Artificial intelligence-driven precision management of chest wall tumors: Prospects and challenges in clinical translation_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

GUO Hai ^1,2,3 , ZENG Zhongyi ^2,3 , LIN Haonan ^1,2,3 ,  LIN Feng ^1,2,3

1. Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;
2. Department of Chest Wall Diseases Center, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;
3. Department of Thoracic Surgery, West China Tianfu Hospital, Sichuan University, Chengdu, 610213, P. R. China;

Corresponding?author：

LIN Feng, Email: linfeng0220@aliyun.com

Keywords：

Artificial intelligence; chest wall tumors; radiomics; precision diagnosis; efficacy prediction; deep learning; genomics

DOI：

10.7507/1007-4848.202602056

Video：

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Abstract Full text Figures/Tables Video References Cited by

The precision diagnosis and treatment of chest wall tumors heavily rely on imaging assessment, which is limited by the subjectivity of conventional methods and their insensitivity to microscopic features. Artificial intelligence (AI) technologies, particularly radiomics and deep learning, offer new opportunities to overcome these bottlenecks. It is important to note, however, that direct AI research focused specifically on chest wall tumors remains scarce. Most current insights are derived from methodological borrowing and conceptual extrapolation of studies on other solid tumors, such as lung, breast, and esophageal cancers. This review systematically outlines the potential application framework of AI in the management of chest wall tumors, encompassing benign-malignant differentiation, non-invasive pathological subtyping, early treatment response prediction, and prognosis stratification based on multi-modal imaging. It discusses the core model-building strategies and validation processes centered on data fusion, and critically analyzes the unique challenges in this field, including data scarcity, model interpretability, and clinical translation. Moving forward, key directions for translating the potential of AI into clinical reality include fostering the development of dedicated chest wall tumor datasets, conducting prospective validation studies, and exploring "imaging-genomics" integration as well as dynamic decision-support systems.

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2.	Indelicato DJ, Vega RBM, Viviers E, et al. Modern therapy for chest wall Ewing sarcoma: an update of the University of Florida experience. Int J Radiat Oncol Biol Phys, 2022, 113(2): 345-354.
3.	Kunkler IH, Russell NS, Anderson N, et al. Ten-year survival after postmastectomy chest-wall irradiation in breast cancer. N Engl J Med, 2025, 393(18): 1771-1783.
4.	Wang L, Yang C, Sun C, et al. Fused deposition modeling PEEK implants for personalized surgical application: from clinical need to biofabrication. Int J Bioprint, 2022, 8(4): 615.
5.	Uota F, Iwano S, Kamiya S, et al. Diagnostic utility of chest wall vessel involvement sign on ultra-high-resolution CT for primary lung cancer infiltrating the chest wall. Eur Radiol, 2025, 35(8): 4824-4833.
6.	Budacan AM, Patel AJ, Babu P, et al. Chest wall resection and reconstruction for primary chest wall sarcomas: analysis of survival, predictors of outcome, and long-term functional status. J Thorac Cardiovasc Surg, 2025, 169(4): 1120-1130. e1121.
7.	Wang F, Bao M, Tao B, et al. A deep learning model combining circulating tumor cells and radiological features in the multi-classification of mediastinal lesions in comparison with thoracic surgeons: a large-scale retrospective study. BMC Med, 2025, 23(1): 267.
8.	Pallumeera M, Giang JC, Singh R, et al. Evolving and novel applications of artificial intelligence in cancer imaging. Cancers, 2025, 17(9): 1510.
9.	Bera K, Braman N, Gupta A, et al. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol, 2022, 19(2): 132-146.
10.	Fountzilas E, Pearce T, Baysal MA, et al. Convergence of evolving artificial intelligence and machine learning techniques in precision oncology. npj Digit Med, 2025, 8(1): 75.
11.	Mansour J, Raptis D, Bhalla S, et al. Diagnostic and imaging approaches to chest wall lesions. Radiographics, 2022, 42(2): 359-378.
12.	Kim H, Kim K, Oh SJ, et al. AI-assisted analysis to facilitate detection of humeral lesions on chest radiographs. Radiol Artif Intell, 2024, 6(3): e230094.
13.	Spaanderman DJ, Marzetti M, Wan X, et al. AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines. EBioMedicine, 2025, 114: 105642.
14.	Nozaki T, Hashimoto M, Ueda D, et al. Recent topics in musculoskeletal imaging focused on clinical applications of AI: how should radiologists approach and use AI? Radiol Med, 2025, 130(5): 587-597.
15.	Lind Plesner L, Müller FC, Brejneb?l MW, et al. Commercially available chest radiograph AI tools for detecting airspace disease, pneumothorax, and pleural effusion. Radiology, 2023, 308(3): e231236.
16.	Barros V, Tlusty T, Barkan E, et al. Virtual biopsy by using artificial intelligence-based multimodal modeling of binational mammography data. Radiology, 2022, 306(3): e220027.
17.	Atallah NM, Wahab N, Toss MS, et al. Deciphering the morphology of tumor-stromal features in invasive breast cancer using artificial intelligence. Mod Pathol, 2023, 36(10): 100254.
18.	Howard FM, Hieromnimon HM, Ramesh S, et al. Generative adversarial networks accurately reconstruct pan-cancer histology from pathologic, genomic, and radiographic latent features. Sci Adv, 2024, 10(46): eadq0856.
19.	劉珂, 張恩龍, 王奇政, 等. 影像組學在骨腫瘤中的臨床研究進展. 磁共振成像, 2020, 11(10): 957-960.Liu K, Zhang EL, Wang QZ, et al. Clinical research progress of radiomics in bone tumors. Chin J Magn Reson Imaging, 2020, 11(10): 957-960.
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21.	Gupta V, Gowing SD, Pandya R, et al. Surgeon-led point-of-care ultrasound-guided thoracic biopsy: a new paradigm in efficient diagnosis and resource-sparing care. J Thorac Cardiovasc Surg, 2025, 170(2): 369-378.
22.	Petkov R, Minchev T, Yamakova Y, et al. Diagnostic value and complication rate of ultrasound-guided transthoracic core needle biopsy in mediastinal lesions. PLoS One, 2020, 15(4): e0231523.
23.	Wallace MJ, Krishnamurthy S, Broemeling LD, et al. CT-guided percutaneous fine-needle aspiration biopsy of small (≤1 cm) pulmonary lesions. Radiology, 2002, 225(3): 823-828.
24.	Skretting IK, Ruud EA, Ashraf H. Diagnostic yield, complications, pathology and anatomical features in CT-guided percutaneous needle biopsy of mediastinal tumours. PLoS One, 2022, 17(11): e0277200.
25.	Brioulet J, David A, Sagan C, et al. Percutaneous CT-guided lung biopsy for the diagnosis of persistent pulmonary consolidation. Diagn Interv Imaging, 2020, 101(11): 727-732.
26.	Zhang X, Zhang G, Qiu X, et al. Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes. Biomark Res, 2024, 12(1): 12.
27.	Shiri I, Amini M, Nazari M, et al. Impact of feature harmonization on radiogenomics analysis: prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images. Comput Biol Med, 2022, 142: 105230.
28.	Zhuo Z, Qu L, Zhang P, et al. Prediction of H3K27M-mutant brainstem glioma by amide proton transfer-weighted imaging and its derived radiomics. Eur J Nucl Med Mol Imaging, 2021, 48(13): 4426-4436.
29.	Cui H, Sun Y, Zhao D, et al. Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions. J Transl Med, 2023, 21(1): 44.
30.	Gounder MM, Agaram NP, Trabucco SE, et al. Clinical genomic profiling in the management of patients with soft tissue and bone sarcoma. Nat Commun, 2022, 13(1): 3406.
31.	Landuzzi L, Ruzzi F, Lollini PL, et al. Synovial sarcoma preclinical modeling: integrating transgenic mouse models and patient-derived models for translational research. Cancers, 2023, 15(3): 588.
32.	Hwang EJ, Goo JM, Park CM. AI applications for thoracic imaging: considerations for best practice. Radiology, 2025, 314(2): e240650.
33.	Vyas A, Kumar K, Sharma A, et al. Advancing the frontier of artificial intelligence on emerging technologies to redefine cancer diagnosis and care. Comput Biol Med, 2025, 191: 110178.
34.	Zhu J, Xu B, Fan T, et al. Sub-regional radiomics combining multichannel 2-dimensional or 3-dimensional deep learning for predicting neoadjuvant chemo-immunotherapy response in esophageal squamous cell carcinoma: a multicenter study. npj Precis Oncol, 2025, 9(1): 248.
35.	Chen ZH, Lin L, Wu CF, et al. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun, 2021, 41(11): 1100-1115.
36.	Morales-Galicia AE, Rincón-Sánchez MN, Ramírez-Mejía MM, et al. Outcome prediction for cholangiocarcinoma prognosis: embracing the machine learning era. World J Gastroenterol, 2025, 31(21): 106808.
37.	He Y, Duan S, Wang W, et al. Integrative radiomics clustering analysis to decipher breast cancer heterogeneity and prognostic indicators through multiparametric MRI. npj Breast Cancer, 2024, 10(1): 72.
38.	Guo H, Tang HT, Hu WL, et al. The application of radiomics in esophageal cancer: predicting the response after neoadjuvant therapy. Front Oncol, 2023, 13: 1082960.
39.	Yusufaly TI, Zou J, Nelson TJ, et al. Improved prognosis of treatment failure in cervical cancer with nontumor PET/CT radiomics. J Nucl Med, 2021, 63(7): 1087-1093.
40.	Zhang Y, Bao X, Wang Y, et al. Machine learning-based radiomics model: prognostic prediction and mechanism exploration in patients with endometrial cancer. Biomark Res, 2025, 13(1): 119.
41.	Yu Y, Ren W, He Z, et al. Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of lncRNAs in patients with breast cancer: a multicenter cohort study. Breast Cancer Res, 2023, 25(1): 132.
42.	Huang Q, Xu N, Yin J, et al. Radiomics reveals the biological basis for non-small cell lung cancer prognostic stratification by reflecting tumor immune microenvironment heterogeneity. Front Immunol, 2025, 16: 1708692.
43.	Guo Y, Gong B, Li Y, et al. Non-invasive prediction of NSCLC immunotherapy efficacy and tumor microenvironment through unsupervised machine learning-driven CT radiomic subtypes: a multi-cohort study. Int J Surg, 2025, 111(10): 6592-6603.
44.	Prinster D, Mahmood A, Saria S, et al. Care to explain? AI explanation types differentially impact chest radiograph diagnostic performance and physician trust in AI. Radiology, 2024, 313(2): e233261.
45.	Gupta A, Bajaj S, Nema P, et al. Potential of AI and ML in oncology research including diagnosis, treatment and future directions: a comprehensive prospective. Comput Biol Med, 2025, 189: 109918.
46.	Khosravi P, Fuchs TJ, Ho DJ. Artificial intelligence-driven cancer diagnostics: enhancing radiology and pathology through reproducibility, explainability, and multimodality. Cancer Res, 2025, 85(13): 2356-2367.
47.	Li Y, Wang J, Pan X, et al. MRI-mediated intelligent multimodal imaging system: from artificial intelligence to clinical imaging diagnosis. Drug Discov Today, 2025, 30(7): 104399.
48.	Ligero M, Gielen B, Navarro V, et al. A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking? npj Precis Oncol, 2024, 8(1): 42.
49.	Wang J, Zeng Z, Li Z, et al. The clinical application of artificial intelligence in cancer precision treatment. J Transl Med, 2025, 23(1): 120.
50.	Shao J, Ma J, Zhang Q, et al. Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology. Semin Cancer Biol, 2023, 91: 1-15.
51.	Schouten D, Nicoletti G, Dille B, et al. Navigating the landscape of multimodal AI in medicine: a scoping review on technical challenges and clinical applications. Med Image Anal, 2025, 105: 103621.
52.	Hong EK, Ham J, Roh B, et al. Diagnostic accuracy and clinical value of a domain-specific multimodal generative AI model for chest radiograph report generation. Radiology, 2025, 314(3): e241476.
53.	Baccouche A, Garcia-Zapirain B, Castillo Olea C, et al. Connected-UNets: a deep learning architecture for breast mass segmentation. npj Breast Cancer, 2021, 7(1): 151.
54.	Liu Z, Xu J, Yin C, et al. Development and external validation of an artificial intelligence-based method for scalable chest radiograph diagnosis: a multi-country cross-sectional study. Research, 2024, 7: 0426.
55.	Zhang Z, Luo T, Yan M, et al. Voxel-level radiomics and deep learning for predicting pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant immunotherapy and chemotherapy. J Immunother Cancer, 2025, 13(3): e011149.
56.	Ali S, Akhlaq F, Imran AS, et al. The enlightening role of explainable artificial intelligence in medical & healthcare domains: a systematic literature review. Comput Biol Med, 2023, 166: 107555.
57.	Sarvan M, Etienne H, Bankel L, et al. Outcome analysis of treatment modalities for thoracic sarcomas. Cancers, 2023, 15(21): 5154.
58.	Yanagawa M, Ito R, Nozaki T, et al. New trend in artificial intelligence-based assistive technology for thoracic imaging. Radiol Med, 2023, 128(10): 1236-1249.
59.	Ramsay AIG, Crellin N, Lawrence R, et al. Procurement and early deployment of artificial intelligence tools for chest diagnostics in NHS services in England: a rapid, mixed method evaluation. EClinicalMedicine, 2025, 89: 103481.
60.	Bennani S, Regnard NE, Ventre J, et al. Using AI to improve radiologist performance in detection of abnormalities on chest radiographs. Radiology, 2023, 309(3): e230860.
61.	Sadilek A, Liu L, Nguyen D, et al. Privacy-first health research with federated learning. npj Digit Med, 2021, 4(1): 132.
62.	Sav S, Bossuat JP, Troncoso-Pastoriza JR, et al. Privacy-preserving federated neural network learning for disease-associated cell classification. Patterns, 2022, 3(5): 100487.
63.	Lo Gullo R, Marcus E, Huayanay J, et al. Artificial intelligence-enhanced breast MRI: applications in breast cancer primary treatment response assessment and prediction. Invest Radiol, 2024, 59(3): 230-242.
64.	Li J, Jin L, Wang Z, et al. Towards precision medicine based on a continuous deep learning optimization and ensemble approach. npj Digit Med, 2023, 6(1): 18.

1. Lu HH, Chiu NT. Dual-time-point 18F-FDG PET/CT for differentiating a chest wall hemangioma from a malignant metastasis. Clin Nucl Med, 2023, 48(4): 332-334.
2. Indelicato DJ, Vega RBM, Viviers E, et al. Modern therapy for chest wall Ewing sarcoma: an update of the University of Florida experience. Int J Radiat Oncol Biol Phys, 2022, 113(2): 345-354.
3. Kunkler IH, Russell NS, Anderson N, et al. Ten-year survival after postmastectomy chest-wall irradiation in breast cancer. N Engl J Med, 2025, 393(18): 1771-1783.
4. Wang L, Yang C, Sun C, et al. Fused deposition modeling PEEK implants for personalized surgical application: from clinical need to biofabrication. Int J Bioprint, 2022, 8(4): 615.
5. Uota F, Iwano S, Kamiya S, et al. Diagnostic utility of chest wall vessel involvement sign on ultra-high-resolution CT for primary lung cancer infiltrating the chest wall. Eur Radiol, 2025, 35(8): 4824-4833.
6. Budacan AM, Patel AJ, Babu P, et al. Chest wall resection and reconstruction for primary chest wall sarcomas: analysis of survival, predictors of outcome, and long-term functional status. J Thorac Cardiovasc Surg, 2025, 169(4): 1120-1130. e1121.
7. Wang F, Bao M, Tao B, et al. A deep learning model combining circulating tumor cells and radiological features in the multi-classification of mediastinal lesions in comparison with thoracic surgeons: a large-scale retrospective study. BMC Med, 2025, 23(1): 267.
8. Pallumeera M, Giang JC, Singh R, et al. Evolving and novel applications of artificial intelligence in cancer imaging. Cancers, 2025, 17(9): 1510.
9. Bera K, Braman N, Gupta A, et al. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol, 2022, 19(2): 132-146.
10. Fountzilas E, Pearce T, Baysal MA, et al. Convergence of evolving artificial intelligence and machine learning techniques in precision oncology. npj Digit Med, 2025, 8(1): 75.
11. Mansour J, Raptis D, Bhalla S, et al. Diagnostic and imaging approaches to chest wall lesions. Radiographics, 2022, 42(2): 359-378.
12. Kim H, Kim K, Oh SJ, et al. AI-assisted analysis to facilitate detection of humeral lesions on chest radiographs. Radiol Artif Intell, 2024, 6(3): e230094.
13. Spaanderman DJ, Marzetti M, Wan X, et al. AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines. EBioMedicine, 2025, 114: 105642.
14. Nozaki T, Hashimoto M, Ueda D, et al. Recent topics in musculoskeletal imaging focused on clinical applications of AI: how should radiologists approach and use AI? Radiol Med, 2025, 130(5): 587-597.
15. Lind Plesner L, Müller FC, Brejneb?l MW, et al. Commercially available chest radiograph AI tools for detecting airspace disease, pneumothorax, and pleural effusion. Radiology, 2023, 308(3): e231236.
16. Barros V, Tlusty T, Barkan E, et al. Virtual biopsy by using artificial intelligence-based multimodal modeling of binational mammography data. Radiology, 2022, 306(3): e220027.
17. Atallah NM, Wahab N, Toss MS, et al. Deciphering the morphology of tumor-stromal features in invasive breast cancer using artificial intelligence. Mod Pathol, 2023, 36(10): 100254.
18. Howard FM, Hieromnimon HM, Ramesh S, et al. Generative adversarial networks accurately reconstruct pan-cancer histology from pathologic, genomic, and radiographic latent features. Sci Adv, 2024, 10(46): eadq0856.
19. 劉珂, 張恩龍, 王奇政, 等. 影像組學在骨腫瘤中的臨床研究進展. 磁共振成像, 2020, 11(10): 957-960.Liu K, Zhang EL, Wang QZ, et al. Clinical research progress of radiomics in bone tumors. Chin J Magn Reson Imaging, 2020, 11(10): 957-960.
20. 鄭小龍. 基于術前CT和機器學習的影像組學對原發性骨腫瘤良惡性鑒別作用的初步研究. 川北醫學院, 2022.Zheng XL. Preliminary study on the differentiation of benign and malignant primary bone tumors using radiomics based on preoperative CT and machine learning. North Sichuan Medical College, 2022.
21. Gupta V, Gowing SD, Pandya R, et al. Surgeon-led point-of-care ultrasound-guided thoracic biopsy: a new paradigm in efficient diagnosis and resource-sparing care. J Thorac Cardiovasc Surg, 2025, 170(2): 369-378.
22. Petkov R, Minchev T, Yamakova Y, et al. Diagnostic value and complication rate of ultrasound-guided transthoracic core needle biopsy in mediastinal lesions. PLoS One, 2020, 15(4): e0231523.
23. Wallace MJ, Krishnamurthy S, Broemeling LD, et al. CT-guided percutaneous fine-needle aspiration biopsy of small (≤1 cm) pulmonary lesions. Radiology, 2002, 225(3): 823-828.
24. Skretting IK, Ruud EA, Ashraf H. Diagnostic yield, complications, pathology and anatomical features in CT-guided percutaneous needle biopsy of mediastinal tumours. PLoS One, 2022, 17(11): e0277200.
25. Brioulet J, David A, Sagan C, et al. Percutaneous CT-guided lung biopsy for the diagnosis of persistent pulmonary consolidation. Diagn Interv Imaging, 2020, 101(11): 727-732.
26. Zhang X, Zhang G, Qiu X, et al. Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes. Biomark Res, 2024, 12(1): 12.
27. Shiri I, Amini M, Nazari M, et al. Impact of feature harmonization on radiogenomics analysis: prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images. Comput Biol Med, 2022, 142: 105230.
28. Zhuo Z, Qu L, Zhang P, et al. Prediction of H3K27M-mutant brainstem glioma by amide proton transfer-weighted imaging and its derived radiomics. Eur J Nucl Med Mol Imaging, 2021, 48(13): 4426-4436.
29. Cui H, Sun Y, Zhao D, et al. Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions. J Transl Med, 2023, 21(1): 44.
30. Gounder MM, Agaram NP, Trabucco SE, et al. Clinical genomic profiling in the management of patients with soft tissue and bone sarcoma. Nat Commun, 2022, 13(1): 3406.
31. Landuzzi L, Ruzzi F, Lollini PL, et al. Synovial sarcoma preclinical modeling: integrating transgenic mouse models and patient-derived models for translational research. Cancers, 2023, 15(3): 588.
32. Hwang EJ, Goo JM, Park CM. AI applications for thoracic imaging: considerations for best practice. Radiology, 2025, 314(2): e240650.
33. Vyas A, Kumar K, Sharma A, et al. Advancing the frontier of artificial intelligence on emerging technologies to redefine cancer diagnosis and care. Comput Biol Med, 2025, 191: 110178.
34. Zhu J, Xu B, Fan T, et al. Sub-regional radiomics combining multichannel 2-dimensional or 3-dimensional deep learning for predicting neoadjuvant chemo-immunotherapy response in esophageal squamous cell carcinoma: a multicenter study. npj Precis Oncol, 2025, 9(1): 248.
35. Chen ZH, Lin L, Wu CF, et al. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun, 2021, 41(11): 1100-1115.
36. Morales-Galicia AE, Rincón-Sánchez MN, Ramírez-Mejía MM, et al. Outcome prediction for cholangiocarcinoma prognosis: embracing the machine learning era. World J Gastroenterol, 2025, 31(21): 106808.
37. He Y, Duan S, Wang W, et al. Integrative radiomics clustering analysis to decipher breast cancer heterogeneity and prognostic indicators through multiparametric MRI. npj Breast Cancer, 2024, 10(1): 72.
38. Guo H, Tang HT, Hu WL, et al. The application of radiomics in esophageal cancer: predicting the response after neoadjuvant therapy. Front Oncol, 2023, 13: 1082960.
39. Yusufaly TI, Zou J, Nelson TJ, et al. Improved prognosis of treatment failure in cervical cancer with nontumor PET/CT radiomics. J Nucl Med, 2021, 63(7): 1087-1093.
40. Zhang Y, Bao X, Wang Y, et al. Machine learning-based radiomics model: prognostic prediction and mechanism exploration in patients with endometrial cancer. Biomark Res, 2025, 13(1): 119.
41. Yu Y, Ren W, He Z, et al. Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of lncRNAs in patients with breast cancer: a multicenter cohort study. Breast Cancer Res, 2023, 25(1): 132.
42. Huang Q, Xu N, Yin J, et al. Radiomics reveals the biological basis for non-small cell lung cancer prognostic stratification by reflecting tumor immune microenvironment heterogeneity. Front Immunol, 2025, 16: 1708692.
43. Guo Y, Gong B, Li Y, et al. Non-invasive prediction of NSCLC immunotherapy efficacy and tumor microenvironment through unsupervised machine learning-driven CT radiomic subtypes: a multi-cohort study. Int J Surg, 2025, 111(10): 6592-6603.
44. Prinster D, Mahmood A, Saria S, et al. Care to explain? AI explanation types differentially impact chest radiograph diagnostic performance and physician trust in AI. Radiology, 2024, 313(2): e233261.
45. Gupta A, Bajaj S, Nema P, et al. Potential of AI and ML in oncology research including diagnosis, treatment and future directions: a comprehensive prospective. Comput Biol Med, 2025, 189: 109918.
46. Khosravi P, Fuchs TJ, Ho DJ. Artificial intelligence-driven cancer diagnostics: enhancing radiology and pathology through reproducibility, explainability, and multimodality. Cancer Res, 2025, 85(13): 2356-2367.
47. Li Y, Wang J, Pan X, et al. MRI-mediated intelligent multimodal imaging system: from artificial intelligence to clinical imaging diagnosis. Drug Discov Today, 2025, 30(7): 104399.
48. Ligero M, Gielen B, Navarro V, et al. A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking? npj Precis Oncol, 2024, 8(1): 42.
49. Wang J, Zeng Z, Li Z, et al. The clinical application of artificial intelligence in cancer precision treatment. J Transl Med, 2025, 23(1): 120.
50. Shao J, Ma J, Zhang Q, et al. Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology. Semin Cancer Biol, 2023, 91: 1-15.
51. Schouten D, Nicoletti G, Dille B, et al. Navigating the landscape of multimodal AI in medicine: a scoping review on technical challenges and clinical applications. Med Image Anal, 2025, 105: 103621.
52. Hong EK, Ham J, Roh B, et al. Diagnostic accuracy and clinical value of a domain-specific multimodal generative AI model for chest radiograph report generation. Radiology, 2025, 314(3): e241476.
53. Baccouche A, Garcia-Zapirain B, Castillo Olea C, et al. Connected-UNets: a deep learning architecture for breast mass segmentation. npj Breast Cancer, 2021, 7(1): 151.
54. Liu Z, Xu J, Yin C, et al. Development and external validation of an artificial intelligence-based method for scalable chest radiograph diagnosis: a multi-country cross-sectional study. Research, 2024, 7: 0426.
55. Zhang Z, Luo T, Yan M, et al. Voxel-level radiomics and deep learning for predicting pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant immunotherapy and chemotherapy. J Immunother Cancer, 2025, 13(3): e011149.
56. Ali S, Akhlaq F, Imran AS, et al. The enlightening role of explainable artificial intelligence in medical & healthcare domains: a systematic literature review. Comput Biol Med, 2023, 166: 107555.
57. Sarvan M, Etienne H, Bankel L, et al. Outcome analysis of treatment modalities for thoracic sarcomas. Cancers, 2023, 15(21): 5154.
58. Yanagawa M, Ito R, Nozaki T, et al. New trend in artificial intelligence-based assistive technology for thoracic imaging. Radiol Med, 2023, 128(10): 1236-1249.
59. Ramsay AIG, Crellin N, Lawrence R, et al. Procurement and early deployment of artificial intelligence tools for chest diagnostics in NHS services in England: a rapid, mixed method evaluation. EClinicalMedicine, 2025, 89: 103481.
60. Bennani S, Regnard NE, Ventre J, et al. Using AI to improve radiologist performance in detection of abnormalities on chest radiographs. Radiology, 2023, 309(3): e230860.
61. Sadilek A, Liu L, Nguyen D, et al. Privacy-first health research with federated learning. npj Digit Med, 2021, 4(1): 132.
62. Sav S, Bossuat JP, Troncoso-Pastoriza JR, et al. Privacy-preserving federated neural network learning for disease-associated cell classification. Patterns, 2022, 3(5): 100487.
63. Lo Gullo R, Marcus E, Huayanay J, et al. Artificial intelligence-enhanced breast MRI: applications in breast cancer primary treatment response assessment and prediction. Invest Radiol, 2024, 59(3): 230-242.
64. Li J, Jin L, Wang Z, et al. Towards precision medicine based on a continuous deep learning optimization and ensemble approach. npj Digit Med, 2023, 6(1): 18.

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Latest ArticlesArtificial intelligence-driven precision management of chest wall tumors: Prospects and challenges in clinical translation

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