[18F]FDG PET-CT radiomics signature to predict pathological complete response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: a multicenter study

European Radiology, Journal Year: 2023, Volume and Issue: 34(7), P. 4352 - 4363

Published: Dec. 21, 2023

Language: Английский

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A review of deep learning approaches in clinical and healthcare systems based on medical image analysis

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(12), P. 36039 - 36080

Published: Sept. 29, 2023

Language: Английский

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Deep learning model based on primary tumor to predict lymph node status in clinical stage IA lung adenocarcinoma: a multicenter study

Journal of the National Cancer Center, Journal Year: 2024, Volume and Issue: 4(3), P. 233 - 240

Published: Feb. 2, 2024

To develop a deep learning model to predict lymph node (LN) status in clinical stage IA lung adenocarcinoma patients. This diagnostic study included 1,009 patients with pathologically confirmed T1N0M0 from two independent datasets (699 Cancer Hospital of Chinese Academy Medical Sciences and 310 PLA General Hospital) between January 2005 December 2019. The dataset was randomly split into training cohort (559 patients) validation (140 train tune based on residual network (ResNet). used as testing evaluate the generalization ability model. Thoracic radiologists manually segmented tumors interpreted high-resolution computed tomography (HRCT) features for predictive performance assessed by area under curves (AUCs), accuracy, precision, recall, F1 score. Subgroup analysis performed potential bias population. A total were this study; 409 (40.5%) male 600 (59.5%) female. median age 57.0 years (inter-quartile range, IQR: 50.0–64.0). achieved AUCs 0.906 (95% CI: 0.873–0.938) 0.893 0.857–0.930) predicting pN0 disease non-pure ground glass nodule (non-pGGN) cohort, respectively. No significant difference detected non-pGGN (P = 0.622). precisions 0.979 0.963–0.995) 0.983 0.967–0.998) 0.848 0.798–0.898) 0.831 0.776–0.887) pN2 0.657). recalls 0.903 0.870–0.936) 0.931 0.901–0.961) superior will help target extension dissection reduce ineffective early-stage

Language: Английский

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A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer

Science Advances, Journal Year: 2025, Volume and Issue: 11(18)

Published: May 1, 2025

Accurately predicting pathological complete response (pCR) before neoadjuvant chemotherapy (NAC) is crucial for patients with breast cancer. In this study, we developed a multimodal integrated fully automated pipeline system (MIFAPS) in forecasting pCR to NAC, using multicenter and prospective dataset of 1004 locally advanced cancer, incorporating pretreatment magnetic resonance imaging, whole slide image, clinical risk factors. The results demonstrated that MIFAPS offered favorable predictive performance both the pooled external test set [area under curve (AUC) = 0.882] (AUC 0.909). addition, significantly outperformed single-modality models ( P < 0.05). Furthermore, high deep learning scores were associated immune-related pathways promotion antitumor cells microenvironment during biological basis exploration. Overall, our study demonstrates promising approach improving prediction NAC cancer through integration data.

Language: Английский

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Multimodal deep-learning model using pre-treatment endoscopic images and clinical information to predict efficacy of neoadjuvant chemotherapy in esophageal squamous cell carcinoma

Esophagus, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 10, 2025

Language: Английский

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Artificial intelligence for diagnosis and predictive biomarkers in Non-Small cell lung cancer Patients: New promises but also new hurdles for the pathologist

Lung Cancer, Journal Year: 2025, Volume and Issue: 200, P. 108110 - 108110

Published: Jan. 27, 2025

Language: Английский

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Bibliometric insight into neoadjuvant immunotherapy in non-small cell lung cancer: trends, collaborations, and future avenues

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: Feb. 10, 2025

Background Neoadjuvant immunotherapy (NIT) is a rapidly emerging paradigm for advanced resectable non-small cell lung cancer (NSCLC). However, there no bibliometric analysis in this research field. Objective To analyze the hotspots and trends of NIT NSCLC provide reference study China. Methods Retrieve literature related to from Web Science, PubMed, Scopus databases up September 10, 2024. Use CiteSpace VOSviewer software visualization keywords country, author, institution, literature. Results There were 1575 references, overall annual publication volume showed an upward trend; Forde Patrick M have published most articles The mainly focus on chemotherapy, NSCLC, immunotherapy, neoadjuvant pathological reactions, etc. Conclusions This first comprehensively summarizing NIT’s development NSCLC. Our assessment provides panoramic view milieu surrounding encapsulating present state, evolving trends, potential future directions, particularly emphasizing promise immunochemotherapy.

Language: Английский

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Radiomics and prognostic nutritional index for predicting postoperative survival in esophageal carcinoma

European journal of medical research, Journal Year: 2025, Volume and Issue: 30(1)

Published: March 17, 2025

Surgery offers the potential for a radical cure and prolonged survival in individuals diagnosed with esophageal squamous cell carcinoma (ESCC). However, rates exhibit significant variability among patients. Accurately assessing surgical outcomes remains critical challenge. This study aimed to evaluate predictive value of preoperative radiomics prognostic nutritional index ESCC develop comprehensive model estimating postoperative overall (OS) these A retrospective analysis was conducted on 466 patients from two medical centers. The dataset randomly divided into training cohort (TC, hospital 1, 246 cases), an internal validation (IVC, 106 external (EVC, 2, 114 cases). Radiological features were extracted after delineating region interest, followed by application least absolute shrinkage selection operator (LASSO) regression identify optimal compute score (RS). Independent factors identified via Cox incorporated RS construct combined nomogram. performance evaluated using concordance index, time-dependent receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis. model, which integrated radiomics, tumor-node-metastasis (TNM) staging estimate 3 year OS rate, achieved area under ROC (AUC) values 0.812, 0.786, 0.810 TC, IVC, EVC, respectively, demonstrating excellent accuracy. These surpassed AUCs TNM 0.717, 0.612, 0.699, respectively. model's indexes EVC 0.780, 0.760, 0.764, Calibration curves highlighted nomogram's superior clinical utility. developed combining enabling estimation ESCC. holds promise as tool risk stratification.

Language: Английский

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Multicenter evaluation of predictive clinical and imaging factors for pathological response in non-small cell lung cancer patients treated with neoadjuvant chemotherapy and immune checkpoint inhibitors

Cancer Immunology Immunotherapy, Journal Year: 2025, Volume and Issue: 74(5)

Published: April 5, 2025

This study aimed to identify clinical factors and develop a predictive model for pathological complete response (pCR) major (MPR) in non-small cell lung cancer (NSCLC) patients receiving neoadjuvant chemotherapy combined with immune checkpoint inhibitors (ICIs). Cases meeting inclusion criteria were divided into high- low-risk groups according 75 indicators based on tenfold LASSO selection. Logistic regression was employed analyze both pCR MPR. The accuracy of the nomograms assessed using time-dependent area under curve (AUC). A total 297 from four multiple centers included study, 212 assigned training set 85 testing set. AUC determined prediction (training: 0.97; testing: 0.88) MPR 0.98; 0.81). Significant associations observed between preoperative tumor maximum diameter, standardized uptake value (SUVmax), changes SUVmax, percentage reduction, baseline prostate-specific antigen (TPSA) (P < 0.001). application including non-invasive imaging hematology can help clinicians obtain higher ability predict NSCLC patient's remission, effect is better than that alone. These findings could guide personalized treatment strategies this patient population.

Language: Английский

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Short-term intra- and peri-tumoral spatiotemporal CT radiomics for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer

European Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: April 11, 2025

Language: Английский

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