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Ramireddy JK, Sathya A, Sasidharan BK, Varghese AJ, Sathyamurthy A, John NO, Chandramohan A, Singh A, Joel A, Mittal R, Masih D, Varghese K, Rebekah G, Ram TS, Thomas HMT. Can Pretreatment MRI and Planning CT Radiomics Improve Prediction of Complete Pathological Response in Locally Advanced Rectal Cancer Following Neoadjuvant Treatment? J Gastrointest Cancer 2024; 55:1199-1211. [PMID: 38856797 DOI: 10.1007/s12029-024-01073-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/19/2024] [Indexed: 06/11/2024]
Abstract
OBJECTIVE(S) The treatment response to neoadjuvant chemoradiation (nCRT) differs largely in individuals treated for rectal cancer. In this study, we investigated the role of radiomics to predict the pathological response in locally advanced rectal cancers at different treatment time points: (1) before the start of any treatment using baseline T2-weighted MRI (T2W-MR) and (2) at the start of radiation treatment using planning CT. METHODS Patients on nCRT followed by surgery between June 2017 to December 2019 were included in the study. Histopathological tumour response grading (TRG) was used for classification, and gross tumour volume was defined by the radiation oncologists. Following resampling, 100 and 103 pyradiomic features were extracted from T2W-MR and planning CT images, respectively. Synthetic minority oversampling technique (SMOTE) was used to address class imbalance. Four machine learning classifiers built clinical, radiomic, and merged models. Model performances were evaluated on a held-out test dataset following 3-fold cross-validation using area under the receiver operator characteristic curves (AUC) with bootstrap 95% confidence intervals. RESULTS One hundred and fifty patients were included; 58/150 with TRG 1 were classified as complete responders, and rest were incomplete responders (IR). Clinical models performed better (AUC = 0.68) compared to radiomics models (AUC = 0.62). Overall, the clinical + T2W-MR model showed best performance (AUC = 0.72) in predicting the pathological response prior to therapy. Clinical + Planning CT-merged models could only achieve the highest AUC of 0.66. CONCLUSION Merging clinical and baseline T2W-MR radiomics enhances predicting pathological response in rectal cancer. Validation in larger cohorts is warranted, especially for watch and wait strategies.
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Affiliation(s)
- Jeba Karunya Ramireddy
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology, Unit 2, Dr Ida B Scudder Cancer Centre, Christian Medical College, Vellore, Tamil Nadu, 632004, India
| | - A Sathya
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology, Unit 2, Dr Ida B Scudder Cancer Centre, Christian Medical College, Vellore, Tamil Nadu, 632004, India
| | - Balu Krishna Sasidharan
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology, Unit 2, Dr Ida B Scudder Cancer Centre, Christian Medical College, Vellore, Tamil Nadu, 632004, India
| | - Amal Joseph Varghese
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology, Unit 2, Dr Ida B Scudder Cancer Centre, Christian Medical College, Vellore, Tamil Nadu, 632004, India
| | - Arvind Sathyamurthy
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology, Unit 2, Dr Ida B Scudder Cancer Centre, Christian Medical College, Vellore, Tamil Nadu, 632004, India
| | - Neenu Oliver John
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology, Unit 2, Dr Ida B Scudder Cancer Centre, Christian Medical College, Vellore, Tamil Nadu, 632004, India
| | | | - Ashish Singh
- Department of Medical Oncology, Christian Medical College, Vellore, India
| | - Anjana Joel
- Department of Medical Oncology, Christian Medical College, Vellore, India
| | - Rohin Mittal
- Department of General Surgery, Christian Medical College, Vellore, India
| | - Dipti Masih
- Department of Pathology, Christian Medical College, Vellore, India
| | - Kripa Varghese
- Department of Pathology, Christian Medical College, Vellore, India
| | - Grace Rebekah
- Department of Biostatistics, Christian Medical College, Vellore, India
| | - Thomas Samuel Ram
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology, Unit 2, Dr Ida B Scudder Cancer Centre, Christian Medical College, Vellore, Tamil Nadu, 632004, India
| | - Hannah Mary T Thomas
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology, Unit 2, Dr Ida B Scudder Cancer Centre, Christian Medical College, Vellore, Tamil Nadu, 632004, India.
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Gülbahar Ateş S, Demirel BB, Kekilli E, Öztürk E, Uçmak G. Primary tumor heterogeneity on pre-treatment [68Ga]Ga-PSMA PET/CT for the prediction of biochemical recurrence in prostate cancer. Rev Esp Med Nucl Imagen Mol 2024:500032. [PMID: 39097169 DOI: 10.1016/j.remnie.2024.500032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 08/05/2024]
Abstract
PURPOSE The aim of this study is to research the value of the texture analysis of primary tumors in pre-treatment [68Ga]Ga-PSMA PET in the prediction of the development of biochemical recurrence (BCR) in prostate cancer patients who underwent definitive therapies. METHODS 51 patients with prostate adenocarcinoma who had a pre-treatment [68Ga]Ga-PSMA-11 PET/CT and underwent definitive radiotherapy (RT) or radical prostatectomy (RP) were included in the study. Demographics, clinicopathologic features, the presence of BCR, and the last follow-up date of patients were recorded. Textural and conventional PET parameters (maximum standardized uptake value (SUVmax), total lesion-PSMA (TL-PSMA), and PSMA-tumor volume (PSMA-TV)) were obtained from PET/CT images using LifeX program. Parameters were grouped using the Youden index in ROC analysis. Factors predicting the BCR were determined using Cox regression analyses. RESULTS 29 (56.9%) patients have received primary curative RT, while the remaining 22 (43.1%) patients have undergone RP. 5 (22.7%) patients with RP and 3 (10.3%) patients with curative RT have developed BCR during the follow-up. INTENSITY-BASED-minimum grey level (P=.050), GLCM-sum variance (P=.019), and GLCM-cluster prominence (P=.050) were associated with BCR in univariate analysis. INTENSITY-BASED-minimum grey level (P=.009) and GLCM-sum variance (P=.004) were found as independent predictors of BCR in the multivariate analysis. CONCLUSION Tumor heterogeneity on pre-treatment [68Ga]Ga-PSMA PET is associated with a high risk of BCR in PCa patients who underwent definitive therapies.
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Affiliation(s)
- Seda Gülbahar Ateş
- Department of Nuclear Medicine, Hitit University Erol Olçok Training and Research Hospital, Çorum, Turkey.
| | - Bedriye Büşra Demirel
- Department of Nuclear Medicine, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, University of Health Sciences, Ankara, Turkey
| | - Esra Kekilli
- Department of Radiation Oncology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, University of Health Sciences, Ankara, Turkey
| | - Erdem Öztürk
- Department of Urology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, University of Health Sciences, Ankara, Turkey
| | - Gülin Uçmak
- Department of Nuclear Medicine, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, University of Health Sciences, Ankara, Turkey
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Chen X, Wang X, Huang S, Luo W, Luo Z, Chen Z. Study on Predicting Clinical Stage of Patients with Bronchial Asthma Based on CT Radiomics. J Asthma Allergy 2024; 17:291-303. [PMID: 38562252 PMCID: PMC10982665 DOI: 10.2147/jaa.s448064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 03/21/2024] [Indexed: 04/04/2024] Open
Abstract
Objective To explore the value of a new model based on CT radiomics in predicting the staging of patients with bronchial asthma (BA). Methods Patients with BA from 2018 to 2021 were retrospectively analyzed and underwent plain chest CT before treatment. According to the guidelines for the prevention and treatment of BA (2016 edition), they were divided into two groups: acute attack and non-acute attack. The images were processed as follows: using Lung Kit software for image standardization and segmentation, using AK software for image feature extraction, and using R language for data analysis and model construction (training set: test set = 7: 3). The efficacy and clinical effects of the constructed model were evaluated with ROC curve, sensitivity, specificity, calibration curve and decision curve. Results A total of 112 patients with BA were enrolled, including 80 patients with acute attack (range: 2-86 years old, mean: 53.89±17.306 years old, males of 33) and 32 patients with non-acute attack (range: 4-79 years old, mean: 57.38±19.223 years old, males of 18). A total of 10 imaging features are finally retained and used to construct model using multi-factor logical regression method. In the training group, the AUC, sensitivity and specificity of the model was 0.881 (95% CI:0.808-0.955), 0.804 and 0.818, separately; while in the test group, it was 0.792 (95% CI:0.608-0.976), 0.792 and 0.80, respectively. Conclusion The model constructed based on radiomics has a good effect on predicting the staging of patients with BA, which provides a new method for clinical diagnosis of staging in BA patients.
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Affiliation(s)
- Xiaodong Chen
- Radiology Imaging Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, People’s Republic of China
| | - Xiangyuan Wang
- Radiology Imaging Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, People’s Republic of China
| | - Shangqing Huang
- Radiology Imaging Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, People’s Republic of China
| | - Wenxuan Luo
- Radiology Imaging Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, People’s Republic of China
| | - Zebin Luo
- Radiology Imaging Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, People’s Republic of China
| | - Zipan Chen
- Health Management Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, People’s Republic of China
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Wu MY, Han QJ, Ai Z, Liang YY, Yan HW, Xie Q, Xiang ZM. Assessment of chemotherapy resistance changes in human colorectal cancer xenografts in rats based on MRI histogram features. Front Oncol 2024; 14:1301649. [PMID: 38357206 PMCID: PMC10864667 DOI: 10.3389/fonc.2024.1301649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 01/11/2024] [Indexed: 02/16/2024] Open
Abstract
Purpose We investigated the value of magnetic resonance imaging (MRI) histogram features, a non-invasive method, in assessing the changes in chemoresistance of colorectal cancer xenografts in rats. Methods A total of 50 tumor-bearing mice with colorectal cancer were randomly divided into two groups: control group and 5-fluorouracil (5-FU) group. The MRI histogram characteristics and the expression levels of p53 protein and MRP1 were obtained at 24 h, 48 h, 72 h, 120 h, and 168 h after treatment. Results Sixty highly repeatable MRI histogram features were obtained. There were 16 MRI histogram parameters and MRP1 resistance protein differences between groups. At 24 h after treatment, the MRI histogram texture parameters of T2-weighted imaging (T2WI) images (10%, 90%, median, energy, and RootMeanSquared) and D images (10% and Range) were positively correlated with MRP1 (r = 0.925, p = 0.005). At 48 h after treatment, histogram texture parameters of apparent diffusion coefficient (ADC) images (Energy) were positively correlated with the presence of MRP1 resistance protein (r = 0.900, p = 0.037). There was no statistically significant difference between MRI histogram features and p53 protein expression level. Conclusions MRI histogram texture parameters based on T2WI, D, and ADC maps can help to predict the change of 5-FU resistance in colorectal cancer in the early stage and provide important reference significance for clinical treatment.
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Affiliation(s)
- Min-Yi Wu
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Qi-Jia Han
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Zhu Ai
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Yu-Ying Liang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Hao-Wen Yan
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Qi Xie
- Department of Radiology, Guangzhou First People’s Hospital/Department of Medical Imaging, Nansha Hospital, Guangzhou, Guangzhou, China
| | - Zhi-Ming Xiang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
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Lu C, Xing ZX, Xia XG, Long ZD, Chen B, Zhou P, Wang R. Development and validation of a postoperative pulmonary infection prediction model for patients with primary hepatic carcinoma. World J Gastrointest Oncol 2023; 15:1241-1252. [PMID: 37546550 PMCID: PMC10401473 DOI: 10.4251/wjgo.v15.i7.1241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 05/14/2023] [Accepted: 06/12/2023] [Indexed: 07/12/2023] Open
Abstract
BACKGROUND There are factors that significantly increase the risk of postoperative pulmonary infections in patients with primary hepatic carcinoma (PHC). Previous reports have shown that over 10% of patients with PHC experience postoperative pulmonary infections. Thus, it is crucial to prioritize the prevention and treatment of postoperative pulmonary infections in patients with PHC.
AIM To identify the risk factors for postoperative pulmonary infection in patients with PHC and develop a prediction model to aid in postoperative management.
METHODS We retrospectively collected data from 505 patients who underwent hepatobiliary surgery between January 2015 and February 2023 in the Department of Hepatobiliary and Pancreaticospleen Surgery. Radiomics data were selected for statistical analysis, and clinical pathological parameters and imaging data were included in the screening database as candidate predictive variables. We then developed a pulmonary infection prediction model using three different models: An artificial neural network model; a random forest model; and a generalized linear regression model. Finally, we evaluated the accuracy and robustness of the prediction model using the receiver operating characteristic curve and decision curve analyses.
RESULTS Among the 505 patients, 86 developed a postoperative pulmonary infection, resulting in an incidence rate of 17.03%. Based on the gray-level co-occurrence matrix, we identified 14 categories of radiomic data for variable screening of pulmonary infection prediction models. Among these, energy, contrast, the sum of squares (SOS), the inverse difference (IND), mean sum (MES), sum variance (SUV), sum entropy (SUE), and entropy were independent risk factors for pulmonary infection after hepatectomy and were listed as candidate variables of machine learning prediction models. The random forest model algorithm, in combination with IND, SOS, MES, SUE, SUV, and entropy, demonstrated the highest prediction efficiency in both the training and internal verification sets, with areas under the curve of 0.823 and 0.801 and a 95% confidence interval of 0.766-0.880 and 0.744-0.858, respectively. The other two types of prediction models had prediction efficiencies between areas under the curve of 0.734 and 0.815 and 95% confidence intervals of 0.677-0.791 and 0.766-0.864, respectively.
CONCLUSION Postoperative pulmonary infection in patients undergoing hepatectomy may be related to risk factors such as IND, SOS, MES, SUE, SUV, energy, and entropy. The prediction model in this study based on diffusion-weighted images, especially the random forest model algorithm, can better predict and estimate the risk of pulmonary infection in patients undergoing hepatectomy, providing valuable guidance for postoperative management.
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Affiliation(s)
- Chao Lu
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
| | - Zhi-Xiang Xing
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
| | - Xi-Gang Xia
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
| | - Zhi-Da Long
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
| | - Bo Chen
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
| | - Peng Zhou
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
| | - Rui Wang
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
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Yaltırık Bilgin E, Ünal Ö, Törenek Ş, Çiledağ N. Computerized Tomography Texture Analysis in the Differential Diagnosis of Intracranial Epidermoid and Arachnoid Cysts. Cureus 2023; 15:e41945. [PMID: 37588326 PMCID: PMC10425918 DOI: 10.7759/cureus.41945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/15/2023] [Indexed: 08/18/2023] Open
Abstract
PURPOSE This study evaluated the differences between arachnoid and epidermoid cysts in computerized tomography (CT) texture analysis (TA). MATERIAL AND METHODS The study included 12 patients with intracranial epidermoid cysts and 26 patients with intracranial arachnoid cysts who were diagnosed with diffusion-weighted magnetic resonance imaging (DW-MRI) and who had undergone an unenhanced CT examination before treatment. The LIFEx application software was used to obtain texture features. Eighty-two texture features from 38 lesions were automatically calculated for each lesion. The Shapiro-Wilk test was used to test the normality of the scores, and the Mann-Whitney U Test was used to test the difference between the groups. Receiver operating characteristic (ROC) curves and multivariate logistic regression modeling examined the parameters' diagnostic performances. RESULTS The median age of the patients was 53 years (range: 19-88 years). Eighty-two texture parameters were evaluated in the first order: gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), neighbor gray-tone difference matrix (NGTDM), and gray-level size zone matrix (GLSZM) groups. There was a statistically significant difference between the arachnoid cyst and the epidermoid cyst in the variables of compacity, compactness 1, compactness 2, sphericity, asphericity, sum average, coarseness, and low gray-level zone (p<0.05). According to the multiple logistic regression model, it was determined that the sum average in the GLCM group (B=-0.11; p=0.015), coarseness (B= 869.5; p=0.044) in the NGTDM group, and morphological sphericity (B=24.18; p=0.047) were the radiomics variables that increased the probability of epidermoid diagnosis. According to the classification table of the model, the sensitivity rate was found to be 83%, and the specificity rate was found to be 96%. Therefore, the probability of accurate model classification was 92%. CONCLUSION CT TA is a method that can be applied with high diagnostic accuracy in the differential diagnosis of intracranial epidermoid and arachnoid cysts, especially in patients who cannot undergo an MRI examination.
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Affiliation(s)
- Ezel Yaltırık Bilgin
- Radiology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, TUR
| | - Özkan Ünal
- Radiology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, TUR
| | - Şahap Törenek
- Radiology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, TUR
| | - Nazan Çiledağ
- Radiology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, TUR
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Zheng T, Pan J, Du D, Liang X, Yi H, Du J, Wu S, Liu L, Shi G. Preoperative assessment of high-grade endometrial cancer using a radiomic signature and clinical indicators. Future Oncol 2023; 19:587-601. [PMID: 37097730 DOI: 10.2217/fon-2022-0631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023] Open
Abstract
Aim: To develop and validate a radiomics-based combined model (ModelRC) to predict the pathological grade of endometrial cancer. Methods: A total of 403 endometrial cancer patients from two independent centers were enrolled as training, internal validation and external validation sets. Radiomic features were extracted from T2-weighted images, apparent diffusion coefficient map and contrast-enhanced 3D volumetric interpolated breath-hold examination images. Results: Compared with the clinical model and radiomics model, ModelRC showed superior performance; the areas under the receiver operating characteristic curves were 0.920 (95% CI: 0.864-0.962), 0.882 (95% CI: 0.779-0.955) and 0.881 (95% CI: 0.815-0.939) for the training, internal validation and external validation sets, respectively. Conclusion: ModelRC, which incorporated clinical and radiomic features, exhibited excellent performance in the prediction of high-grade endometrial cancer.
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Affiliation(s)
- Tao Zheng
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, PR China
| | - Jiangyang Pan
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, PR China
| | - Dan Du
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Xin Liang
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Huiling Yi
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Juan Du
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Shuo Wu
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Lanxiang Liu
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Gaofeng Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, PR China
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Bykanov A, Danilov G, Kostumov V, Pilipenko O, Nutfullin B, Rastvorova O, Pitskhelauri D. Artificial Intelligence Technologies in the Microsurgical Operating Room (Review). Sovrem Tekhnologii Med 2023; 15:86-94. [PMID: 37389018 PMCID: PMC10306972 DOI: 10.17691/stm2023.15.2.08] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Indexed: 07/01/2023] Open
Abstract
Surgery performed by a novice neurosurgeon under constant supervision of a senior surgeon with the experience of thousands of operations, able to handle any intraoperative complications and predict them in advance, and never getting tired, is currently an elusive dream, but can become a reality with the development of artificial intelligence methods. This paper has presented a review of the literature on the use of artificial intelligence technologies in the microsurgical operating room. Searching for sources was carried out in the PubMed text database of medical and biological publications. The key words used were "surgical procedures", "dexterity", "microsurgery" AND "artificial intelligence" OR "machine learning" OR "neural networks". Articles in English and Russian were considered with no limitation to publication date. The main directions of research on the use of artificial intelligence technologies in the microsurgical operating room have been highlighted. Despite the fact that in recent years machine learning has been increasingly introduced into the medical field, a small number of studies related to the problem of interest have been published, and their results have not proved to be of practical use yet. However, the social significance of this direction is an important argument for its development.
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Affiliation(s)
- A.E. Bykanov
- Neurosurgeon, 7 Department of Neurosurgery, Researcher; National Medical Research Center for Neurosurgery named after Academician N.N. Burdenko, Ministry of Healthcare of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - G.V. Danilov
- Academic Secretary; National Medical Research Center for Neurosurgery named after Academician N.N. Burdenko, Ministry of Healthcare of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - V.V. Kostumov
- PhD Student, Programmer, the CMC Faculty; Lomonosov Moscow State University, 1 Leninskiye Gory, Moscow, 119991, Russia
| | - O.G. Pilipenko
- PhD Student, Programmer, the CMC Faculty; Lomonosov Moscow State University, 1 Leninskiye Gory, Moscow, 119991, Russia
| | - B.M. Nutfullin
- PhD Student, Programmer, the CMC Faculty; Lomonosov Moscow State University, 1 Leninskiye Gory, Moscow, 119991, Russia
| | - O.A. Rastvorova
- Resident, 7 Department of Neurosurgery; National Medical Research Center for Neurosurgery named after Academician N.N. Burdenko, Ministry of Healthcare of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - D.I. Pitskhelauri
- Professor, Head of the 7 Department of Neurosurgery; National Medical Research Center for Neurosurgery named after Academician N.N. Burdenko, Ministry of Healthcare of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
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Danilov G, Kalayeva D, Vikhrova N, Konakova T, Zagorodnova A, Popova A, Postnov A, Shugay S, Pronin I. Radiomics in Determining Tumor-to-Normal Brain SUV Ratio Based on 11C-Methionine PET/CT in Glioblastoma. Sovrem Tekhnologii Med 2023; 15:5-11. [PMID: 37388754 PMCID: PMC10306961 DOI: 10.17691/stm2023.15.1.01] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Indexed: 12/10/2023] Open
Abstract
UNLABELLED Modern methodology of PET/CT quantitative analysis in patients with glioblastomas is not strictly standardized in clinic settings and does not exclude the influence of the human factor. Methods of radiomics may facilitate unification, and improve objectivity and efficiency of the medical image analysis. The aim of the study is to evaluate the potential of radiomics in the analysis of PET/CT glioblastoma images identifying the relationship between the radiomic features and the 11С-methionine tumor-to-normal brain uptake ratio (TNR) determined by an expert in routine. MATERIALS AND METHODS PET/CT data (2018-2020) from 40 patients (average age was 55±12 years; 77.5% were males) with a histologically confirmed diagnosis of "glioblastoma" were included in the analysis. TNR was calculated as a ratio of the standardized uptake value of 11C-methionine measured in the tumor and intact tissue. Calculation of radiomic features for each PET was performed in the specified volumetric region of interest, capturing the tumor with the surrounding tissues. The relationship between TNR and the radiomic features was determined using the linear regression model. Predictors were included in the model following correlation analysis and LASSO regularization. The experiment with machine learning was repeated 300 times, splitting the training (70%) and test (30%) subsets randomly. The model quality metrics and predictor significance obtained in 300 tests were summarized. RESULTS Of 412 PET/CT radiomic parameters significantly correlated with TNR (p<0.05), the regularization procedure left no more than 30 in each model (the median number of predictors was 9 [7; 13]). The experiment has demonstrated a non-random linear correlation (the Spearman correlation coefficient was 0.58 [0.43; 0.74]) between TNR and separate radiomic features, primarily fractal dimensions, characterizing the geometrical properties of the image. CONCLUSION Radiomics enabled an objective determination of PET/CT image texture features reflecting the biological activity of glioblastomas. Despite the existing limitations in the application, the first results provide a good perspective of these methods in neurooncology.
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Affiliation(s)
- G.V. Danilov
- Scientific Board Secretary, Head of the Laboratory of Biomedical Informatics and Artificial Intelligence; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - D.B. Kalayeva
- Medical Physicist; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia; PhD Student; National Research Nuclear University MEPhI, 31 Kashirskoe Shosse, Moscow, 115409, Russia
| | - N.B. Vikhrova
- Radiologist; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - T.A. Konakova
- PhD Student; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - A.I. Zagorodnova
- Student; Pirogov Russian National Research Medical University, 1 Ostrovitianova St., Moscow, 117997, Russia
| | - A.A. Popova
- Student; Pirogov Russian National Research Medical University, 1 Ostrovitianova St., Moscow, 117997, Russia
| | - A.A. Postnov
- Researcher; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia; Associate Professor; National Research Nuclear University MEPhI, 31 Kashirskoe Shosse, Moscow, 115409, Russia; Researcher; Lebedev Physical Institute of the Russian Academy of Sciences, 53 Leninskiy Prospect, Moscow, 119991, Russia; Head of the Project; Research Institute of Technical Physics and Automation, 46 Varshavskoye Shosse, Moscow, 115230, Russia
| | - S.V. Shugay
- Professor, Academician of the Russian Academy of Sciences, Deputy Director for Research, Head of the Unit for X-ray and Radioisotopic Methods of Diagnosis; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - I.N. Pronin
- Pathologist; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
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Posa A, Barbieri P, Mazza G, Tanzilli A, Natale L, Sala E, Iezzi R. Technological Advancements in Interventional Oncology. Diagnostics (Basel) 2023; 13:diagnostics13020228. [PMID: 36673038 PMCID: PMC9857620 DOI: 10.3390/diagnostics13020228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/31/2022] [Accepted: 01/02/2023] [Indexed: 01/11/2023] Open
Abstract
Interventional radiology, and particularly interventional oncology, represents one of the medical subspecialties in which technological advancements and innovations play an utterly fundamental role. Artificial intelligence, consisting of big data analysis and feature extrapolation through computational algorithms for disease diagnosis and treatment response evaluation, is nowadays playing an increasingly important role in various healthcare fields and applications, from diagnosis to treatment response prediction. One of the fields which greatly benefits from artificial intelligence is interventional oncology. In addition, digital health, consisting of practical technological applications, can assist healthcare practitioners in their daily activities. This review aims to cover the most useful, established, and interesting artificial intelligence and digital health innovations and updates, to help physicians become more and more involved in their use in clinical practice, particularly in the field of interventional oncology.
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Affiliation(s)
- Alessandro Posa
- Department of Diagnostic Imaging, Oncologic Radiotherapy and Hematology—A. Gemelli University Hospital Foundation IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy
- Correspondence:
| | - Pierluigi Barbieri
- Department of Diagnostic Imaging, Oncologic Radiotherapy and Hematology—A. Gemelli University Hospital Foundation IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy
| | - Giulia Mazza
- Department of Diagnostic Imaging, Oncologic Radiotherapy and Hematology—A. Gemelli University Hospital Foundation IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy
| | - Alessandro Tanzilli
- Department of Diagnostic Imaging, Oncologic Radiotherapy and Hematology—A. Gemelli University Hospital Foundation IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy
| | - Luigi Natale
- Department of Diagnostic Imaging, Oncologic Radiotherapy and Hematology—A. Gemelli University Hospital Foundation IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy
- Istituto di Radiodiagnostica, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Evis Sala
- Department of Diagnostic Imaging, Oncologic Radiotherapy and Hematology—A. Gemelli University Hospital Foundation IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy
- Istituto di Radiodiagnostica, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Roberto Iezzi
- Department of Diagnostic Imaging, Oncologic Radiotherapy and Hematology—A. Gemelli University Hospital Foundation IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy
- Istituto di Radiodiagnostica, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
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11
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Virtual Image-based Biopsy of Lung Metastases: The Promise of Radiomics. Acad Radiol 2023; 30:47-48. [PMID: 36371374 DOI: 10.1016/j.acra.2022.10.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 10/31/2022] [Indexed: 11/12/2022]
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12
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Danilov GV, Shevchenko AM, Konakova TA, Pogosbekyan EL, Shugai SV, Tsukanova TV, Zakharova NE, Batalov AI, Agrba SB, Vikhrova NB, Pronin IN. [Non-invasive diagnosis of brain gliomas by histological type using neuroradiomics in standardized regions of interest: towards digital biopsy]. ZHURNAL VOPROSY NEIROKHIRURGII IMENI N. N. BURDENKO 2023; 87:59-66. [PMID: 38054228 DOI: 10.17116/neiro20238706159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
The future of contemporary neuroimaging does not solely lie in novel image-capturing technologies, but also in better methods for extraction of useful information from these images. Scientists see great promise in radiomics, i.e. the methodology for analysis of multiple features in medical image. However, there are certain issues in this field impairing reproducibility of results. One such issue is no standards in establishing the regions of interest. OBJECTIVE To introduce a standardized method for identification of regions of interest when analyzing MR images using radiomics; to test the hypothesis that this approach is effective for distinguishing different histological types of gliomas. MATERIAL AND METHODS We analyzed preoperative MR data in 83 adults with various gliomas (WHO classification, 2016), i.e. oligodendroglioma, anaplastic oligodendroglioma, anaplastic astrocytoma, and glioblastoma. Radiomic features were computed for T1, T1-enhanced, T2 and T2-FLAIR modalities in four standardized volumetric regions of interest by 356 voxels (46.93 mm3): 1) contrast enhancement; 2) edema-infiltration; 3) area adjacent to edema-infiltration; 4) reference area in contralateral hemisphere. Subsequently, mathematical models were trained to classify MR-images of glioma depending on histological type and quantitative features. RESULTS Mean accuracy of differential diagnosis of 4 histological types of gliomas in experiments with machine learning was 81.6%, mean accuracy of identification of tumor types - from 94.1% to 99.5%. The best results were obtained using support vector machines and random forest model. CONCLUSION In a pilot study, the proposed standardization of regions of interest demonstrated high effectiveness for MR-based differential diagnosis of oligodendroglioma, anaplastic oligodendroglioma, anaplastic astrocytoma and glioblastoma. There are grounds for applying and improving this methodology in further studies.
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Affiliation(s)
- G V Danilov
- Burdenko Neurosurgical Center, Moscow, Russia
| | | | | | - E L Pogosbekyan
- Burdenko Neurosurgical Center, Moscow, Russia
- Institute of Higher Nervous Activity and Neurophysiology, Moscow, Russia
| | - S V Shugai
- Burdenko Neurosurgical Center, Moscow, Russia
| | | | | | - A I Batalov
- Burdenko Neurosurgical Center, Moscow, Russia
| | - S B Agrba
- Burdenko Neurosurgical Center, Moscow, Russia
| | - N B Vikhrova
- Burdenko Neurosurgical Center, Moscow, Russia
- Scientific Practical Clinical Center for Diagnosis and Telemedicine Technologies, Moscow, Russia
| | - I N Pronin
- Burdenko Neurosurgical Center, Moscow, Russia
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13
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García-García S, García-Galindo M, Arrese I, Sarabia R, Cepeda S. Current Evidence, Limitations and Future Challenges of Survival Prediction for Glioblastoma Based on Advanced Noninvasive Methods: A Narrative Review. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58121746. [PMID: 36556948 PMCID: PMC9786785 DOI: 10.3390/medicina58121746] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/16/2022] [Accepted: 11/28/2022] [Indexed: 12/03/2022]
Abstract
Background and Objectives: Survival estimation for patients diagnosed with Glioblastoma (GBM) is an important information to consider in patient management and communication. Despite some known risk factors, survival estimation remains a major challenge. Novel non-invasive technologies such as radiomics and artificial intelligence (AI) have been implemented to increase the accuracy of these predictions. In this article, we reviewed and discussed the most significant available research on survival estimation for GBM through advanced non-invasive methods. Materials and Methods: PubMed database was queried for articles reporting on survival prognosis for GBM through advanced image and data management methods. Articles including in their title or abstract the following terms were initially screened: ((glioma) AND (survival)) AND ((artificial intelligence) OR (radiomics)). Exclusively English full-text articles, reporting on humans, published as of 1 September 2022 were considered. Articles not reporting on overall survival, evaluating the effects of new therapies or including other tumors were excluded. Research with a radiomics-based methodology were evaluated using the radiomics quality score (RQS). Results: 382 articles were identified. After applying the inclusion criteria, 46 articles remained for further analysis. These articles were thoroughly assessed, summarized and discussed. The results of the RQS revealed some of the limitations of current radiomics investigation on this field. Limitations of analyzed studies included data availability, patient selection and heterogeneity of methodologies. Future challenges on this field are increasing data availability, improving the general understanding of how AI handles data and establishing solid correlations between image features and tumor's biology. Conclusions: Radiomics and AI methods of data processing offer a new paradigm of possibilities to tackle the question of survival prognosis in GBM.
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Affiliation(s)
- Sergio García-García
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
- Correspondence:
| | - Manuel García-Galindo
- Faculty of Medicine, University of Valladolid, Avenida Ramón y Cajal 7, 47003 Valladolid, Spain
| | - Ignacio Arrese
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
| | - Rosario Sarabia
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
| | - Santiago Cepeda
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
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Ilyushenkova J, Sazonova S, Popov E, Zavadovsky K, Batalov R, Archakov E, Moskovskih T, Popov S, Minin S, Romanov A. Radiomic phenotype of epicardial adipose tissue in the prognosis of atrial fibrillation recurrence after catheter ablation in patients with lone atrial fibrillation. J Arrhythm 2022; 38:682-693. [PMID: 36237852 PMCID: PMC9535779 DOI: 10.1002/joa3.12760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/10/2022] [Accepted: 07/18/2022] [Indexed: 11/30/2022] Open
Abstract
Background Epicardial adipose tissue (EAT) has been considered as one of the probable triggers of atrial fibrillation (AF). CT‐rediomics is a perspective noninvasive method of assessment of EAT. We evaluate the radiomic phenotype of EAT in patients with lone AF in the prognosis of AF recurrence after catheter ablation. Methods A total of 43 patients with lone AF referred for CA and 20 out‐hospital patients without arrhythmia underwent multidetector computed tomography coronary angiography. Segmentation of EAT and extraction radiomic features were performed on calcium scoring series using by 3D‐Slicer. Clinical follow‐up was performed for 12 months period after the CA. Results EAT in patients with lone AF had a distinct radiomic phenotype. Thus, 45 of 93 calculated radiomic features, volume and attenuation of EAT were significantly different between patients with lone AF and persons without any arrhythmia. In addition, 17 radiomic features were significantly different in subgroups with and without AF recurrence. Multivariate regression analysis demonstrated that only gray level nonuniformity normalized (GLSZM) was an independent predictor of AF recurrence (OR 1.0027, 95%CI 1.0009–1.0044, p = 0.002). ROC analysis data showed that GLSZM >1227.4 indicates high probability of AF recurrence during 12 months (sensitivity 89.4%, specificity 70.8%, AUC: 0.809; p = 0.001). Conclusion The radiomic parameter GLSZM is associated with late AF recurrence after CA in patients with lone AF. In current study GLSZM was a stronger predictor of lone AF recurrence in multivariate analysis comparing with other established risk factors and EAT volume and attenuation.
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Affiliation(s)
- Julia Ilyushenkova
- Cardiology Research Institute, Tomsk National Research Medical Centre Russian Academy of Sciences Tomsk Russian Federation
| | - Svetlana Sazonova
- Cardiology Research Institute, Tomsk National Research Medical Centre Russian Academy of Sciences Tomsk Russian Federation
| | - Evgeny Popov
- Cardiology Research Institute, Tomsk National Research Medical Centre Russian Academy of Sciences Tomsk Russian Federation
| | - Konstantin Zavadovsky
- Cardiology Research Institute, Tomsk National Research Medical Centre Russian Academy of Sciences Tomsk Russian Federation
| | - Roman Batalov
- Cardiology Research Institute, Tomsk National Research Medical Centre Russian Academy of Sciences Tomsk Russian Federation
| | - Evgeny Archakov
- Cardiology Research Institute, Tomsk National Research Medical Centre Russian Academy of Sciences Tomsk Russian Federation
| | - Tatyana Moskovskih
- Cardiology Research Institute, Tomsk National Research Medical Centre Russian Academy of Sciences Tomsk Russian Federation
| | - Sergey Popov
- Cardiology Research Institute, Tomsk National Research Medical Centre Russian Academy of Sciences Tomsk Russian Federation
| | - Stanislav Minin
- E. Meshalkin National Medical Research Ministry of Health of the Russian Federation Novosibirsk Russian Federation
| | - Alexander Romanov
- E. Meshalkin National Medical Research Ministry of Health of the Russian Federation Novosibirsk Russian Federation
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Borgheresi A, De Muzio F, Agostini A, Ottaviani L, Bruno A, Granata V, Fusco R, Danti G, Flammia F, Grassi R, Grassi F, Bruno F, Palumbo P, Barile A, Miele V, Giovagnoni A. Lymph Nodes Evaluation in Rectal Cancer: Where Do We Stand and Future Perspective. J Clin Med 2022; 11:2599. [PMID: 35566723 PMCID: PMC9104021 DOI: 10.3390/jcm11092599] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/25/2022] [Accepted: 05/03/2022] [Indexed: 12/12/2022] Open
Abstract
The assessment of nodal involvement in patients with rectal cancer (RC) is fundamental in disease management. Magnetic Resonance Imaging (MRI) is routinely used for local and nodal staging of RC by using morphological criteria. The actual dimensional and morphological criteria for nodal assessment present several limitations in terms of sensitivity and specificity. For these reasons, several different techniques, such as Diffusion Weighted Imaging (DWI), Intravoxel Incoherent Motion (IVIM), Diffusion Kurtosis Imaging (DKI), and Dynamic Contrast Enhancement (DCE) in MRI have been introduced but still not fully validated. Positron Emission Tomography (PET)/CT plays a pivotal role in the assessment of LNs; more recently PET/MRI has been introduced. The advantages and limitations of these imaging modalities will be provided in this narrative review. The second part of the review includes experimental techniques, such as iron-oxide particles (SPIO), and dual-energy CT (DECT). Radiomics analysis is an active field of research, and the evidence about LNs in RC will be discussed. The review also discusses the different recommendations between the European and North American guidelines for the evaluation of LNs in RC, from anatomical considerations to structured reporting.
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Affiliation(s)
- Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
| | - Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | - Andrea Agostini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
| | - Letizia Ottaviani
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
| | - Alessandra Bruno
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale IRCCS di Napoli, 80131 Naples, Italy;
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy
| | - Ginevra Danti
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Federica Flammia
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Roberta Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy
| | - Francesca Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Abruzzo Health Unit 1, Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, 67100 L’Aquila, Italy
| | - Antonio Barile
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
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The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment. Neuroradiology 2022; 64:1639-1647. [PMID: 35459957 PMCID: PMC9271107 DOI: 10.1007/s00234-022-02959-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 04/07/2022] [Indexed: 11/19/2022]
Abstract
Purpose
Human papillomavirus (HPV) status assessment is crucial for decision making in oropharyngeal cancer patients. In last years, several articles have been published investigating the possible role of radiomics in distinguishing HPV-positive from HPV-negative neoplasms. Aim of this review was to perform a systematic quality assessment of radiomic studies published on this topic. Methods Radiomics studies on HPV status prediction in oropharyngeal cancer patients were selected. The Radiomic Quality Score (RQS) was assessed by three readers to evaluate their methodological quality. In addition, possible correlations between RQS% and journal type, year of publication, impact factor, and journal rank were investigated. Results After the literature search, 19 articles were selected whose RQS median was 33% (range 0–42%). Overall, 16/19 studies included a well-documented imaging protocol, 13/19 demonstrated phenotypic differences, and all were compared with the current gold standard. No study included a public protocol, phantom study, or imaging at multiple time points. More than half (13/19) included feature selection and only 2 were comprehensive of non-radiomic features. Mean RQS was significantly higher in clinical journals. Conclusion Radiomics has been proposed for oropharyngeal cancer HPV status assessment, with promising results. However, these are supported by low methodological quality investigations. Further studies with higher methodological quality, appropriate standardization, and greater attention to validation are necessary prior to clinical adoption. Supplementary Information The online version contains supplementary material available at 10.1007/s00234-022-02959-0.
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Jiang C, Huang X, Li A, Teng Y, Ding C, Chen J, Xu J, Zhou Z. Radiomics signature from [ 18F]FDG PET images for prognosis predication of primary gastrointestinal diffuse large B cell lymphoma. Eur Radiol 2022; 32:5730-5741. [PMID: 35298676 DOI: 10.1007/s00330-022-08668-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 02/13/2022] [Accepted: 02/17/2022] [Indexed: 11/26/2022]
Abstract
OBJECTIVES To investigate the prognostic value of PET radiomics feature in the prognosis of patients with primary gastrointestinal diffuse large B cell lymphoma (PGI-DLBCL) treated with R-CHOP-like regimen. METHODS A total of 140 PGI-DLBCL patients who underwent pre-therapy [18F] FDG PET/CT were enrolled in this retrospective analysis. PET radiomics features obtained from patients in the training cohort were subjected to three machine learning methods and Pearson's correlation test for feature selection. Support vector machine (SVM) was used to build a radiomics signature classifier associated with progression-free survival (PFS) and overall survival (OS). A multivariate Cox proportional hazards regression model was established to predict survival outcomes. RESULTS A total of 1421 PET radiomics features were extracted and reduced to 5 features to build a radiomics signature which was significantly associated with PFS and OS (p < 0.05). The combined model incorporating radiomics signatures, metabolic metrics, and clinical risk factors showed high C-indices in both the training (PFS: 0.825, OS: 0.834) and validation sets (PFS: 0.831, OS: 0.877). Decision curve analysis (DCA) demonstrated that the combined models achieved the most net benefit across a wider reasonable range of threshold probabilities for predicting PFS and OS. CONCLUSION The newly developed radiomics signatures obtained by the ensemble strategy were independent predictors of PFS and OS for PGI-DLBCL patients. Moreover, the combined model with clinical and metabolic factors was able to predict patient prognosis and may enable personalized treatment decision-making. KEY POINTS • Radiomics signatures generated from the optimal radiomics feature set from the [18F]FDG PET images can predict the survival of PGI-DLBCL patients. • The optimal radiomics feature set is constructed by integrating the feature selection outputs of LASSO, RF, Xgboost, and PC methods. • Combined models incorporating radiomics signatures from18F-FDG PET images, metabolic parameters, and clinical factors outperformed clinical models, and NCCN-IPI.
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Affiliation(s)
- Chong Jiang
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, China
| | - Xiangjun Huang
- The Key Laboratory of Broadband Wireless Communication and Sensor Network Technology (Ministry of Education), Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Ang Li
- The Key Laboratory of Broadband Wireless Communication and Sensor Network Technology (Ministry of Education), Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Yue Teng
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, China
| | - Chongyang Ding
- Department of Nuclear Medicine, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
| | - Jianxin Chen
- The Key Laboratory of Broadband Wireless Communication and Sensor Network Technology (Ministry of Education), Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Jingyan Xu
- Department of Hematology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321, Zhongshan Road, Nanjing City, Jiangsu Province, 210008, China.
| | - Zhengyang Zhou
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, China.
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