Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study
Shunli LiuWeikai SunShifeng YangLisha DuanChencui HuangJingxu XuFeng HouDapeng HaoTengbo YuHexiang Wang
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Neuroradiology
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A radiomics nomogram for preoperatively predicting prognosis of patients in hepatocellular carcinoma
Background: Increasing studies have indicated that biomarkers based on quantitative radiomics features are related to clinical prognosis across a range of cancer types, but the association between radiomics and prognosis in hepatocellular carcinoma (HCC) is unclear. We aimed to develop and validate a radiomics nomogram for the preoperative prediction of prognosis for patients with HCC undergoing partial hepatectomy. Methods: In total, 177 patients were randomly divided into training (n=113) and validation (n=64) cohorts. A total number of 980 radiomics features were extracted from computed tomography images. And the least absolute shrinkage and selection operator algorithm was used to select the optimal features and build a radiomics signature in the training set. Besides, a radiomics nomogram was developed using multivariate regression analysis. The performance of the radiomics nomogram was estimated regarding its discrimination and calibration abilities, and clinical usefulness. Results: The radiomics signature was significantly associated with disease-free survival (DFS) (P Conclusions: The proposed radiomics nomogram showed excellent performance for the individualized and non-invasive estimation of DFS, which may help clinicians better identify patients with HBV-related HCC who can benefit from the surgery.
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• The novel radiomics nomogram developed based on Gd-EOB-DTPA MRI achieved preoperative non-invasive MVI risk prediction.• An m-score based on the radiomics nomogram could stratify HCC patients and further identify individuals who may benefit from the PA-TACE.• The radiomics nomogram could help clinicians to implement more appropriate interventions and perform individualized precision therapies.
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Soft tissue sarcomas (STSs) are common cutaneous or subcutaneous neoplasms in dogs. Most STSs are initially treated by surgical excision, and local recurrence may develop in almost 20% of patients. Currently, it is difficult to predict which STS will recur after excision, but this ability would greatly assist patient management. In recent years, the nomogram has emerged as a tool to allow oncologists to predict an outcome from a combination of risk factors. The aim of this study was to develop a nomogram for canine STSs and determine if the nomogram could predict patient outcomes better than individual tumour characteristics. The current study provides the first evidence in veterinary oncology to support a role for the nomogram to assist with predicting the outcome for patients after surgery for STSs. The nomogram developed in this study accurately predicted tumour-free survival in 25 patients but failed to predict recurrence in 1 patient. Overall, the sensitivity, specificity, positive predictive, and negative predictive values for the nomogram were 96%, 45%, 45%, and 96%, respectively (area under the curve: AUC = 0.84). This study suggests a nomogram could play an important role in helping to identify patients who could benefit from revision surgery or adjuvant therapy for an STS.
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Abstract BACKGROUND A nomogram for predicting long term tumor‐specific death in patients with soft tissue sarcoma (STS) was developed at the Memorial Sloan‐Kettering Cancer Center (MSKCC). METHODS To assess the performance of the MSKCC nomogram, 642 consecutive patients with extremity STS who underwent surgery over a 20‐year span at a single referral center were analyzed. Nomogram predictions were based on tumor size, depth, site, patient age, histologic subtype, and grade. The latter, at variance with the system in use at the MSKCC, was classified as Grade 1–3 according to the French Federation of Cancer Centers Sarcoma Group (FNCLCC) system. The statistical approach used for nomogram performance assessment was that of “validation by calibration” proposed by Van Houwelingen. RESULTS Graphic comparison of observed and predicted sarcoma‐specific survival curves showed that predictions by the nomogram were quite accurate, within 10% of actual survival for all prognostic strata. Statistical analysis showed that such predictions could be improved by employing approximately 25% shrinkage to achieve good calibration. The contribution of histologic grade was highly significant in both univariate analysis ( P < 0.001) and multivariate analysis ( P < 0.001), and a survival trend across the 3 grade categories was observed. Based on those findings, a nomogram that included the FNCLCC histologic grade classification was produced. CONCLUSIONS Results of the current study confirmed that the MSKCC nomogram is a valuable tool for individual prognostic assessment. A nomogram that included the FNCLCC histologic grade classification was proposed and was validated internally. Cancer 2005. © 2004 American Cancer Society.
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