Training and validation of a robust PET radiomic-based index to predict distant-relapse-free-survival after radio-chemotherapy for locally advanced pancreatic cancer

Mori, Martina; Passoni, P.; Incerti, Elena; Bettinardi, Valentino; Broggi, S.; Reni, Michele; Whybra, Philip; Spezi, Emiliano; Vanoli, Elena G.; Gianolli, Luigi; Picchio, Maria; Muzio, N.G. Di; Fiorino, Claudio

doi:10.1016/j.radonc.2020.07.003

Cited by 21 publications

(23 citation statements)

References 35 publications

(45 reference statements)

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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, and the average number of textural features extracted was 29 (median = 17, range, 1-236).…”

Section: Other Cancersmentioning

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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Section: Other Cancersmentioning

confidence: 99%

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

et al. 2021

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“…Depending on where the tail is elongated and the mass of the distribution is concentrated, this value can be positive or negative. Fiorino et al (25) established a radiomic-based index with good performance derived from 18 F-FDG PET/CT radiomics features to predict distant RFS in 176 patients with locally advanced pancreatic cancer treated with radio-chemotherapy. In their study, one-hundred-ninety-eight radiomic features were extracted, and only two robust features were included, Morpholoigical_COMshift and Statistical_P 10 .…”

Section: Discussionmentioning

confidence: 99%

A Novel Validated Recurrence Stratification System Based on 18F-FDG PET/CT Radiomics to Guide Surveillance After Resection of Pancreatic Cancer

Wei

Song

et al. 2021

Front. Oncol.

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objectiveDespite the heterogeneous biology of pancreatic cancer, similar surveillance schemas have been used. Identifying the high recurrence risk population and conducting prompt intervention may improve prognosis and prolong overall survival.MethodsOne hundred fifty-six resectable pancreatic cancer patients who had undergone 18F-FDG PET/CT from January 2013 to December 2018 were retrospectively reviewed. The patients were categorized into a training cohort (n = 109) and a validation cohort (n = 47). LIFEx software was used to extract radiomic features from PET/CT. The risk stratification system was based on predictive factors for recurrence, and the index of prediction accuracy was used to reflect both the discrimination and calibration.ResultsOverall, seven risk factors comprising the rad-score and clinical variables that were significantly correlated with relapse were incorporated into the final risk stratification system. The 1-year recurrence-free survival differed significantly among the low-, intermediate-, and high-risk groups (85.5, 24.0, and 9.1%, respectively; p < 0.0001). The C-index of the risk stratification system in the development cohort was 0.890 (95% CI, 0.835–0.945).ConclusionThe 18F-FDG PET/CT-based radiomic features and clinicopathological factors demonstrated good performance in predicting recurrence after pancreatectomy in pancreatic cancer patients, providing a strong recommendation for an adequate adjuvant therapy course in all patients. The high-risk recurrence population should proceed with closer follow-up in a clinical setting.

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“…In addition, the intraclass correlation coefficient (ICC) Fig. 1 Overall scheme served to evaluate the agreement between the values of multiple features, and feature selection was performed with ICC > 0.8 [24,30]. In this study, we used 23 radiomic features selected in a previous study by Kadoya et al, who already performed test-retest and multiple segmentation using the abovementioned dataset and method [24].…”

Section: Feature Selectionmentioning

confidence: 99%

Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients

et al. 2021

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Background Radiomics is a new technology to noninvasively predict survival prognosis with quantitative features extracted from medical images. Most radiomics-based prognostic studies of non-small-cell lung cancer (NSCLC) patients have used mixed datasets of different subgroups. Therefore, we investigated the radiomics-based survival prediction of NSCLC patients by focusing on subgroups with identical characteristics. Methods A total of 304 NSCLC (Stages I–IV) patients treated with radiotherapy in our hospital were used. We extracted 107 radiomic features (i.e., 14 shape features, 18 first-order statistical features, and 75 texture features) from the gross tumor volume drawn on the free breathing planning computed tomography image. Three feature selection methods [i.e., test–retest and multiple segmentation (FS1), Pearson's correlation analysis (FS2), and a method that combined FS1 and FS2 (FS3)] were used to clarify how they affect survival prediction performance. Subgroup analysis for each histological subtype and each T stage applied the best selection method for the analysis of All data. We used a least absolute shrinkage and selection operator Cox regression model for all analyses and evaluated prognostic performance using the concordance-index (C-index) and the Kaplan–Meier method. For subgroup analysis, fivefold cross-validation was applied to ensure model reliability. Results In the analysis of All data, the C-index for the test dataset is 0.62 (FS1), 0.63 (FS2), and 0.62 (FS3). The subgroup analysis indicated that the prediction model based on specific histological subtypes and T stages had a higher C-index for the test dataset than that based on All data (All data, 0.64 vs. SCCall, 060; ADCall, 0.69; T1, 0.68; T2, 0.65; T3, 0.66; T4, 0.70). In addition, the prediction models unified for each T stage in histological subtype showed a different trend in the C-index for the test dataset between ADC-related and SCC-related models (ADCT1–ADCT4, 0.72–0.83; SCCT1–SCCT4, 0.58–0.71). Conclusions Our results showed that feature selection methods moderately affected the survival prediction performance. In addition, prediction models based on specific subgroups may improve the prediction performance. These results may prove useful for determining the optimal radiomics-based predication model.

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Training and validation of a robust PET radiomic-based index to predict distant-relapse-free-survival after radio-chemotherapy for locally advanced pancreatic cancer

Cited by 21 publications

References 35 publications

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

A Novel Validated Recurrence Stratification System Based on 18F-FDG PET/CT Radiomics to Guide Surveillance After Resection of Pancreatic Cancer

Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients

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