3/10; p = 0.017). Patients with detectable ctDNA during CRT had shorter PFS (p = 0.014). Similarly, patients with post-CRT detectable ctDNA had a significantly shorter PFS (p = 0.012) and worse OS (p = 0.004). Moreover, patients with high bTMB levels during CRT had prolonged OS (p = 0.027). In conclusion, ctDNA and bTMB have the potential to predict treatment efficacy and survival in ESCC treated with CRT and immunotherapy.
| S-EPMC10907344 | biostudies-literature