Predictive role of radiomics features extracted from preoperative cross-sectional imaging of pancreatic ductal adenocarcinoma in detecting lymph node metastasis: a systemic review and meta-analysis

Madani, Seyedeh Panid; Shahbazian, Haneyeh; Ansari, Golnoosh; Mohseni, Alireza; Borhani, Ali; Afyouni, Shadi; Mirza-Aghazadeh-Attari, Mohammad

doi:10.1007/s00261-023-03940-y

Cited by 6 publications

(1 citation statement)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These complexities are often not extensively covered in quality assessment tools. In line with our study, meta-analyses conducted on other clinical contexts have similarly identified methodological limitations in their included studies 58 , 59 , 61 , 66 .…”

Section: Discussionsupporting

confidence: 88%

Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis

Abbaspour,

Karimzadhagh,

Monsef

et al. 2024

International Journal of Surgery

View full text Add to dashboard Cite

Background: Colorectal cancer (CRC) stands as the third most prevalent cancer globally, projecting 3.2 million new cases and 1.6 million deaths by 2040. Accurate lymph node metastasis (LNM) detection is critical for determining optimal surgical approaches, including preoperative neoadjuvant chemoradiotherapy and surgery, which significantly influence CRC prognosis. However, conventional imaging lacks adequate precision, prompting exploration into radiomics, which addresses this shortfall by converting medical images into reproducible, quantitative data. Methods: Following PRISMA, Supplemental Digital Content 1 (http://links.lww.com/JS9/C77) and Supplemental Digital Content 2 (http://links.lww.com/JS9/C78), and AMSTAR-2 guidelines, Supplemental Digital Content 3 (http://links.lww.com/JS9/C79), we systematically searched PubMed, Web of Science, Embase, Cochrane Library, and Google Scholar databases until 11 January 2024, to evaluate radiomics models’ diagnostic precision in predicting preoperative LNM in CRC patients. The quality and bias risk of the included studies were assessed using the Radiomics Quality Score (RQS) and the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Subsequently, statistical analyses were conducted. Results: Thirty-six studies encompassing 8039 patients were included, with a significant concentration in 2022–2023 (20/36). Radiomics models predicting LNM demonstrated a pooled area under the curve (AUC) of 0.814 (95% CI: 0.78–0.85), featuring sensitivity and specificity of 0.77 (95% CI: 0.69, 0.84) and 0.73 (95% CI: 0.67, 0.78), respectively. Subgroup analyses revealed similar AUCs for CT and MRI-based models, and rectal cancer models outperformed colon and colorectal cancers. Additionally, studies utilizing cross-validation, 2D segmentation, internal validation, manual segmentation, prospective design, and single-center populations tended to have higher AUCs. However, these differences were not statistically significant. Radiologists collectively achieved a pooled AUC of 0.659 (95% CI: 0.627, 0.691), significantly differing from the performance of radiomics models (P<0.001). Conclusion: Artificial intelligence-based radiomics shows promise in preoperative lymph node staging for CRC, exhibiting significant predictive performance. These findings support the integration of radiomics into clinical practice to enhance preoperative strategies in CRC management.

show abstract

Section: Discussionsupporting

confidence: 88%

Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis

Abbaspour,

Karimzadhagh,

Monsef

et al. 2024

International Journal of Surgery

View full text Add to dashboard Cite

show abstract

Radiomics and deep learning models for CT pre-operative lymph node staging in pancreatic ductal adenocarcinoma: A systematic review and meta-analysis

Castellana,

Fanni,

Roncella

et al. 2024

European Journal of Radiology

View full text Add to dashboard Cite

Pancreatic ductal adenocarcinoma staging: a narrative review of radiologic techniques and advances

Chu,

Fishman

2023

International Journal of Surgery

View full text Add to dashboard Cite

Radiology plays an important role in the initial diagnosis and staging of patients with pancreatic ductal adenocarcinoma (PDAC). CT is the preferred modality over MRI, due to wider availability, greater consistency in image quality, and lower cost. MRI and PET/CT are usually reserved as problem-solving tools in select patients. The National Comprehensive Cancer Network (NCCN) guidelines define resectability criteria based on tumor involvement of the arteries and veins, and triage patients into resectable, borderline resectable, locally advanced, and metastatic categories. Patients with resectable disease are eligible for upfront surgical resection, while patients with high-stage disease are treated with neoadjuvant chemotherapy and/or radiation therapy with hopes of downstaging the disease. The accuracy of staging critically depends on imaging technique and the experience of the radiologists. Several challenges in accurate preoperative staging include prediction of lymph node metastases, detection of subtle liver and peritoneal metastases, and disease restaging following neoadjuvant therapy. Artificial intelligence (AI) has the potential to function as “second readers” to improve upon the radiologists’ detection of small early-stage tumors, which can shift more patients toward surgical resection of potentially curable cancer. AI may also provide imaging biomarkers that can predict disease recurrence and patient survival after pancreatic resection and assist in selection of patients most likely to benefit from surgery thus improving patient outcomes.

show abstract

Predictive role of radiomics features extracted from preoperative cross-sectional imaging of pancreatic ductal adenocarcinoma in detecting lymph node metastasis: a systemic review and meta-analysis

Cited by 6 publications

References 56 publications

Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis

Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis

Radiomics and deep learning models for CT pre-operative lymph node staging in pancreatic ductal adenocarcinoma: A systematic review and meta-analysis

Pancreatic ductal adenocarcinoma staging: a narrative review of radiologic techniques and advances

Contact Info

Product

Resources

About