Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer

Toyama, Yoshitaka; Hotta, Masatoshi; Motoi, Fuyuhiko; Takanami, Kentaro; Minamimoto, Ryogo; Takase, Kei

doi:10.1038/s41598-020-73237-3

Cited by 56 publications

(55 citation statements)

References 29 publications

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“…The GLNU increases with the tumor heterogeneity, which is related to tumor invasion, treatment response and prognosis (76). As an independent risk factor for poor prognosis, high GLNU is associated with worse survival in patients with pancreatic cancer who have undergone surgery (72). Our study showed that tumors with LVI had higher GLNU values than those without LVI, while the presence of LVI implies an increase in tumor heterogeneity.…”

Section: Discussionmentioning

confidence: 55%

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Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma

Wang

Yang

et al. 2021

Front. Oncol.

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ObjectivesTo develop a radiomics model based on contrast-enhanced CT (CECT) to predict the lymphovascular invasion (LVI) in esophageal squamous cell carcinoma (ESCC) and provide decision-making support for clinicians.Patients and MethodsThis retrospective study enrolled 334 patients with surgically resected and pathologically confirmed ESCC, including 96 patients with LVI and 238 patients without LVI. All enrolled patients were randomly divided into a training cohort and a testing cohort at a ratio of 7:3, with the training cohort containing 234 patients (68 patients with LVI and 166 without LVI) and the testing cohort containing 100 patients (28 patients with LVI and 72 without LVI). All patients underwent preoperative CECT scans within 2 weeks before operation. Quantitative radiomics features were extracted from CECT images, and the least absolute shrinkage and selection operator (LASSO) method was applied to select radiomics features. Logistic regression (Logistic), support vector machine (SVM), and decision tree (Tree) methods were separately used to establish radiomics models to predict the LVI status in ESCC, and the best model was selected to calculate Radscore, which combined with two clinical CT predictors to build a combined model. The clinical model was also developed by using logistic regression. The receiver characteristic curve (ROC) and decision curve (DCA) analysis were used to evaluate the model performance in predicting the LVI status in ESCC.ResultsIn the radiomics model, Sphericity and gray-level non-uniformity (GLNU) were the most significant radiomics features for predicting LVI. In the clinical model, the maximum tumor thickness based on CECT (cThick) in patients with LVI was significantly greater than that in patients without LVI (P<0.001). Patients with LVI had higher clinical N stage based on CECT (cN stage) than patients without LVI (P<0.001). The ROC analysis showed that both the radiomics model (AUC values were 0.847 and 0.826 in the training and testing cohort, respectively) and the combined model (0.876 and 0.867, respectively) performed better than the clinical model (0.775 and 0.798, respectively), with the combined model exhibiting the best performance.ConclusionsThe combined model incorporating radiomics features and clinical CT predictors may potentially predict the LVI status in ESCC and provide support for clinical treatment decisions.

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Section: Discussionmentioning

confidence: 55%

“…In our study, GLNU was another independent predictor for LVI. Gray-level non-uniformity (GLNU) is a measure of the similarity of gray-level values throughout the image (72). Many radiomics features are unstable in different reconstruction algorithms, while GLNU is one of the most repetitive radiomics features showing good stability (73).…”

Section: Discussionmentioning

confidence: 99%

Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma

Wang

Yang

et al. 2021

Front. Oncol.

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“…We collected 47 publications on “Other” cancer types. This category included studies on gastrointestinal (11) [ 257 , 258 , 259 , 260 , 261 , 262 , 263 , 264 , 265 , 266 , 267 ], pancreatic (8) [ 268 , 269 , 270 , 271 , 272 , 273 , 274 , 275 ], sarcoma (8) [ 276 , 277 , 278 , 279 , 280 , 281 , 282 , 283 ], neuroendocrine (5) [ 284 , 285 , 286 , 287 , 288 ], prostate (4) [ 289 , 290 , 291 , 292 ], thyroid (3) [ 293 , 294 , 295 ], thymic (2) [ 296 , 297 ], skin (2) [ 298 , 299 ], liver (2) [ 300 , 301 ], and renal carcinomas (1) [ 302 ]. The average number of patients was 84 (median = 70, range, 26–214) and the average number of textural features extracted was 29 (median = 17, ...…”

Section: Resultsmentioning

confidence: 99%

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

et al. 2021

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Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

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“…Compared with most previous texture analysis studies in PDAC that were based on CT imaging [ 15 – 17 , 39 , 40 ], we performed radiomics analysis based on MRI, which has a higher resolution in soft tissue than CT. Although based on different examination techniques, the selected features are mostly related to intratumoural heterogeneity [ 32 , 37 , 38 ]. However, performances have not yet been compared among CT, MRI, and PET/CT [ 41 , 42 ].…”

Section: Discussionmentioning

confidence: 99%

Whole-tumour evaluation with MRI and radiomics features to predict the efficacy of S-1 for adjuvant chemotherapy in postoperative pancreatic cancer patients: a pilot study

Liang

Ding

et al. 2021

BMC Med Imaging

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Background Multiple guidelines for pancreatic ductal adenocarcinoma (PDAC) suggest that all stages of patients need to receive postoperative adjuvant chemotherapy. S-1 is a recently emerged oral antitumour agent recommended by the guidelines. However, which population would benefit from S-1 needs to be determined, and predictors of chemotherapy response are needed for personalized precision medicine. This pilot study aimed to initially identify whether whole-tumour evaluation with MRI and radiomics features could be used for predicting the efficacy of S-1 and to find potential predictors of the efficacy of S-1 as evidence to assist personalized precision treatment. Methods Forty-six patients with PDAC (31 in the primary cohort and 15 in the validation cohort) who underwent curative resection and subsequently adjuvant chemotherapy with S-1 were included. Pre-operative abdominal contrast-enhanced MRI was performed, and radiomics features of the whole PDAC were extracted from the primary cohort. After univariable analysis and radiomics features selection, a multivariable Cox regression model for survival analysis was subsequently used to select statistically significant factors associated with postoperative disease-free survival (DFS). Predictive capacities of the factors were tested on the validation cohort by using Kaplan–Meier method. Results Multivariable Cox regression analysis identified the probability of T1WI_NGTDM_Strength and tumour location as independent predictors of the efficacy of S-1 for adjuvant chemotherapy of PDAC (p = 0.005 and 0.013) in the primary cohort, with hazard ratios (HRs) of 0.289 and 0.293, respectively. Further survival analysis showed that patients in the low-T1WI_NGTDM_Strength group had shorter DFS (median = 5.1 m) than those in the high-T1WI_NGTDM_Strength group (median = 13.0 m) (p = 0.006), and patients with PDAC on the pancreatic head exhibited shorter DFS (median = 7.0 m) than patients with tumours in other locations (median = 20.0 m) (p = 0.016). In the validation cohort, the difference in DFS between patients with low-T1WI_NGTDM_Strength and high-T1WI_NGTDM_Strength and the difference between patients with PDAC on the pancreatic head and that in other locations were approved, with marginally significant (p = 0.073 and 0.050), respectively. Conclusions Whole-tumour radiomics feature of T1WI_NGTDM_Strength and tumour location were potential predictors of the efficacy of S-1 and for the precision selection of S-1 as adjuvant chemotherapy regimen for PDAC.

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Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer

Cited by 56 publications

References 29 publications

Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma

Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Whole-tumour evaluation with MRI and radiomics features to predict the efficacy of S-1 for adjuvant chemotherapy in postoperative pancreatic cancer patients: a pilot study

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