A computed tomography-based radiomic score to predict survival in patients with adrenocortical carcinoma

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Moawad, Philippe Soyer, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3910331/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Adrenocortical carcinoma (ACC) is a rare condition with a poor and hardly predictable prognosis. This study aims to build and evaluate a preoperative computed tomography (CT)-based radiomic score (Radscore) using features previously reported as biomarkers in adrenocortical carcinoma (ACC) to predict overall survival (OS) in patients with ACC. Methods: In this retrospective study, a Radscore based on preoperative CT examinations combining shape elongation, tumor maximal diameter, and the European Network for the Study of Adrenal Tumors (ENSAT) stage and was built using a logistic regression model to predict OS duration in a development cohort. An optimal cut-off of the Radscore was defined and the Kaplan-Meier method was used to assess OS. The Radscore was then tested in an external validation cohort. The C-index of the Radscore for the prediction of OS was compared to that of ENSAT stage alone. Findings : The Radscore was able to discriminate between patients with poor prognosis and patients with good prognosis in both the the validation cohort (54 patients; mean OS, 69·4 months; 95% CI: 57·4–81·4 months vs. mean OS, 75·6 months; 95% CI: 62·9–88·4 months, respectively; P = 0·022). In the validation cohort the C-index of the Radscore was significantly better than that of the ENSAT stage alone (0.62 vs. 0.35; P = 0·002). Conclusion: A Radscore combining morphological criteria, radiomics, and ENSAT stage on preoperative CT examinations allow a stratification of prognosis in patients with ACC compared with ENSAT stage alone. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Adrenocortical carcinoma (ACC) is a rare condition with an estimated incidence of 0·5–2 cases per million of inhabitants per year, and it accounts for 0·04–0·2% of all cancer deaths in the US ( 1 , 2 ). For patients with ACC, the prognosis is generally poor; the 5-year overall survival (OS) rate is only about 40%. However, OS varies considerably between patients ( 3 ). In patients with ACC, OS prognostic factors include clinical, histopathological, and molecular features; these factors could help determine appropriate management on the basis of patient prognosis ( 4 ), but histopathological and molecular information are obtained at the penalty of invasive tissue sampling that is not free of morbidity ( 5 ) and carries a risk of tumor dissemination ( 6 ). Therefore, adrenal biopsy is not recommended in the setting of ACC ( 7 ). Among the histopathological factors that have demonstrated prognostic capabilities is the Ki-67 index ( 8 ). A Ki-67 index > 10% is regarded as a marker of high risk of recurrence, which justifies the use of adjuvant chemotherapy in these patients ( 8 ). Preoperative prognosis of patients with ACC is usually assessed using the European Network for the Study of Adrenal Tumors (ENSAT) staging system, which is based on adrenal magnetic resonance imaging (MRI) and chest abdominal and pelvic computed tomography (CT) examinations ( 8 ). Conventional imaging alone is not sufficient for the diagnosis, classification, or estimation of prognosis of patients with ACC ( 9 ); therefore, the ENSAT classification takes into account tumor size, infiltration of surrounding adipose tissue, invasion of adjacent organs, positive lymph nodes, and distant metastases ( 8 ). It provides a stratification strongly associated with cancer-specific mortality; the 5-year OS is > 60% for patients with ENSAT stage I or II, < 50% for patients with stage III, and 20% for patients with stage IV ACC ( 10 , 11 ). However, the high heterogeneity of OS especially in patients with ENSAT stage I to III may require improvements. Transcriptomics, which consists of an unsupervised high-throughput analysis of tumor genome expression profiling ( 12 ), can also be used to predict outcomes in patients with ACC. This analysis is performed on histopathological tissue samples, and findings correlate with patient disease-free survival (DFS) and OS ( 13 ). In ACC, transcriptomics identifies two genomic profiles that correspond to two groups of patients with different outcomes ( 13 ). The C1A profile is associated with a poor outcome, with a 5-year OS rate approaching 0%, whereas the C1B profile is associated with a 5-year OS greater than 90% ( 13 ). However, this analysis requires invasive tissue sampling with a potential associated morbidity. To improve the preoperative and non-invasive evaluation of patients with ACC, the radiomic approach has been considered ( 8 ). Radiomics is the analysis of mathematically derived textural features on radiologic imaging ( 14 ). Radiomics has demonstrated capabilities in the field of adrenal lesion characterization ( 15 – 17 ) and can also be used to predict OS and DFS in other cancers ( 18 , 19 ). One study specifically evaluated the capabilities of radiomics for estimating Ki-67 index in ACC ( 20 ). That study found that two shape-based features ( i.e. , shape elongation [SE] and shape flatness [SF]) were predictive of Ki-67 index > 10% ( 20 ). SE > 0·668 was a predictor of a Ki-67 index > 10% with sensitivity, specificity, and positive likelihood and negative likelihood ratios of 75%, 69·2%, 2·4, and 0·36, respectively. SF > 0.4966 was a predictor of a Ki-67 index > 10% with sensitivity, specificity, and positive likelihood and negative likelihood ratios of 80%, 69·2%, 2·5, and 0·28, respectively. However, the study did not evaluate to what extent those features could be used as predictors of patient survival ( 20 ). The aim of the current study was to evaluate the performance of a preoperative computed tomography (CT)-based radiomic prognostic score using features previously reported as predictors of high Ki-67 index ( i.e. , Ki-67 index > 10%) in ACC to predict OS in patients with this condition. 2. Materials and Methods 2.1. Patients This retrospective study was approved by the institutional review board of Center 1 (N°: AAA-2020-08048), and the requirement for written informed consent was waived. The database of the department of pathology of our institution (Center 1) was queried to identify all patients who underwent adrenalectomy from January 2007 through December 2022. This initial search retrieved 708 patient records. Patients were included in the study if they were older than 18 years, had a histopathologically proven diagnosis of ACC, and had a preoperative CT examination performed less than 3 months before surgery that was available for review. Patients with adrenal tumors other than ACC (n = 599), patients with collision tumors (n = 1), and those without a preoperative CT examination (n = 19) were excluded. Figure 1 shows the study flow chart of patients who were considered for this study. Patients’ clinical and histopathological data were recorded, including age, sex, tumor secretion, tumor largest diameter, tumor side, Ki-67 index, Weiss pathological score, and ENSAT stage. The characteristics of patients in the development cohort are summarized in Table 1 . Table 1 Characteristics of 143 patients with adrenocortical carcinomas used for the development (n = 89) and the validation (n = 54) cohorts Variable Development cohort (n = 89) Validation cohort (n = 54) P value* Male 26/89 (29%) 22/54 (41%) 0·20 Right-sided ACC 47/89 (53%) 21/54 (39%) 0·11 Secretion 56/89 (63%) 26/54 (48%) 0·08 ENSAT Stage 0·08 1 10/89 (11%) 4/54 (7.4%) 2 45/89 (51%) 19/54 (35%) 3 19/89 (21%) 22/54 (41%) 4 15/89 (17%) 9/54 (17%) Ki67 rate (%) 22 ± 19 [0–95] 25 ± 17 [2–79] 0·07 Weiss Score 6·53 ± 1·96 [3·00–9·00] 5·83 ± 1·57 [3·00–9·00] 0·06 Follow up (months) 51 ± 46 [1–176] 69 ± 42 [7–206] < 0 · 01 Death 28/89 (31%) 29/54 (54%) < 0 · 01 Shape elongation 0·80 ± 0·11 [0.54–0.97] 0.66 ± 0·25 [0·01–0·96] < 0 · 01 Largest ACC diameter (mm) 82 ± 45 [10–219] 116 ± 80 [18–368] 0 · 03 Qualitative variables are expressed as proportions followed by percentages into parentheses; Quantitative variables are expressed as means ± standard deviation followed by ranges into brackets. ACC indicates adrenocortical carcinoma; ENSAT indicates European Network for the Study of Adrenal Tumors. * comparison between building and validation cohorts. 2.2. CT data acquisition Patients’ CT images were acquired from different CT-scans. One radiologist blinded to the clinical outcomes (M.B. with ten years of experience in abdominal imaging) reviewed all CT examinations after anonymization on a picture-archiving and communication system viewing station (DirectView®, 11.4.0.1253 sp1 version, Carestream Health, Rochester, NY, USA). CT examinations had been performed with various helical CT units including Revolution HD® (General-Electric Healthcare, Wauwatosa, WI, USA), Somatom Sensation® 64 (Siemens Healthineers, Erlangen, Germany), or Somatom Definition® Flash (Siemens Healthineers). Acquisition parameters were as follows: field-of-view, 279–350 mm; number of detector rows, 16–64; acquisition slice thickness, 0·6–1·25 mm; peak tube potential, 110–120 kVp; gantry revolution time, 0·5 − 0·7 s; and reconstruction slice thickness, 1–3 mm. Iodinated contrast material (iomeprol, Iomeron 350®, Bracco Imaging, Milan, Italy, or iobitridol, Xenetix 350®, Guerbet, Aulnay-sous-Bois, France) was injected intravenously with an automated power injector (rate, 2·5–4 mL/s; total volume, 90–130 mL). CT examinations covered the thorax, abdomen, and pelvis and were obtained before and after intravenous administration of iodinated contrast material during venous (60 s) and delayed (10 min) phases. 2.3. Data analysis The morphological characteristics of the adrenal lesions, including tumor maximal diameter measured in the axial plane, vascular involvement, and all criteria of the ENSAT stage, were determined by M.B. on the basis of review of the CT examinations. Three-dimensional segmentation of CT images of the whole adrenal lesion was performed using the ITKsnap v1.0.0rc2 module of the 3D-Slicer® software ( https://www.slicer.org ) by a radiologist with five years of experience in image segmentation. Shape radiomic features were extracted from CT data obtained during the venous phase of enhancement using the PyRadiomic ( http://www.radiomics.io/pyradiomics.html ) module of the same software after normalization of the voxel size at 1 × 1 × 1 mm 3 ( 21 ). A total of 12 shape radiomic features were extracted ( 21 ). These features were analyzed without any pre-processing step after extraction. 2.4 Validation cohort For the validation cohort, 54 patients from another center (Center 2) were retrospectively identified from a previously published study ( 20 ). Similar inclusion and exclusion criterions were used. The patients’ medical records were retrospectively analyzed, and age, sex, Ki-67 index, ENSAT stage, shape elongation, occurrence of death, and duration of follow-up were recorded. In this cohort, preoperative CT images were obtained in the venous phase (60–80 s after intravenous contrast medium injection). Some of the scans included in the present study were performed at outside facilities. The scans were acquired on 64-multidetector CT Light-Speed scanners (GE Healthcare, Waukesha, WI, USA) with a section thickness of 2·5 mm and an injection rate of 3–5 ml/s ( 20 ). 2. 5. Statistical analysis Statistical analysis was performed using R software (version 4.1.0, R-foundation, http://www.r-project . org/). Quantitative (continuous) variables were reported as means ± standard deviations (SD) and ranges. Qualitative (binary) variables were reported as raw numbers, proportions, and percentages. Continuous variables were compared using the Student t -test and categorical variables using the χ2 test or Fisher exact test, as appropriate ( 22 ). Thirty-five ACC images were randomly selected from the development cohort for the evaluation of interrater agreement. A radiologist (M.G.) with ten years of experience in abdominal imaging performed new segmentations for the selected ACC images while blinded to the segmentation results of the previous radiologist. The Dice similarity coefficient (DSC) was calculated for the evaluation of interobserver reproducibility of the segmentation methods for each ACC ( 23 ). Intraclass correlation coefficients (ICCs) were calculated for each radiomic feature using a two-way mixed-effect model ( 24 ). ICCs between 0·00 and 0·20; 0·21 and 0·40; 0·41 and 0·60; 0·61 and 0·80; and 0·81 and 1·00 indicated slight, fair, moderate, substantial, and almost perfect agreement, respectively ( 25 ). Features with ICC 0·9 in our cohort were evaluated for their ability to predict Ki-67 index > 10% using previously published cut-off values ( 20 ). A receiver operating characteristic (ROC) curve was built, and areas under the curve (AUROCs) for the prediction of Ki-67 index > 10% were calculated for each feature with an ICC < 0·9. Then, in order to improve patient stratification, a multivariable score using a logistic regression method was developed based on the development cohort. Tumor maximal diameter, patient ENSAT stage, and selected radiomic features as described above were included ( 20 ). The score was tested for its ability to predict patients’ OS using a ROC curve. The optimal cut-off value, defined as the value of features that yielded the best accuracy for the diagnosis of a high Ki-67 index as identified by ROC curve analysis, was used to split the cohort into patients with a good prognosis ( i.e. , OS > 24 months) and those with a poor prognosis ( i.e. , OS < 24 months). Patients’ OS was estimated by using the Kaplan–Meier method. The log-rank test was used to compare OS between patients with good prognosis and those with poor prognosis. The radiomic score was then tested in the validation cohort using the same method. Performance of the radiomic score for the prediction of OS was assessed using Harrel’s C-index and compared to the ENSAT stage alone in the validation cohort using the Student t -test for the comparison of C-indexes. All tests were two-tailed, and significance was set at P < 0·05. 3. Results Eighty-nine patients were included in the development cohort and 54 patients in the validation cohort. Characteristics of the patients are summarized in Table 1 . Segmentation and extraction of radiomic features was possible for all ACC images. The reproducibility of segmentation was considered almost perfect for 34/35 (97%) ACCs with a mean DSC of 0·92 ± 0·03 (SD) (range: 0·72–0·97). SF had an ICC < 0·9 (ICC = 0·85) and was excluded from the analysis. Therefore, the only radiomic feature used for analysis was SE. In the development cohort, the Ki-67 index was available for 77 of 89 patients (87%). The AUROC for the prediction of Ki-67 index > 10% was 0·64 (95% CI: 0·54–0·76). The Radscore developed in the development cohort included SE, tumor maximal diameter, and the ENSAT stage evaluated using pre-treatment CT examination. The AUROC for the prediction of patients’ OS was 0·779, and the best cut-off value to split the development cohort between patients with an OS > 24 months and those with an OS < 24 months was 0·562 (Fig. 2 ). The Kaplan Meier analysis in the development cohort yielded a mean OS of 27·1 months (95% CI: 13·7–40·1 months) in patients with poor prognosis and 63.3 months (95% CI: 50·1–76·5 months) in those with good prognosis (P < 0·001) (Fig. 3 ). In the validation cohort, the Radscore was able to discriminate between patients with poor prognosis (mean OS, 69·4 months; 95% CI: 57·4–81·4 months) and those with good prognosis (mean OS, 75·6 months; 95% CI: 62·9–88·4 months) (P = 0·022) (Fig. 4 ). The C-index of the Radscore was significantly better than that of the ENSAT stage alone (0·62 vs. 0·35, respectively; P = 0·002) in the validation cohort (Fig. 5 ). 4. Discussion This retrospective two-center study reveals that a Radscore including SE, tumor maximal diameter, and ENSAT stage helps distinguish between patients with ACC who have a good prognosis and those with poor prognosis in an external validation cohort. As in the study by Ahmed et al., tumor segmentation, radiomic feature extraction, and analysis were performed using CT data obtained from portal venous phase images only ( 20 ). For most patients with suspected adrenal disease, CT is the first-line imaging modality for adrenal lesion characterization, whereas MRI is used primarily for specific indications ( 27 , 28 ). In addition, the portal venous phase is the most frequently performed acquisition phase for oncologic staging, and results in homogeneous contrast agent distribution, which can potentially improve features extraction reproducibility. SE is a radiomic feature consisting of the ratio between the minor axis length and the major axis length of the tumor ( 21 ). SE on preoperative imaging was found to be correlated with a Ki-67 index cut-off value of 10% ( 20 ). The combination of SE with existing prognostic factors on preoperative imaging ( i.e. , ENSAT stage and maximal diameter) may improve patient care by providing accurate risk stratification and enabling aggressive treatment, if needed, immediately after diagnosis. Moreover, using a manual segmentation method, SE extraction was highly reproducible, with an ICC greater than 0·9 ( 24 ). Shape-based radiomic features have demonstrated utility for tumor characterization in oncological imaging and for patient prognostic stratification ( 29 ). Moreover, high interobserver reproducibility between radiologists has been reported ( 29 ). A shape-based classification using three features (roughness, convexity, and sphericity) proved to be able to discriminate between lung granuloma and lung carcinoma with an AUROC of 0·72, which was equivalent to that of expert radiologists ( 29 ). Similarly, Alvarez-Jimenez, et al. showed that first-order and shape-based radiomic features measured on T2-weighted MRI images of the rectal wall and peritumoral environment of rectal cancers after neoadjuvant radio-chemotherapy were effective for the restaging of rectal tumors before surgery ( 30 ). In that study, the radiomic features were associated with tumor grade after radio-chemotherapy with an accuracy of 69% for the detection of ypT0-2 versus ypT3-4 tumors using data from 52 patients; in an external validation cohort of 42 patients, the detection accuracy was 62% ( 30 ). Currently, adjuvant treatments such as mitotane are indicated in patients with ACC at high risk of recurrence based on surgical and histopathological findings including completeness of the resection, tumor grade, and Ki-67 index ( 8 ). When indicated, adjuvant treatment must be started as soon as possible ( 8 ). Our finding may help improve patient care by proposing a more aggressive surgery or an early adjuvant therapy for patients with a poor prognosis. Our study has some limitations. First, despite a two-center design, the number of patients remains limited, mostly because of the rarity of ACC, which makes the performance of large studies difficult. However, our study includes a relatively large number of patients compared to previous published studies, as well as an external validation cohort ( 20 ). Second, the retrospective design of the study may induce a selection bias, but patients with this rare disease are mostly treated in reference centers that belong to the COMETE-cancer network, so their treatment is largely homogeneous ( 31 ). Finally, the retrospective design also limits OS analysis because a subset of patients was lost to follow-up. 5. Conclusion We built a Radscore based on preoperative CT images that is able to discriminate between ACC patients with good and poor prognosis in an external validation cohort. These results may improve the perioperative management and risk stratification of patients with ACC but require confirmation in prospective studies. Abbreviations ACC: Adrenocortical carcinoma AUC: Area under receiver operating characteristic curve CI: Confidence interval DFS: Disease-free survival DSC: Dice similarity coefficient ENSAT: European Network for the Study of Adrenal Tumors ICC: Intraclass correlation coefficient OS: Overall survival SE: Shape elongation SF: Shape flatness Declarations Disclosures: none Financial support: none Ethics approval and consent to participate This retrospective study was approved by the institutional review board of Center 1 (N°: AAA-2020-08048), and the requirement for written informed consent was waived. Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests in relation with this study Funding No external founding. Authors' contributions M.B, P.S, G.A, C.H, J.B and A.D conceived the study, extract data from the training cohort, built the rad score and wrote the manuscript. M.E, A.W.M, D.F, A.A.A, M.A.S, M.M.E, M.A.H and K.M.E provided data for the validation set. M.G performed segmentations for the reproducibility A.J and M.H overviewed the study data analysis. All authors read and approved the final manuscript Acknowledgements: Maxime Barat received a grant in support for its PhD from the Société Française de Radiologie and the Servier Institute. References Souteiro P, Donato S, Costa C, Pereira CA, Simões-Pereira J, Oliveira J, et al. 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Moawad","email":"","orcid":"","institution":"Mercy Catholic Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"W.","lastName":"Moawad","suffix":""},{"id":270462446,"identity":"84251b8b-0601-4b18-be29-730cd77e471b","order_by":3,"name":"Philippe Soyer","email":"","orcid":"","institution":"Hôpital Cochin, AP-HP","correspondingAuthor":false,"prefix":"","firstName":"Philippe","middleName":"","lastName":"Soyer","suffix":""},{"id":270462447,"identity":"bf534304-b623-450e-8595-20cba4c37dfd","order_by":4,"name":"David Fuentes","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Fuentes","suffix":""},{"id":270462448,"identity":"1e876046-46b3-4531-a497-dd331f91b542","order_by":5,"name":"Marianne Golse","email":"","orcid":"","institution":"Hôpital Cochin, AP-HP","correspondingAuthor":false,"prefix":"","firstName":"Marianne","middleName":"","lastName":"Golse","suffix":""},{"id":270462449,"identity":"4e48fd6d-819e-4be4-a7f7-f86ea68fff5c","order_by":6,"name":"Anne Jouinot","email":"","orcid":"","institution":"Université Paris Cité","correspondingAuthor":false,"prefix":"","firstName":"Anne","middleName":"","lastName":"Jouinot","suffix":""},{"id":270462450,"identity":"120f5acf-4367-4a33-a2a2-aeb1a81e9f22","order_by":7,"name":"Ayahallah A. Ahmed","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Ayahallah","middleName":"A.","lastName":"Ahmed","suffix":""},{"id":270462451,"identity":"182bcd94-0fdb-4b92-a528-9002114808a4","order_by":8,"name":"Mostafa A. Shehata","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Mostafa","middleName":"A.","lastName":"Shehata","suffix":""},{"id":270462452,"identity":"cda90732-306f-47d9-af39-c6cc9c8aaf2e","order_by":9,"name":"Guillaume Assié","email":"","orcid":"","institution":"Université Paris Cité","correspondingAuthor":false,"prefix":"","firstName":"Guillaume","middleName":"","lastName":"Assié","suffix":""},{"id":270462453,"identity":"b0106f96-f56d-4d4f-922f-4eda98341b9d","order_by":10,"name":"Mohab M. Elmohr","email":"","orcid":"","institution":"Baylor College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mohab","middleName":"M.","lastName":"Elmohr","suffix":""},{"id":270462454,"identity":"f0d62aa4-3db8-4e0e-8ebf-b657e4574f7a","order_by":11,"name":"Magalie Haissaguerre","email":"","orcid":"","institution":"Universitaire Hôpital de Bordeaux","correspondingAuthor":false,"prefix":"","firstName":"Magalie","middleName":"","lastName":"Haissaguerre","suffix":""},{"id":270462455,"identity":"bb543bff-d662-42a1-a752-f88a832558c0","order_by":12,"name":"Mouhammed A. Habra","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Mouhammed","middleName":"A.","lastName":"Habra","suffix":""},{"id":270462456,"identity":"53e78954-473a-46ac-8f16-8496b653bb15","order_by":13,"name":"Christine Hoeffel","email":"","orcid":"","institution":"Hôpital Robert Debré, Université Champagne-Ardennes","correspondingAuthor":false,"prefix":"","firstName":"Christine","middleName":"","lastName":"Hoeffel","suffix":""},{"id":270462457,"identity":"1eb7825a-5d0e-4eba-b40b-d9818e5e2699","order_by":14,"name":"Khaled M. Elsayes","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Khaled","middleName":"M.","lastName":"Elsayes","suffix":""},{"id":270462458,"identity":"c609741a-9fc7-4dc2-9eb2-ba03deb99fb3","order_by":15,"name":"Jérome Bertherat","email":"","orcid":"","institution":"Université Paris Cité","correspondingAuthor":false,"prefix":"","firstName":"Jérome","middleName":"","lastName":"Bertherat","suffix":""},{"id":270462459,"identity":"1d978082-ea6a-4379-8099-3ca27b113a89","order_by":16,"name":"Anthony Dohan","email":"","orcid":"","institution":"Hôpital Cochin, AP-HP","correspondingAuthor":false,"prefix":"","firstName":"Anthony","middleName":"","lastName":"Dohan","suffix":""}],"badges":[],"createdAt":"2024-01-30 10:49:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3910331/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3910331/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50578754,"identity":"76335bd5-3226-4872-8edd-68da8378f3a4","added_by":"auto","created_at":"2024-02-02 18:10:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":222515,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patients in the development cohort.\u003c/p\u003e","description":"","filename":"Figure1FlowchartLocalCohort.png","url":"https://assets-eu.researchsquare.com/files/rs-3910331/v1/0d59f650a8dd33cf3da0d8f5.png"},{"id":50579585,"identity":"785e53c5-7f5b-4073-8167-af4c2eafcddb","added_by":"auto","created_at":"2024-02-02 18:18:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69762,"visible":true,"origin":"","legend":"\u003cp\u003eGraph shows receiver operating characteristic (ROC) curve of the Radscore for the prediction of patient survival using the cut-off of 24 months. Area under the ROC curve was 0·779, and the best cut-off to split the development cohort between patients with an overall survival (OS) \u0026gt; 24 months and those with an OS \u0026lt; 24 months was 0·562.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3910331/v1/c9d9bb118d8d125fe2657903.png"},{"id":50578752,"identity":"90d49264-4b08-41c8-92f4-a773f36c06e3","added_by":"auto","created_at":"2024-02-02 18:10:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60369,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curve of overall survival associated with the Radscore using a cut-off value of 0.562 in the development cohort (log-rank P \u0026lt; 0·001).\u003c/p\u003e","description":"","filename":"Figure3recadr.png","url":"https://assets-eu.researchsquare.com/files/rs-3910331/v1/d5b0e60e0626c7023b7ca2c3.png"},{"id":50578750,"identity":"8ff926f9-dd53-4c15-9042-32f59db4ee28","added_by":"auto","created_at":"2024-02-02 18:10:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":61614,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curve of overall survival associated with the Radscore using a cut-off value of 0.562 in the validation cohort (log-rank P = 0·022).\u003c/p\u003e","description":"","filename":"Figure4recadr.png","url":"https://assets-eu.researchsquare.com/files/rs-3910331/v1/80fc15248d80302f0d6662a4.png"},{"id":50578751,"identity":"431232e7-ee95-45bc-a795-7aae109fbe22","added_by":"auto","created_at":"2024-02-02 18:10:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":61126,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curve of overall survival associated with the European Network for the Study of Adrenal Tumors stage using a cut-off value of 2 in the validation cohort (log-rank P\u003cem\u003e \u003c/em\u003e\u0026lt; 0·17).\u003c/p\u003e","description":"","filename":"Figure5recadre.png","url":"https://assets-eu.researchsquare.com/files/rs-3910331/v1/ac49c135a28440ac1ba1b318.png"},{"id":50943488,"identity":"36f82acf-136c-4108-bb78-62df4d82d8c3","added_by":"auto","created_at":"2024-02-10 03:38:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":683629,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3910331/v1/5c5c12af-330c-4654-a381-a393fe9d180d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A computed tomography-based radiomic score to predict survival in patients with adrenocortical carcinoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAdrenocortical carcinoma (ACC) is a rare condition with an estimated incidence of 0\u0026middot;5\u0026ndash;2 cases per million of inhabitants per year, and it accounts for 0\u0026middot;04\u0026ndash;0\u0026middot;2% of all cancer deaths in the US (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). For patients with ACC, the prognosis is generally poor; the 5-year overall survival (OS) rate is only about 40%. However, OS varies considerably between patients (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In patients with ACC, OS prognostic factors include clinical, histopathological, and molecular features; these factors could help determine appropriate management on the basis of patient prognosis (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), but histopathological and molecular information are obtained at the penalty of invasive tissue sampling that is not free of morbidity (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and carries a risk of tumor dissemination (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Therefore, adrenal biopsy is not recommended in the setting of ACC (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Among the histopathological factors that have demonstrated prognostic capabilities is the Ki-67 index (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). A Ki-67 index\u0026thinsp;\u0026gt;\u0026thinsp;10% is regarded as a marker of high risk of recurrence, which justifies the use of adjuvant chemotherapy in these patients (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePreoperative prognosis of patients with ACC is usually assessed using the European Network for the Study of Adrenal Tumors (ENSAT) staging system, which is based on adrenal magnetic resonance imaging (MRI) and chest abdominal and pelvic computed tomography (CT) examinations (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Conventional imaging alone is not sufficient for the diagnosis, classification, or estimation of prognosis of patients with ACC (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e); therefore, the ENSAT classification takes into account tumor size, infiltration of surrounding adipose tissue, invasion of adjacent organs, positive lymph nodes, and distant metastases (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). It provides a stratification strongly associated with cancer-specific mortality; the 5-year OS is \u0026gt;\u0026thinsp;60% for patients with ENSAT stage I or II, \u0026lt; 50% for patients with stage III, and 20% for patients with stage IV ACC (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, the high heterogeneity of OS especially in patients with ENSAT stage I to III may require improvements.\u003c/p\u003e \u003cp\u003eTranscriptomics, which consists of an unsupervised high-throughput analysis of tumor genome expression profiling (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), can also be used to predict outcomes in patients with ACC. This analysis is performed on histopathological tissue samples, and findings correlate with patient disease-free survival (DFS) and OS (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In ACC, transcriptomics identifies two genomic profiles that correspond to two groups of patients with different outcomes (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The C1A profile is associated with a poor outcome, with a 5-year OS rate approaching 0%, whereas the C1B profile is associated with a 5-year OS greater than 90% (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, this analysis requires invasive tissue sampling with a potential associated morbidity.\u003c/p\u003e \u003cp\u003eTo improve the preoperative and non-invasive evaluation of patients with ACC, the radiomic approach has been considered (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Radiomics is the analysis of mathematically derived textural features on radiologic imaging (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Radiomics has demonstrated capabilities in the field of adrenal lesion characterization (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) and can also be used to predict OS and DFS in other cancers (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). One study specifically evaluated the capabilities of radiomics for estimating Ki-67 index in ACC (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). That study found that two shape-based features (\u003cem\u003ei.e.\u003c/em\u003e, shape elongation [SE] and shape flatness [SF]) were predictive of Ki-67 index\u0026thinsp;\u0026gt;\u0026thinsp;10% (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). SE\u0026thinsp;\u0026gt;\u0026thinsp;0\u0026middot;668 was a predictor of a Ki-67 index\u0026thinsp;\u0026gt;\u0026thinsp;10% with sensitivity, specificity, and positive likelihood and negative likelihood ratios of 75%, 69\u0026middot;2%, 2\u0026middot;4, and 0\u0026middot;36, respectively. SF\u0026thinsp;\u0026gt;\u0026thinsp;0.4966 was a predictor of a Ki-67 index\u0026thinsp;\u0026gt;\u0026thinsp;10% with sensitivity, specificity, and positive likelihood and negative likelihood ratios of 80%, 69\u0026middot;2%, 2\u0026middot;5, and 0\u0026middot;28, respectively. However, the study did not evaluate to what extent those features could be used as predictors of patient survival (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe aim of the current study was to evaluate the performance of a preoperative computed tomography (CT)-based radiomic prognostic score using features previously reported as predictors of high Ki-67 index (\u003cem\u003ei.e.\u003c/em\u003e, Ki-67 index\u0026thinsp;\u0026gt;\u0026thinsp;10%) in ACC to predict OS in patients with this condition.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Patients\u003c/h2\u003e \u003cp\u003e This retrospective study was approved by the institutional review board of Center 1 (N\u0026deg;: AAA-2020-08048), and the requirement for written informed consent was waived.\u003c/p\u003e \u003cp\u003eThe database of the department of pathology of our institution (Center 1) was queried to identify all patients who underwent adrenalectomy from January 2007 through December 2022. This initial search retrieved 708 patient records. Patients were included in the study if they were older than 18 years, had a histopathologically proven diagnosis of ACC, and had a preoperative CT examination performed less than 3 months before surgery that was available for review. Patients with adrenal tumors other than ACC (n\u0026thinsp;=\u0026thinsp;599), patients with collision tumors (n\u0026thinsp;=\u0026thinsp;1), and those without a preoperative CT examination (n\u0026thinsp;=\u0026thinsp;19) were excluded. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the study flow chart of patients who were considered for this study. Patients\u0026rsquo; clinical and histopathological data were recorded, including age, sex, tumor secretion, tumor largest diameter, tumor side, Ki-67 index, Weiss pathological score, and ENSAT stage. The characteristics of patients in the development cohort are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of 143 patients with adrenocortical carcinomas used for the development (n\u0026thinsp;=\u0026thinsp;89) and the validation (n\u0026thinsp;=\u0026thinsp;54) cohorts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDevelopment cohort\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;89)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation cohort\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26/89 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22/54 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight-sided ACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47/89 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21/54 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecretion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56/89 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26/54 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENSAT Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10/89 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4/54 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45/89 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19/54 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19/89 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22/54 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15/89 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9/54 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi67 rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u0026thinsp;\u0026plusmn;\u0026thinsp;19 [0\u0026ndash;95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u0026thinsp;\u0026plusmn;\u0026thinsp;17 [2\u0026ndash;79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeiss Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026middot;53\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026middot;96 [3\u0026middot;00\u0026ndash;9\u0026middot;00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026middot;83\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026middot;57 [3\u0026middot;00\u0026ndash;9\u0026middot;00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow up (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u0026thinsp;\u0026plusmn;\u0026thinsp;46 [1\u0026ndash;176]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u0026thinsp;\u0026plusmn;\u0026thinsp;42 [7\u0026ndash;206]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0\u003c/b\u003e\u0026middot;\u003cb\u003e01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28/89 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29/54 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0\u003c/b\u003e\u0026middot;\u003cb\u003e01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShape elongation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026middot;80\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u0026middot;11 [0.54\u0026ndash;0.97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u0026middot;25 [0\u0026middot;01\u0026ndash;0\u0026middot;96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0\u003c/b\u003e\u0026middot;\u003cb\u003e01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLargest ACC diameter (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82\u0026thinsp;\u0026plusmn;\u0026thinsp;45 [10\u0026ndash;219]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116\u0026thinsp;\u0026plusmn;\u0026thinsp;80 [18\u0026ndash;368]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u0026middot;\u003cb\u003e03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eQualitative variables are expressed as proportions followed by percentages into parentheses; Quantitative variables are expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation followed by ranges into brackets.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eACC indicates adrenocortical carcinoma; ENSAT indicates European Network for the Study of Adrenal Tumors. * comparison between building and validation cohorts.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. CT data acquisition\u003c/h2\u003e \u003cp\u003ePatients\u0026rsquo; CT images were acquired from different CT-scans. One radiologist blinded to the clinical outcomes (M.B. with ten years of experience in abdominal imaging) reviewed all CT examinations after anonymization on a picture-archiving and communication system viewing station (DirectView\u0026reg;, 11.4.0.1253 sp1 version, Carestream Health, Rochester, NY, USA). CT examinations had been performed with various helical CT units including Revolution HD\u0026reg; (General-Electric Healthcare, Wauwatosa, WI, USA), Somatom Sensation\u0026reg; 64 (Siemens Healthineers, Erlangen, Germany), or Somatom Definition\u0026reg; Flash (Siemens Healthineers). Acquisition parameters were as follows: field-of-view, 279\u0026ndash;350 mm; number of detector rows, 16\u0026ndash;64; acquisition slice thickness, 0\u0026middot;6\u0026ndash;1\u0026middot;25 mm; peak tube potential, 110\u0026ndash;120 kVp; gantry revolution time, 0\u0026middot;5\u0026thinsp;\u0026minus;\u0026thinsp;0\u0026middot;7 s; and reconstruction slice thickness, 1\u0026ndash;3 mm. Iodinated contrast material (iomeprol, Iomeron 350\u0026reg;, Bracco Imaging, Milan, Italy, or iobitridol, Xenetix 350\u0026reg;, Guerbet, Aulnay-sous-Bois, France) was injected intravenously with an automated power injector (rate, 2\u0026middot;5\u0026ndash;4 mL/s; total volume, 90\u0026ndash;130 mL). CT examinations covered the thorax, abdomen, and pelvis and were obtained before and after intravenous administration of iodinated contrast material during venous (60 s) and delayed (10 min) phases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data analysis\u003c/h2\u003e \u003cp\u003eThe morphological characteristics of the adrenal lesions, including tumor maximal diameter measured in the axial plane, vascular involvement, and all criteria of the ENSAT stage, were determined by M.B. on the basis of review of the CT examinations.\u003c/p\u003e \u003cp\u003eThree-dimensional segmentation of CT images of the whole adrenal lesion was performed using the ITKsnap v1.0.0rc2 module of the 3D-Slicer\u0026reg; software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.slicer.org\u003c/span\u003e\u003cspan address=\"https://www.slicer.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) by a radiologist with five years of experience in image segmentation. Shape radiomic features were extracted from CT data obtained during the venous phase of enhancement using the PyRadiomic (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.radiomics.io/pyradiomics.html\u003c/span\u003e\u003cspan address=\"http://www.radiomics.io/pyradiomics.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) module of the same software after normalization of the voxel size at 1 \u0026times; 1 \u0026times; 1 mm\u003csup\u003e3\u003c/sup\u003e (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). A total of 12 shape radiomic features were extracted (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). These features were analyzed without any pre-processing step after extraction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Validation cohort\u003c/h2\u003e \u003cp\u003eFor the validation cohort, 54 patients from another center (Center 2) were retrospectively identified from a previously published study (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Similar inclusion and exclusion criterions were used. The patients\u0026rsquo; medical records were retrospectively analyzed, and age, sex, Ki-67 index, ENSAT stage, shape elongation, occurrence of death, and duration of follow-up were recorded.\u003c/p\u003e \u003cp\u003eIn this cohort, preoperative CT images were obtained in the venous phase (60\u0026ndash;80 s after intravenous contrast medium injection). Some of the scans included in the present study were performed at outside facilities. The scans were acquired on 64-multidetector CT Light-Speed scanners (GE Healthcare, Waukesha, WI, USA) with a section thickness of 2\u0026middot;5 mm and an injection rate of 3\u0026ndash;5 ml/s (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2. 5. Statistical analysis\u003c/h3\u003e\n\u003cp\u003eStatistical analysis was performed using R software (version 4.1.0, R-foundation, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project\u003c/span\u003e\u003cspan address=\"http://www.r-project\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. org/). Quantitative (continuous) variables were reported as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SD) and ranges. Qualitative (binary) variables were reported as raw numbers, proportions, and percentages. Continuous variables were compared using the Student \u003cem\u003et\u003c/em\u003e-test and categorical variables using the χ2 test or Fisher exact test, as appropriate (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThirty-five ACC images were randomly selected from the development cohort for the evaluation of interrater agreement. A radiologist (M.G.) with ten years of experience in abdominal imaging performed new segmentations for the selected ACC images while blinded to the segmentation results of the previous radiologist. The Dice similarity coefficient (DSC) was calculated for the evaluation of interobserver reproducibility of the segmentation methods for each ACC (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Intraclass correlation coefficients (ICCs) were calculated for each radiomic feature using a two-way mixed-effect model (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). ICCs between 0\u0026middot;00 and 0\u0026middot;20; 0\u0026middot;21 and 0\u0026middot;40; 0\u0026middot;41 and 0\u0026middot;60; 0\u0026middot;61 and 0\u0026middot;80; and 0\u0026middot;81 and 1\u0026middot;00 indicated slight, fair, moderate, substantial, and almost perfect agreement, respectively (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Features with ICC\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;9 were considered non-reproducible (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePreviously published features (SE and SF) with ICC\u0026thinsp;\u0026gt;\u0026thinsp;0\u0026middot;9 in our cohort were evaluated for their ability to predict Ki-67 index\u0026thinsp;\u0026gt;\u0026thinsp;10% using previously published cut-off values (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). A receiver operating characteristic (ROC) curve was built, and areas under the curve (AUROCs) for the prediction of Ki-67 index\u0026thinsp;\u0026gt;\u0026thinsp;10% were calculated for each feature with an ICC\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;9.\u003c/p\u003e \u003cp\u003eThen, in order to improve patient stratification, a multivariable score using a logistic regression method was developed based on the development cohort. Tumor maximal diameter, patient ENSAT stage, and selected radiomic features as described above were included (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The score was tested for its ability to predict patients\u0026rsquo; OS using a ROC curve. The optimal cut-off value, defined as the value of features that yielded the best accuracy for the diagnosis of a high Ki-67 index as identified by ROC curve analysis, was used to split the cohort into patients with a good prognosis (\u003cem\u003ei.e.\u003c/em\u003e, OS\u0026thinsp;\u0026gt;\u0026thinsp;24 months) and those with a poor prognosis (\u003cem\u003ei.e.\u003c/em\u003e, OS\u0026thinsp;\u0026lt;\u0026thinsp;24 months). Patients\u0026rsquo; OS was estimated by using the Kaplan\u0026ndash;Meier method. The log-rank test was used to compare OS between patients with good prognosis and those with poor prognosis. The radiomic score was then tested in the validation cohort using the same method.\u003c/p\u003e \u003cp\u003ePerformance of the radiomic score for the prediction of OS was assessed using Harrel\u0026rsquo;s C-index and compared to the ENSAT stage alone in the validation cohort using the Student \u003cem\u003et\u003c/em\u003e-test for the comparison of C-indexes. All tests were two-tailed, and significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;05.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eEighty-nine patients were included in the development cohort and 54 patients in the validation cohort. Characteristics of the patients are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eSegmentation and extraction of radiomic features was possible for all ACC images. The reproducibility of segmentation was considered almost perfect for 34/35 (97%) ACCs with a mean DSC of 0\u0026middot;92\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u0026middot;03 (SD) (range: 0\u0026middot;72\u0026ndash;0\u0026middot;97). SF had an ICC\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;9 (ICC\u0026thinsp;=\u0026thinsp;0\u0026middot;85) and was excluded from the analysis. Therefore, the only radiomic feature used for analysis was SE.\u003c/p\u003e \u003cp\u003eIn the development cohort, the Ki-67 index was available for 77 of 89 patients (87%). The AUROC for the prediction of Ki-67 index\u0026thinsp;\u0026gt;\u0026thinsp;10% was 0\u0026middot;64 (95% CI: 0\u0026middot;54\u0026ndash;0\u0026middot;76).\u003c/p\u003e \u003cp\u003eThe Radscore developed in the development cohort included SE, tumor maximal diameter, and the ENSAT stage evaluated using pre-treatment CT examination. The AUROC for the prediction of patients\u0026rsquo; OS was 0\u0026middot;779, and the best cut-off value to split the development cohort between patients with an OS\u0026thinsp;\u0026gt;\u0026thinsp;24 months and those with an OS\u0026thinsp;\u0026lt;\u0026thinsp;24 months was 0\u0026middot;562 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Kaplan Meier analysis in the development cohort yielded a mean OS of 27\u0026middot;1 months (95% CI: 13\u0026middot;7\u0026ndash;40\u0026middot;1 months) in patients with poor prognosis and 63.3 months (95% CI: 50\u0026middot;1\u0026ndash;76\u0026middot;5 months) in those with good prognosis (P\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the validation cohort, the Radscore was able to discriminate between patients with poor prognosis (mean OS, 69\u0026middot;4 months; 95% CI: 57\u0026middot;4\u0026ndash;81\u0026middot;4 months) and those with good prognosis (mean OS, 75\u0026middot;6 months; 95% CI: 62\u0026middot;9\u0026ndash;88\u0026middot;4 months) (P\u0026thinsp;=\u0026thinsp;0\u0026middot;022) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The C-index of the Radscore was significantly better than that of the ENSAT stage alone (0\u0026middot;62 \u003cem\u003evs.\u003c/em\u003e 0\u0026middot;35, respectively; P\u0026thinsp;=\u0026thinsp;0\u0026middot;002) in the validation cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis retrospective two-center study reveals that a Radscore including SE, tumor maximal diameter, and ENSAT stage helps distinguish between patients with ACC who have a good prognosis and those with poor prognosis in an external validation cohort.\u003c/p\u003e \u003cp\u003eAs in the study by Ahmed et al., tumor segmentation, radiomic feature extraction, and analysis were performed using CT data obtained from portal venous phase images only (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). For most patients with suspected adrenal disease, CT is the first-line imaging modality for adrenal lesion characterization, whereas MRI is used primarily for specific indications (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). In addition, the portal venous phase is the most frequently performed acquisition phase for oncologic staging, and results in homogeneous contrast agent distribution, which can potentially improve features extraction reproducibility.\u003c/p\u003e \u003cp\u003eSE is a radiomic feature consisting of the ratio between the minor axis length and the major axis length of the tumor (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). SE on preoperative imaging was found to be correlated with a Ki-67 index cut-off value of 10% (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The combination of SE with existing prognostic factors on preoperative imaging (\u003cem\u003ei.e.\u003c/em\u003e, ENSAT stage and maximal diameter) may improve patient care by providing accurate risk stratification and enabling aggressive treatment, if needed, immediately after diagnosis. Moreover, using a manual segmentation method, SE extraction was highly reproducible, with an ICC greater than 0\u0026middot;9 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eShape-based radiomic features have demonstrated utility for tumor characterization in oncological imaging and for patient prognostic stratification (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Moreover, high interobserver reproducibility between radiologists has been reported (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). A shape-based classification using three features (roughness, convexity, and sphericity) proved to be able to discriminate between lung granuloma and lung carcinoma with an AUROC of 0\u0026middot;72, which was equivalent to that of expert radiologists (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Similarly, Alvarez-Jimenez, et al. showed that first-order and shape-based radiomic features measured on T2-weighted MRI images of the rectal wall and peritumoral environment of rectal cancers after neoadjuvant radio-chemotherapy were effective for the restaging of rectal tumors before surgery (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In that study, the radiomic features were associated with tumor grade after radio-chemotherapy with an accuracy of 69% for the detection of ypT0-2 versus ypT3-4 tumors using data from 52 patients; in an external validation cohort of 42 patients, the detection accuracy was 62% (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrently, adjuvant treatments such as mitotane are indicated in patients with ACC at high risk of recurrence based on surgical and histopathological findings including completeness of the resection, tumor grade, and Ki-67 index (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). When indicated, adjuvant treatment must be started as soon as possible (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Our finding may help improve patient care by proposing a more aggressive surgery or an early adjuvant therapy for patients with a poor prognosis.\u003c/p\u003e \u003cp\u003eOur study has some limitations. First, despite a two-center design, the number of patients remains limited, mostly because of the rarity of ACC, which makes the performance of large studies difficult. However, our study includes a relatively large number of patients compared to previous published studies, as well as an external validation cohort (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Second, the retrospective design of the study may induce a selection bias, but patients with this rare disease are mostly treated in reference centers that belong to the COMETE-cancer network, so their treatment is largely homogeneous (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Finally, the retrospective design also limits OS analysis because a subset of patients was lost to follow-up.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eWe built a Radscore based on preoperative CT images that is able to discriminate between ACC patients with good and poor prognosis in an external validation cohort. These results may improve the perioperative management and risk stratification of patients with ACC but require confirmation in prospective studies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACC: Adrenocortical carcinoma\u003c/p\u003e\n\u003cp\u003eAUC: Area under receiver operating characteristic curve\u003c/p\u003e\n\u003cp\u003eCI: Confidence interval\u003c/p\u003e\n\u003cp\u003eDFS: Disease-free survival\u003c/p\u003e\n\u003cp\u003eDSC: Dice similarity coefficient\u003c/p\u003e\n\u003cp\u003eENSAT: European Network for the Study of Adrenal Tumors\u003c/p\u003e\n\u003cp\u003eICC: Intraclass correlation coefficient\u003c/p\u003e\n\u003cp\u003eOS: Overall survival\u003c/p\u003e\n\u003cp\u003eSE: Shape elongation\u003c/p\u003e\n\u003cp\u003eSF: Shape flatness\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eDisclosures: none\u003c/p\u003e\n\u003cp\u003eFinancial support: none\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis retrospective study was approved by the institutional review board of Center 1 (N\u0026deg;: AAA-2020-08048), and the requirement for written informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Consent for publication\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Not applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Competing interests\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; The authors declare that they have no competing interests in relation with this study\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Funding\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; No external founding.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Authors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; M.B, P.S, G.A, C.H, J.B and A.D conceived the study, extract data from the training cohort, built the rad score and wrote the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;M.E, A.W.M, D.F, A.A.A, M.A.S, M.M.E, M.A.H and K.M.E provided data for the validation set.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;M.G performed segmentations for the reproducibility\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;A.J and M.H overviewed the study data analysis.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; All authors read and approved the final manuscript\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Acknowledgements: Maxime Barat received a grant in support for its PhD from the Soci\u0026eacute;t\u0026eacute; Fran\u0026ccedil;aise de Radiologie and the Servier Institute.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSouteiro P, Donato S, Costa C, Pereira CA, Sim\u0026otilde;es-Pereira J, Oliveira J, et al. 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Cancers 2021;13(7):1603.\u003c/li\u003e\n\u003cli\u003eAssie G, Giordano TJ, Bertherat J. Gene expression profiling in adrenocortical neoplasia. Mol Cell Endocrinol 2012;351(1):111-117.\u003c/li\u003e\n\u003cli\u003eAssie G, Letouze E, Fassnacht M, Jouinot A, Luscap W, Barreau O, et al. Integrated genomic characterization of adrenocortical carcinoma. Nat Genet 2014;46(6):607-612.\u003c/li\u003e\n\u003cli\u003eBoeken T, Feydy J, Lecler A, Soyer P, Feydy A, Barat M, et al. Artificial intelligence in diagnostic and interventional radiology: Where are we now? Diagn Interv Imaging 2023;104(1):1-5.\u003c/li\u003e\n\u003cli\u003eYi X, Guan X, Zhang Y, Liu L, Long X, Yin H, et al. Radiomics improves efficiency for differentiating subclinical pheochromocytoma from lipid-poor adenoma: a predictive, preventive and personalized medical approach in adrenal incidentalomas. EPMA J 2018;9(4):421-429.\u003c/li\u003e\n\u003cli\u003eElmohr MM, Fuentes D, Habra MA, Bhosale PR, Qayyum AA, Gates E, et al. Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT. Clin Radiol 2019;74(10):818.e1-818.e7..\u003c/li\u003e\n\u003cli\u003eYi X, Guan X, Chen C, Zhang Y, Zhang Z, Li M, et al. Adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced CT can effectively differentiate sPHEO from lipid-poor adrenal adenoma. J Cancer 2018;9(19):3577-3582.\u003c/li\u003e\n\u003cli\u003eDohan A, Gallix B, Guiu B, Le Malicot K, Reinhold C, Soyer P, et al. Early evaluation using a radiomic signature of unresectable hepatic metastases to predict outcome in patients with colorectal cancer treated with FOLFIRI and bevacizumab. Gut 2020;69(3):531-539. \u003c/li\u003e\n\u003cli\u003eLiu P, Tan XZ, Zhang T, Gu QB, Mao XH, Li YC, et al. Prediction of microvascular invasion in solitary hepatocellular carcinoma \u0026lt;/= 5 cm based on computed tomography radiomics. World J Gastroenterol 2021;27(17):2015-2024.\u003c/li\u003e\n\u003cli\u003eAhmed AA, Elmohr MM, Fuentes D, Habra MA, Fisher SB, Perrier ND, et al. Radiomic mapping model for prediction of Ki-67 expression in adrenocortical carcinoma. Clin Radiol 2020; 75(6):479.e17-479.e22.\u003c/li\u003e\n\u003cli\u003evan Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res 2017;77(21):e104-e107.\u003c/li\u003e\n\u003cli\u003eBarat M, Jannot AS, Dohan A, Soyer P. How to report and compare quantitative variables in a radiology article. Diagn Interv Imaging 2022;103(12):571-573.\u003c/li\u003e\n\u003cli\u003eCourot A, Cabrera DLF, Gogin N, Gaillandre L, Rico G, Zhang-Yin J, et al. Automatic cervical lymphadenopathy segmentation from CT data using deep learning. Diagn Interv Imaging 2021;102(11):675-681.\u003c/li\u003e\n\u003cli\u003eBenchoufi M, Matzner-Lober E, Molinari N, Jannot AS, Soyer P. 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An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT. Med Phys 2017;44(7):3556-3569.\u003c/li\u003e\n\u003cli\u003eAlvarez-Jimenez C, Antunes JT, Talasila N, Bera K, Brady JT, Gollamudi J, et al. Radiomic Texture and Shape Descriptors of the Rectal Environment on Post-Chemoradiation T2-Weighted MRI are Associated with Pathologic Tumor Stage Regression in Rectal Cancers: A Retrospective, Multi-Institution Study. Cancers 2020;12(8) :2027.\u003c/li\u003e\n\u003cli\u003eHescot S, Debien V, Hadoux J, Drui D, Haissaguerre M, de la Fouchardiere C, et al. Outcome of adrenocortical carcinoma patients included in early phase clinical trials: Results from the French network ENDOCAN-COMETE. Eur J Cancer 2023;189:112917.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3910331/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3910331/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eAdrenocortical carcinoma (ACC) is a rare condition with a poor and hardly predictable prognosis.\u003cstrong\u003e \u003c/strong\u003eThis study aims to build and evaluate a preoperative computed tomography (CT)-based radiomic score (Radscore) using features previously reported as biomarkers in adrenocortical carcinoma (ACC) to predict overall survival (OS) in patients with ACC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn this retrospective study,\u003cstrong\u003e \u003c/strong\u003ea Radscore based on preoperative CT examinations combining shape elongation, tumor maximal diameter, and the European Network for the Study of Adrenal Tumors (ENSAT) stage and was built using a logistic regression model to predict OS duration in a development cohort. An optimal cut-off of the Radscore was defined and the Kaplan-Meier method was used to assess OS. The Radscore was then tested in an external validation cohort. The C-index of the Radscore for the prediction of OS was compared to that of ENSAT stage alone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e: The Radscore was able to discriminate between patients with poor prognosis and patients with good prognosis in both the the validation cohort (54 patients; mean OS, 69·4 months; 95% CI: 57·4–81·4 months vs. mean OS, 75·6 months; 95% CI: 62·9–88·4 months, respectively; P = 0·022). In the validation cohort the C-index of the Radscore was significantly better than that of the ENSAT stage alone (0.62 vs. 0.35; P = 0·002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eA Radscore combining morphological criteria, radiomics, and ENSAT stage on preoperative CT examinations allow a stratification of prognosis in patients with ACC compared with ENSAT stage alone.\u003c/p\u003e","manuscriptTitle":"A computed tomography-based radiomic score to predict survival in patients with adrenocortical carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-02 18:10:13","doi":"10.21203/rs.3.rs-3910331/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3980ab0d-9a71-44df-ba1f-8af6a2beeed6","owner":[],"postedDate":"February 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-10T03:29:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-02 18:10:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3910331","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3910331","identity":"rs-3910331","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}