1
|
Yan HJ, Zhao JS, Zuo HD, Zhang JJ, Deng ZQ, Yang C, Luo X, Wan JX, Zheng XY, Chen WY, Li SP, Tian D. Dual-Region Computed Tomography Radiomics-Based Machine Learning Predicts Subcarinal Lymph Node Metastasis in Patients with Non-small Cell Lung Cancer. Ann Surg Oncol 2024; 31:5011-5020. [PMID: 38520581 DOI: 10.1245/s10434-024-15197-w] [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] [Received: 01/04/2024] [Accepted: 03/04/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Noninvasively and accurately predicting subcarinal lymph node metastasis (SLNM) for patients with non-small cell lung cancer (NSCLC) remains challenging. This study was designed to develop and validate a tumor and subcarinal lymph nodes (tumor-SLNs) dual-region computed tomography (CT) radiomics model for predicting SLNM in NSCLC. METHODS This retrospective study included NSCLC patients who underwent lung resection and SLNs dissection between January 2017 and December 2020. The radiomic features of the tumor and SLNs were extracted from preoperative CT, respectively. Ninety machine learning (ML) models were developed based on tumor region, SLNs region, and tumor-SLNs dual-region. The model performance was assessed by the area under the curve (AUC) and validated internally by fivefold cross-validation. RESULTS In total, 202 patients were included in this study. ML models based on dual-region radiomics showed good performance for SLNM prediction, with a median AUC of 0.794 (range, 0.686-0.880), which was superior to those of models based on tumor region (median AUC, 0.746; range, 0.630-0.811) and SLNs region (median AUC, 0.700; range, 0.610-0.842). The ML model, which is developed by using the naive Bayes algorithm and dual-region features, had the highest AUC of 0.880 (range of cross-validation, 0.825-0.937) among all ML models. The optimal logistic regression model was inferior to the optimal ML model for predicting SLNM, with an AUC of 0.727. CONCLUSIONS The CT radiomics showed the potential for accurately predicting SLNM in NSCLC patients. The ML model with dual-region radiomic features has better performance than the logistic regression or single-region models.
Collapse
Affiliation(s)
- Hao-Ji Yan
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
- Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan
| | - Jia-Sheng Zhao
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Hou-Dong Zuo
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Jun-Jie Zhang
- College of Medical Imaging, North Sichuan Medical College, Nanchong, China
| | - Zhi-Qiang Deng
- College of Medical Imaging, North Sichuan Medical College, Nanchong, China
| | - Chen Yang
- College of Medical Imaging, North Sichuan Medical College, Nanchong, China
| | - Xi Luo
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Jia-Xin Wan
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Xiang-Yun Zheng
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Wei-Yang Chen
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Su-Ping Li
- Department of Nuclear Medicine, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, Nanchong, China.
| | - Dong Tian
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China.
| |
Collapse
|
2
|
Sibilia MC, Danuzzo F, Spinelli F, Cassina EM, Libretti L, Pirondini E, Raveglia F, Tuoro A, Bertolaccini L, Isgro’ S, Perrone S, Rizzo S, Petrella F. Prognostic Factors and Clinical Outcomes of Surgical Treatment of Major Thoracic Trauma. Healthcare (Basel) 2024; 12:1147. [PMID: 38891222 PMCID: PMC11171996 DOI: 10.3390/healthcare12111147] [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: 04/22/2024] [Revised: 06/03/2024] [Accepted: 06/04/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Major thoracic trauma represents a life-threatening condition, requiring a prompt multidisciplinary approach and appropriate pathways for effective recovery. While acute morbidity and mortality are well-known outcomes in thoracic-traumatized patients, long-term quality of life in patients surviving surgical treatment has not been widely investigated before. METHODS Between November 2016 and November 2023, thirty-two consecutive patients were operated on because of thoracic trauma. Age, sex, comorbidities, location and extent of thoracic trauma, Injury Severity Score (ISS), Abbreviated Injury Scale (AIS), Organ Injury Scale (OIS), intra and extrathoracic organ involvement, mechanism of injury, type of surgical procedure, postoperative complications, ICU and total length of stay, immediate clinical outcomes and long-term quality of life-by using the EQ-5D-3L scale and Numeric Rate Pain Score (NPRS)-were collected for each patient Results: Results indicated no significant difference in EQOL.5D3L among patients with thoracic trauma based on AIS (p = 0.55), but a significant difference was observed in relation to ISS (p = 0.000011). CONCLUSIONS ISS is correlated with the EQOL.5D3L questionnaire on long-term quality of life, representing the best prognostic factor-in terms of long-term quality of life-in patients surviving major thoracic trauma surgical treatment.
Collapse
Affiliation(s)
- Maria Chiara Sibilia
- Department of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, 20900 Monza, Italy; (M.C.S.); (F.D.); (F.S.); (E.M.C.); (L.L.); (E.P.); (F.R.); (A.T.)
| | - Federica Danuzzo
- Department of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, 20900 Monza, Italy; (M.C.S.); (F.D.); (F.S.); (E.M.C.); (L.L.); (E.P.); (F.R.); (A.T.)
| | - Francesca Spinelli
- Department of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, 20900 Monza, Italy; (M.C.S.); (F.D.); (F.S.); (E.M.C.); (L.L.); (E.P.); (F.R.); (A.T.)
| | - Enrico Mario Cassina
- Department of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, 20900 Monza, Italy; (M.C.S.); (F.D.); (F.S.); (E.M.C.); (L.L.); (E.P.); (F.R.); (A.T.)
| | - Lidia Libretti
- Department of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, 20900 Monza, Italy; (M.C.S.); (F.D.); (F.S.); (E.M.C.); (L.L.); (E.P.); (F.R.); (A.T.)
| | - Emanuele Pirondini
- Department of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, 20900 Monza, Italy; (M.C.S.); (F.D.); (F.S.); (E.M.C.); (L.L.); (E.P.); (F.R.); (A.T.)
| | - Federico Raveglia
- Department of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, 20900 Monza, Italy; (M.C.S.); (F.D.); (F.S.); (E.M.C.); (L.L.); (E.P.); (F.R.); (A.T.)
| | - Antonio Tuoro
- Department of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, 20900 Monza, Italy; (M.C.S.); (F.D.); (F.S.); (E.M.C.); (L.L.); (E.P.); (F.R.); (A.T.)
| | - Luca Bertolaccini
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Via Ripamonti 234, 20141 Milan, Italy;
| | - Stefano Isgro’
- Department of Emergency and Intensive Care, Fondazione IRCCS San Gerardo dei Tintori, 20900 Monza, Italy;
| | - Stefano Perrone
- Department of Surgery, Fondazione IRCCS San Gerardo dei Tintori, 20900 Monza, Italy;
| | - Stefania Rizzo
- Service of Radiology, Imaging Institute of Southern Switzerland (IIMSI), EOC Via Tesserete 46, 6900 Lugano, Switzerland;
- Facoltà di Scienze Biomediche, Università della Svizzera Italiana (USI), Via Buffi 13, 6900 Lugano, Switzerland
| | - Francesco Petrella
- Department of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, 20900 Monza, Italy; (M.C.S.); (F.D.); (F.S.); (E.M.C.); (L.L.); (E.P.); (F.R.); (A.T.)
| |
Collapse
|
3
|
Petrella F, Rizzo SMR, Rampinelli C, Casiraghi M, Bagnardi V, Frassoni S, Pozzi S, Pappalardo O, Pravettoni G, Spaggiari L. Assessment of pulmonary vascular anatomy: comparing augmented reality by holograms versus standard CT images/reconstructions using surgical findings as reference standard. Eur Radiol Exp 2024; 8:57. [PMID: 38724831 PMCID: PMC11082107 DOI: 10.1186/s41747-024-00458-w] [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] [Received: 12/19/2023] [Accepted: 03/07/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND We compared computed tomography (CT) images and holograms (HG) to assess the number of arteries of the lung lobes undergoing lobectomy and assessed easiness in interpretation by radiologists and thoracic surgeons with both techniques. METHODS Patients scheduled for lobectomy for lung cancer were prospectively included and underwent CT for staging. A patient-specific three-dimensional model was generated and visualized in an augmented reality setting. One radiologist and one thoracic surgeon evaluated CT images and holograms to count lobar arteries, having as reference standard the number of arteries recorded at surgery. The easiness of vessel identification was graded according to a Likert scale. Wilcoxon signed-rank test and κ statistics were used. RESULTS Fifty-two patients were prospectively included. The two doctors detected the same number of arteries in 44/52 images (85%) and in 51/52 holograms (98%). The mean difference between the number of artery branches detected by surgery and CT images was 0.31 ± 0.98, whereas it was 0.09 ± 0.37 between surgery and HGs (p = 0.433). In particular, the mean difference in the number of arteries detected in the upper lobes was 0.67 ± 1.08 between surgery and CT images and 0.17 ± 0.46 between surgery and holograms (p = 0.029). Both radiologist and surgeon showed a higher agreement for holograms (κ = 0.99) than for CT (κ = 0.81) and found holograms easier to evaluate than CTs (p < 0.001). CONCLUSIONS Augmented reality by holograms is an effective tool for preoperative vascular anatomy assessment of lungs, especially when evaluating the upper lobes, more prone to anatomical variations. TRIAL REGISTRATION ClinicalTrials.gov, NCT04227444 RELEVANCE STATEMENT: Preoperative evaluation of the lung lobe arteries through augmented reality may help the thoracic surgeons to carefully plan a lobectomy, thus contributing to optimize patients' outcomes. KEY POINTS • Preoperative assessment of the lung arteries may help surgical planning. • Lung artery detection by augmented reality was more accurate than that by CT images, particularly for the upper lobes. • The assessment of the lung arterial vessels was easier by using holograms than CT images.
Collapse
Affiliation(s)
- Francesco Petrella
- Department of Thoracic Surgery, IRCCS European Institute of Oncology, Via Ripamonti 435, 20141, Milan, Italy
- Department of Oncology and Hemato-oncology, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy
- Department of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, Via G. B. Pergolesi, 33, 20900, Monza, Italy
| | - Stefania Maria Rita Rizzo
- Clinic of Radiology, Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC) Via Tesserete 46, 6900, Lugano, Switzerland.
- Facoltà di Scienze biomediche, Università della Svizzera italiana (USI), Via Buffi 13, 6900, Lugano, Switzerland.
| | - Cristiano Rampinelli
- Division of Radiology, IRCCS European Institute of Oncology, Via Ripamonti 435, 20141, Milan, Italy
| | - Monica Casiraghi
- Department of Thoracic Surgery, IRCCS European Institute of Oncology, Via Ripamonti 435, 20141, Milan, Italy
- Department of Oncology and Hemato-oncology, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Vincenzo Bagnardi
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, 20126, Milan, Italy
| | - Samuele Frassoni
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, 20126, Milan, Italy
| | - Silvia Pozzi
- Artiness srl, Viale Cassala 57, 20143, Milan, Italy
| | | | - Gabriella Pravettoni
- Department of Oncology and Hemato-oncology, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Lorenzo Spaggiari
- Department of Thoracic Surgery, IRCCS European Institute of Oncology, Via Ripamonti 435, 20141, Milan, Italy
- Department of Oncology and Hemato-oncology, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy
| |
Collapse
|
4
|
Petrella F, Danuzzo F, Sibilia MC, Vaquer S, Longarini R, Guidi A, Raveglia F, Libretti L, Pirondini E, Cara A, Cassina EM, Tuoro A, Cortinovis D. Colorectal Cancer Pulmonary Metastasectomy: When, Why and How. Cancers (Basel) 2024; 16:1408. [PMID: 38611086 PMCID: PMC11010871 DOI: 10.3390/cancers16071408] [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: 03/14/2024] [Revised: 03/29/2024] [Accepted: 04/02/2024] [Indexed: 04/14/2024] Open
Abstract
Colorectal cancer is the third-most-diagnosed cancer in males and in females, representing 8% of estimated new cases, and the third cause of cancer-related death in both sexes, accounting for 9% of cancer deaths in men and 8% in women. About 20% of patients diagnosed with CRC present metastatic disease. Although lung metachronous or synchronous metastatic spread without other involved sites has been reported in only a small proportion of patients, considering that this tumor is frequently diagnosed, the clinical approach to CRC pulmonary metastases represents a major issue for thoracic surgeons and CRC oncologists. Among patients diagnosed with pulmonary metastases from CRC, about 9-12% are eligible for local treatments with radical intent, including surgical resection, SBRT (stereotactic body radiation therapy) and ablation therapy. Due to the lack of randomized controlled trials among different local strategies, there is no definitive evidence about the optimal approach, although surgical resection is considered the most effective therapeutic option in this clinical scenario. Oncological achievement of primary radical resection, the biology of primary tumor and metastatic sites, disease free interval and or progression free survival are independent prognostic factors which make it possible to define a cohort of patients which might significantly benefit from pulmonary metastasectomy.
Collapse
Affiliation(s)
- Francesco Petrella
- Division of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, Via GB Pergolesi 33, 20900 Monza, Italy; (F.D.); (M.C.S.); (S.V.); (L.L.); (E.P.); (A.C.); (E.M.C.); (A.T.)
| | - Federica Danuzzo
- Division of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, Via GB Pergolesi 33, 20900 Monza, Italy; (F.D.); (M.C.S.); (S.V.); (L.L.); (E.P.); (A.C.); (E.M.C.); (A.T.)
| | - Maria Chiara Sibilia
- Division of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, Via GB Pergolesi 33, 20900 Monza, Italy; (F.D.); (M.C.S.); (S.V.); (L.L.); (E.P.); (A.C.); (E.M.C.); (A.T.)
| | - Sara Vaquer
- Division of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, Via GB Pergolesi 33, 20900 Monza, Italy; (F.D.); (M.C.S.); (S.V.); (L.L.); (E.P.); (A.C.); (E.M.C.); (A.T.)
| | - Raffaella Longarini
- Division of Medical Oncology, Fondazione IRCCS San Gerardo dei Tintori, Via GB Pergolesi 33, 20900 Monza, Italy; (R.L.); or (D.C.)
| | - Alessandro Guidi
- Division of Medical Oncology, Fondazione IRCCS San Gerardo dei Tintori, Via GB Pergolesi 33, 20900 Monza, Italy; (R.L.); or (D.C.)
| | - Federico Raveglia
- Division of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, Via GB Pergolesi 33, 20900 Monza, Italy; (F.D.); (M.C.S.); (S.V.); (L.L.); (E.P.); (A.C.); (E.M.C.); (A.T.)
| | - Lidia Libretti
- Division of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, Via GB Pergolesi 33, 20900 Monza, Italy; (F.D.); (M.C.S.); (S.V.); (L.L.); (E.P.); (A.C.); (E.M.C.); (A.T.)
| | - Emanuele Pirondini
- Division of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, Via GB Pergolesi 33, 20900 Monza, Italy; (F.D.); (M.C.S.); (S.V.); (L.L.); (E.P.); (A.C.); (E.M.C.); (A.T.)
| | - Andrea Cara
- Division of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, Via GB Pergolesi 33, 20900 Monza, Italy; (F.D.); (M.C.S.); (S.V.); (L.L.); (E.P.); (A.C.); (E.M.C.); (A.T.)
| | - Enrico Mario Cassina
- Division of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, Via GB Pergolesi 33, 20900 Monza, Italy; (F.D.); (M.C.S.); (S.V.); (L.L.); (E.P.); (A.C.); (E.M.C.); (A.T.)
| | - Antonio Tuoro
- Division of Thoracic Surgery, Fondazione IRCCS San Gerardo dei Tintori, Via GB Pergolesi 33, 20900 Monza, Italy; (F.D.); (M.C.S.); (S.V.); (L.L.); (E.P.); (A.C.); (E.M.C.); (A.T.)
| | - Diego Cortinovis
- Division of Medical Oncology, Fondazione IRCCS San Gerardo dei Tintori, Via GB Pergolesi 33, 20900 Monza, Italy; (R.L.); or (D.C.)
- School of Medicine and Surgery, University of Milano Bicocca, 20126 Milan, Italy
| |
Collapse
|
5
|
Yang Y, Wang P, Yang Z, Zeng Y, Chen F, Wang Z, Rizzo S. Segmentation method of magnetic resonance imaging brain tumor images based on improved UNet network. Transl Cancer Res 2024; 13:1567-1583. [PMID: 38617525 PMCID: PMC11009801 DOI: 10.21037/tcr-23-1858] [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: 10/09/2023] [Accepted: 03/01/2024] [Indexed: 04/16/2024]
Abstract
Background Glioma is a primary malignant craniocerebral tumor commonly found in the central nervous system. According to research, preoperative diagnosis of glioma and a full understanding of its imaging features are very significant. Still, the traditional segmentation methods of image dispensation and machine wisdom are not acceptable in glioma segmentation. This analysis explores the potential of magnetic resonance imaging (MRI) brain tumor images as an effective segmentation method of glioma. Methods This study used 200 MRI images from the affiliated hospital and applied the 2-dimensional residual block UNet (2DResUNet). Features were extracted from input images using a 2×2 kernel size (64-kernel) 1-step 2D convolution (Conv) layer. The 2DDenseUNet model implemented in this study incorporates a ResBlock mechanism within the UNet architecture, as well as a Gaussian noise layer for data augmentation at the input stage, and a pooling layer for replacing the conventional 2D convolutional layers. Finally, the performance of the proposed protocol and its effective measures in glioma segmentation were verified. Results The outcomes of the 5-fold cross-validation evaluation show that the proposed 2DResUNet and 2DDenseUNet structure has a high sensitivity despite the slightly lower evaluation result on the Dice score. At the same time, compared with other models used in the experiment, the DM-DA-UNet model proposed in this paper was significantly improved in various indicators, increasing the reliability of the model and providing a reference and basis for the accurate formulation of clinical treatment strategies. The method used in this study showed stronger feature extraction ability than the UNet model. In addition, our findings demonstrated that using generalized die harm and prejudiced cross entropy as loss functions in the training process effectively alleviated the class imbalance of glioma data and effectively segmented glioma. Conclusions The method based on the improved UNet network has obvious advantages in the MRI brain tumor portrait segmentation procedure. The result showed that we developed a 2D residual block UNet, which can improve the incorporation of glioma segmentation into the clinical process.
Collapse
Affiliation(s)
- Yang Yang
- Department of Neurosurgery, Xiangyang Central Hospital (Hospital of Hubei University of Arts and Science), Xiangyang, China
| | - Peng Wang
- Department of Neurosurgery, Xiangyang Central Hospital (Hospital of Hubei University of Arts and Science), Xiangyang, China
| | - Zhenyu Yang
- Department of Neurosurgery, Xiangyang Central Hospital (Hospital of Hubei University of Arts and Science), Xiangyang, China
| | - Yuecheng Zeng
- Department of Neurosurgery, Xiangyang Central Hospital (Hospital of Hubei University of Arts and Science), Xiangyang, China
| | - Feng Chen
- Department of Neurosurgery, Xiangyang Central Hospital (Hospital of Hubei University of Arts and Science), Xiangyang, China
| | - Zhiyong Wang
- Department of Neurosurgery, Xiangyang Central Hospital (Hospital of Hubei University of Arts and Science), Xiangyang, China
| | - Stefania Rizzo
- Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale (EOC), Lugano, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera italiana, Lugano, Switzerland
| |
Collapse
|
6
|
Pan F, Feng L, Liu B, Hu Y, Wang Q. Application of radiomics in diagnosis and treatment of lung cancer. Front Pharmacol 2023; 14:1295511. [PMID: 38027000 PMCID: PMC10646419 DOI: 10.3389/fphar.2023.1295511] [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: 09/16/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023] Open
Abstract
Radiomics has become a research field that involves the process of converting standard nursing images into quantitative image data, which can be combined with other data sources and subsequently analyzed using traditional biostatistics or artificial intelligence (Al) methods. Due to the capture of biological and pathophysiological information by radiomics features, these quantitative radiomics features have been proven to provide fast and accurate non-invasive biomarkers for lung cancer risk prediction, diagnosis, prognosis, treatment response monitoring, and tumor biology. In this review, radiomics has been emphasized and discussed in lung cancer research, including advantages, challenges, and drawbacks.
Collapse
Affiliation(s)
- Feng Pan
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
- Department of CT, Jilin Province FAW General Hospital, Changchun, China
| | - Li Feng
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Baocai Liu
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Yue Hu
- Department of Biobank, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Qian Wang
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
| |
Collapse
|
7
|
Cefalì M, Scala I, Pavone G, Helbling D, Hussung S, Fritsch R, Reiner C, Stocker S, Koeberle D, Kissling M, Chianca V, Del Grande F, De Dosso S, Rizzo S. Is Computed-Tomography-Based Body Composition a Reliable Predictor of Chemotherapy-Related Toxicity in Pancreatic Cancer Patients? Cancers (Basel) 2023; 15:4398. [PMID: 37686674 PMCID: PMC10486498 DOI: 10.3390/cancers15174398] [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: 04/14/2023] [Revised: 08/13/2023] [Accepted: 09/01/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND Malnutrition, loss of weight and of skeletal muscle mass are frequent in pancreatic cancer patients, a majority of which will undergo chemotherapy over the course of their disease. Available data suggest a negative prognostic role of these changes in body composition on disease outcomes; however, it is unclear whether tolerance to chemotherapeutic treatment is similarly and/or negatively affected. We aimed to explore this association by retrospectively assessing changes in body composition and chemotherapy-related toxicity in a cohort of advanced pancreatic cancer patients. METHODS Body composition was evaluated through clinical parameters and through radiological assessment of muscle mass, skeletal muscle area, skeletal muscle index and skeletal muscle density; and an assessment of fat distribution by subcutaneous adipose tissue and visceral adipose tissue. We performed descriptive statistics, pre/post chemotherapy comparisons and uni- and multivariate analyses to assess the relation between changes in body composition and toxicity. RESULTS Toxicity risk increased with an increase of skeletal muscle index (OR: 1.03) and body mass index (OR: 1.07), whereas it decreased with an increase in skeletal muscle density (OR: 0.96). Multivariate analyses confirmed a reduction in the risk of toxicity only with an increase in skeletal muscle density (OR: 0.96). CONCLUSIONS This study suggests that the retrospective analysis of changes in body composition is unlikely to be useful to predict toxicity to gemcitabine-nab-paclitaxel.
Collapse
Affiliation(s)
- Marco Cefalì
- Istituto Oncologico della Svizzera Italiana (IOSI), Ente Ospedaliero Cantonale (EOC), 6500 Bellinzona, Switzerland; (M.C.); (G.P.)
| | - Isabel Scala
- Facoltà di Scienze Biomediche, Università della Svizzera Italiana, Via Buffi 13, 6900 Lugano, Switzerland; (I.S.); (F.D.G.); (S.R.)
| | - Giuliana Pavone
- Istituto Oncologico della Svizzera Italiana (IOSI), Ente Ospedaliero Cantonale (EOC), 6500 Bellinzona, Switzerland; (M.C.); (G.P.)
| | - Daniel Helbling
- Onkozentrum Zürich, Seestrasse 259, 8038 Zurich, Switzerland;
| | - Saskia Hussung
- Department of Medical Oncology and Hematology, University Hospital of Zurich, 8091 Zurich, Switzerland; (S.H.); (R.F.)
| | - Ralph Fritsch
- Department of Medical Oncology and Hematology, University Hospital of Zurich, 8091 Zurich, Switzerland; (S.H.); (R.F.)
| | - Cäcilia Reiner
- Institute for Diagnostic and Interventional Radiology, University Hospital of Zurich, 8091 Zurich, Switzerland; (C.R.); (S.S.)
| | - Soleen Stocker
- Institute for Diagnostic and Interventional Radiology, University Hospital of Zurich, 8091 Zurich, Switzerland; (C.R.); (S.S.)
| | - Dieter Koeberle
- Oncology Departement, St. Claraspital, Kleinriehenstrasse 39, 4058 Basel, Switzerland;
| | - Marc Kissling
- Radiology Department, St. Claraspital, Kleinriehenstrasse 39, 4058 Basel, Switzerland;
| | - Vito Chianca
- Istituto di Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900 Lugano, Switzerland;
| | - Filippo Del Grande
- Facoltà di Scienze Biomediche, Università della Svizzera Italiana, Via Buffi 13, 6900 Lugano, Switzerland; (I.S.); (F.D.G.); (S.R.)
- Istituto di Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900 Lugano, Switzerland;
| | - Sara De Dosso
- Istituto Oncologico della Svizzera Italiana (IOSI), Ente Ospedaliero Cantonale (EOC), 6500 Bellinzona, Switzerland; (M.C.); (G.P.)
- Facoltà di Scienze Biomediche, Università della Svizzera Italiana, Via Buffi 13, 6900 Lugano, Switzerland; (I.S.); (F.D.G.); (S.R.)
| | - Stefania Rizzo
- Facoltà di Scienze Biomediche, Università della Svizzera Italiana, Via Buffi 13, 6900 Lugano, Switzerland; (I.S.); (F.D.G.); (S.R.)
- Istituto di Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900 Lugano, Switzerland;
| |
Collapse
|
8
|
Cheng A, Lee JWK, Ngiam KY. Use of 3D ultrasound to characterise temporal changes in thyroid nodules: an in vitro study. J Ultrasound 2023; 26:643-651. [PMID: 36053484 PMCID: PMC10468465 DOI: 10.1007/s40477-022-00698-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 06/13/2022] [Indexed: 10/14/2022] Open
Abstract
OBJECTIVE Thyroid nodules are extremely common, with prevalence rate up to 68%, yet only 7-15% of these are malignant. Many nodules require surveillance and 2-dimensional ultrasound (2D US) is used. Issues include the huge workload of obtaining and labeling images and difficulty comparing sizes of nodules over time due to large inter-operator variability. Inaccuracies may result in unnecessary FNAC or missed diagnosis of malignant nodules. METHODS We compared two techniques: freehand plain 2D US against freehand 2D US with gyroscopic guidance, both followed by 3D reconstruction using software. We measured the volume of nodules and a normal thyroid gland. RESULTS We found 2D US with gyroscopic guidance to be superior to plain 2D US as 3D reconstructions of greater accuracy are produced. The volume of the thyroid lobe measured 8.42 cm3 ± 0.94 was reasonably close to the normal average volume. However, the measured volume of the ellipsoidal nodule by the software is 8.69 cm3 ± 0.97 while the measured volume of the spherical nodule is 7.09 cm3 ± 0.79. As the expected volume of the nodules were 4.24cm3 and 4.19 cm3 respectively, the measured volume of the nodule was not accurate. The time taken to characterise nodules was reduced greatly from over 30 min in usual procedure to less than 10 min. CONCLUSION We find 3D US promising for evaluating size of thyroid nodules, with potential to study other TIRAD characteristics. Freehand 2D US with gyroscopic guidance shows the most promise for producing reliable, accurate and faster 3D reconstructions of thyroid nodules.
Collapse
Affiliation(s)
- Aldred Cheng
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - James Wai Kit Lee
- Division of Endocrine Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore
| | - Kee Yuan Ngiam
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Division of Endocrine Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore
| |
Collapse
|
9
|
Liu Z, Duan T, Zhang Y, Weng S, Xu H, Ren Y, Zhang Z, Han X. Radiogenomics: a key component of precision cancer medicine. Br J Cancer 2023; 129:741-753. [PMID: 37414827 PMCID: PMC10449908 DOI: 10.1038/s41416-023-02317-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 05/02/2023] [Accepted: 06/12/2023] [Indexed: 07/08/2023] Open
Abstract
Radiogenomics, focusing on the relationship between genomics and imaging phenotypes, has been widely applied to address tumour heterogeneity and predict immune responsiveness and progression. It is an inevitable consequence of current trends in precision medicine, as radiogenomics costs less than traditional genetic sequencing and provides access to whole-tumour information rather than limited biopsy specimens. By providing voxel-by-voxel genetic information, radiogenomics can allow tailored therapy targeting a complete, heterogeneous tumour or set of tumours. In addition to quantifying lesion characteristics, radiogenomics can also be used to distinguish benign from malignant entities, as well as patient characteristics, to better stratify patients according to disease risk, thereby enabling more precise imaging and screening. Here, we have characterised the radiogenomic application in precision medicine using a multi-omic approach. we outline the main applications of radiogenomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalised medicine. Finally, we discuss the challenges in the field of radiogenomics and the scope and clinical applicability of these methods.
Collapse
Affiliation(s)
- Zaoqu Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
- Interventional Institute of Zhengzhou University, 450052, Zhengzhou, Henan, China
- Interventional Treatment and Clinical Research Center of Henan Province, 450052, Zhengzhou, Henan, China
| | - Tian Duan
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Yuyuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Siyuan Weng
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Hui Xu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Yuqing Ren
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Zhenyu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China.
| | - Xinwei Han
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China.
- Interventional Institute of Zhengzhou University, 450052, Zhengzhou, Henan, China.
- Interventional Treatment and Clinical Research Center of Henan Province, 450052, Zhengzhou, Henan, China.
| |
Collapse
|
10
|
Inchingolo R, Maino C, Cannella R, Vernuccio F, Cortese F, Dezio M, Pisani AR, Giandola T, Gatti M, Giannini V, Ippolito D, Faletti R. Radiomics in colorectal cancer patients. World J Gastroenterol 2023; 29:2888-2904. [PMID: 37274803 PMCID: PMC10237092 DOI: 10.3748/wjg.v29.i19.2888] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/07/2023] [Accepted: 04/25/2023] [Indexed: 05/16/2023] Open
Abstract
The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.
Collapse
Affiliation(s)
- Riccardo Inchingolo
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Federica Vernuccio
- Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
| | - Francesco Cortese
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Michele Dezio
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Antonio Rosario Pisani
- Interdisciplinary Department of Medicine, Section of Nuclear Medicine, University of Bari “Aldo Moro”, Bari 70121, Italy
| | - Teresa Giandola
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| |
Collapse
|
11
|
Petrella F, Rizzo S, Attili I, Passaro A, Zilli T, Martucci F, Bonomo L, Del Grande F, Casiraghi M, De Marinis F, Spaggiari L. Stage III Non-Small-Cell Lung Cancer: An Overview of Treatment Options. Curr Oncol 2023; 30:3160-3175. [PMID: 36975452 PMCID: PMC10047909 DOI: 10.3390/curroncol30030239] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/27/2023] [Accepted: 03/06/2023] [Indexed: 03/12/2023] Open
Abstract
Lung cancer is the second-most commonly diagnosed cancer and the leading cause of cancer death worldwide. The most common histological type is non-small-cell lung cancer, accounting for 85% of all lung cancer cases. About one out of three new cases of non-small-cell lung cancer are diagnosed at a locally advanced stage—mainly stage III—consisting of a widely heterogeneous group of patients presenting significant differences in terms of tumor volume, local diffusion, and lymph nodal involvement. Stage III NSCLC therapy is based on the pivotal role of multimodal treatment, including surgery, radiotherapy, and a wide-ranging option of systemic treatments. Radical surgery is indicated in the case of hilar lymphnodal involvement or single station mediastinal ipsilateral involvement, possibly after neoadjuvant chemotherapy; the best appropriate treatment for multistation mediastinal lymph node involvement still represents a matter of debate. Although the main scope of treatments in this setting is potentially curative, the overall survival rates are still poor, ranging from 36% to 26% and 13% in stages IIIA, IIIB, and IIIC, respectively. The aim of this article is to provide an up-to-date, comprehensive overview of the state-of-the-art treatments for stage III non-small-cell lung cancer.
Collapse
Affiliation(s)
- Francesco Petrella
- Department of Thoracic Surgery, European Institute of Oncology IRCCS, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20141 Milan, Italy
- Correspondence: ; Tel.: +0039-0257489362
| | - Stefania Rizzo
- Service of Radiology, Imaging Institute of Southern Switzerland (IIMSI), EOC, Via Tesserete 46, 6900 Lugano, Switzerland
- Faculty of Biomedical Sciences, University of Italian Switzerland, Via Buffi 13, 6900 Lugano, Switzerland
| | - Ilaria Attili
- Division of Thoracic Oncology, European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Antonio Passaro
- Division of Thoracic Oncology, European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Thomas Zilli
- Faculty of Biomedical Sciences, University of Italian Switzerland, Via Buffi 13, 6900 Lugano, Switzerland
- Radiation Oncology, Oncological Institute of Southern Switzerland, EOC, 6500 Bellinzona, Switzerland
- Faculty of Medicine, University of Geneva, 1211 Geneva, Switzerland
| | - Francesco Martucci
- Radiation Oncology, Oncological Institute of Southern Switzerland, EOC, 6500 Bellinzona, Switzerland
| | - Luca Bonomo
- Service of Radiology, Imaging Institute of Southern Switzerland (IIMSI), EOC, Via Tesserete 46, 6900 Lugano, Switzerland
| | - Filippo Del Grande
- Service of Radiology, Imaging Institute of Southern Switzerland (IIMSI), EOC, Via Tesserete 46, 6900 Lugano, Switzerland
- Faculty of Biomedical Sciences, University of Italian Switzerland, Via Buffi 13, 6900 Lugano, Switzerland
| | - Monica Casiraghi
- Department of Thoracic Surgery, European Institute of Oncology IRCCS, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20141 Milan, Italy
| | - Filippo De Marinis
- Division of Thoracic Oncology, European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Lorenzo Spaggiari
- Department of Thoracic Surgery, European Institute of Oncology IRCCS, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20141 Milan, Italy
| |
Collapse
|
12
|
Verma A, Suryawanshi P, Chetan C, Oka G, Singh Y, Kallimath A, Singh P, Garegrat R. A detailed echocardiographic evaluation of ventricular functions in stable full term small for gestational age babies. J Ultrasound 2023; 26:117-127. [PMID: 35616853 PMCID: PMC10063694 DOI: 10.1007/s40477-022-00691-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 04/23/2022] [Indexed: 10/18/2022] Open
Abstract
PURPOSE SGA infants with fetal growth restriction have reduced ability to adapt themselves to the postnatal life because of certain epigenetic changes in cardiac function. The aim of the present study is to assess and compare the cardiac functions of fetal growth restricted SGA newborns to the term stable AGA newborns, and evaluate any differences in the cardiac functions during the postnatal transitional circulation. METHOD This observational study was conducted at a multispecialty tertiary care hospital in Western India from June to November 2021. The newborns were evaluated using bedside echocardiography at 24-48 h and repeat screening after 48 h. The echocardiographic assessment of the systolic function was done using EF, FS, FAC and TAPSE; diastolic function using E/A wave ratio and global functioning using LV MPI. RESULT Twnety-four babies were included in cases and 30 in the control arm of the study. Maternal and newborn characteristics were comparable between the two groups. FS, EF for left ventricle and TAPSE, FAC for right ventricular systolic function were significantly lower in SGA group (p = 0.02, 0.02, 0.00 and 0.01; respectively). The current study revealed a lower tricuspid E/A ratio and higher mitral E/A ratio with a significant difference beyond 48 h in the first week of life (p value 0.00). Left ventricular MPI was significantly higher in SGA infants compared to AGA infants during two subsequent readings in immediate newborn period with p values 0.01 and 0.02 respectively. The subgroup analysis revealed that fetal growth-restricted neonates with absent end-diastolic flow had a greater impact on ventricular functions. CONCLUSION Present study showed a significant systolic and diastolic dysfunction during initial newborn period in growth restricted SGA infants.
Collapse
Affiliation(s)
- Arjun Verma
- Department of Neonatology, Bharati Vidyapeeth University Medical College, Hospital and Research Center, Pune, Maharashtra, India
| | - Pradeep Suryawanshi
- Department of Neonatology, Bharati Vidyapeeth University Medical College, Hospital and Research Center, Pune, Maharashtra, India.
| | - Chinmay Chetan
- Department of Neonatology, Himalayan Institute of Medical Sciences, Dehradun, Uttarakhand, India
| | - Gauri Oka
- Department of Research, Bharati Vidyapeeth University Medical College, Hospital and Research Center, Pune, Maharashtra, India
| | - Yogen Singh
- Department of Pediatrics, Division of Neonatology, Loma Linda University School of Medicine, Loma Linda, CA, 92354, USA
| | - Aditya Kallimath
- Department of Neonatology, Bharati Vidyapeeth University Medical College, Hospital and Research Center, Pune, Maharashtra, India
| | - Pari Singh
- Department of Neonatology, Bharati Vidyapeeth University Medical College, Hospital and Research Center, Pune, Maharashtra, India
| | - Reema Garegrat
- Department of Neonatology, Bharati Vidyapeeth University Medical College, Hospital and Research Center, Pune, Maharashtra, India
| |
Collapse
|
13
|
Duyan M, Saridas A. Relation between partial arterial carbon dioxide pressure and pH value and optic nerve sheath diameter: a prospective self-controlled non-randomized trial study. J Ultrasound 2023; 26:107-116. [PMID: 35511351 PMCID: PMC10063762 DOI: 10.1007/s40477-022-00677-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 03/14/2022] [Indexed: 12/17/2022] Open
Abstract
OBJECTIVE This study aims to determine if there is a correlation between differences in optic nerve sheath diameter (ONSD) and changes in PaCO2 and pH values that were measured in the arterial blood gas (ABG) before and after treatment in COPD patients with acute hypercarbic respiratory failure (AHRF). MATERIALS AND METHODS This study serves as a prospective self-controlled non-randomized trial study conducted in the emergency clinic of a tertiary hospital. Forty-four patients with COPD, who were found to have acidosis and hypercarbia in ABG and had an indication for non-invasive mechanic ventilation (NIMV), were analyzed prospectively. Demographic information, vital findings, initial ABG values, the ONSD measurement (before the NIMV treatment), consciousness state, and the ABG results obtained in the second hour of the monitoring and the ONSD measurement (after the NIMV treatment) were recorded. RESULTS In this study, 13 (29.5%) of the patients were female and 31 (70.5%) were male. The age distribution was evaluated as 68.3 ± 9.2 years; the minimum age was 54 and the maximum was 91. A high level of positively significant correlation was found between the mean ONSD and PaCO2 (p < 0.0001). There is a high fit (0.72) between the mean ONSD and PaCO2. A moderate level of negatively significant correlation was found between the mean ONSD and the pH (p < 0.0001). However, there is an insignificant low fit (0.32) between the mean ONSD and the pH. CONCLUSION The ONSD changed significantly and in a highly correlated manner to acute changes in PaCO2 levels.
Collapse
Affiliation(s)
- Murat Duyan
- Department of Emergency Medicine, Emergency Medicine Specialist, Antalya Training and Research Hospital, Varlik District, Kazim Karabekir Street, 07100 Antalya, Turkey
| | - Ali Saridas
- Department of Emergency Medicine, Emergency Medicine Specialist, Prof. Dr. Cemil Taşçıoğlu City Hospital, Istanbul, Turkey
| |
Collapse
|
14
|
Intratumoral and peritumoral radiomics nomograms for the preoperative prediction of lymphovascular invasion and overall survival in non-small cell lung cancer. Eur Radiol 2023; 33:947-958. [PMID: 36064979 DOI: 10.1007/s00330-022-09109-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 06/03/2022] [Accepted: 07/24/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVES To evaluate the predictive value of intratumoral and peritumoral radiomics and radiomics nomogram for preoperative lymphovascular invasion (LVI) status and overall survival (OS) in patients with non-small cell lung cancer (NSCLC). METHODS In total, 240 NSCLC patients from our institution were randomly divided into the training cohort (n = 145) and internal validation cohort (n = 95) with a ratio of 6:4, and 65 patients from the Cancer Imaging Archive were enrolled as the external validation cohort. We extracted 1217 CT-based radiomics features from the gross tumor volume (GTV) and gross tumor volume incorporating peritumoral 3, 6, and 9 mm regions (GPTV3, GPTV6, GPTV9). A radiomics nomogram based on clinical independent predictors and radiomics score (Radscore) of the best radiomics model was constructed. The correlation between factors and OS was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis. RESULTS Compared with GTV, GPTV3, and GPTV6 radiomics models, GPTV9 radiomics model exhibited better prediction performance with the AUCs of 0.82, 0.75, and 0.67 in the training, internal validation, and external validation cohorts, respectively. In the clinical model, smoking and clinical stage were independent predictors. The nomogram incorporating independent predictors and GPTV9-Radscore was clinically useful, with the AUCs of 0.89, 0.83, and 0.66 in three cohorts. Pathological LVI, GPTV9-Radscore-predicted, and Nomoscore-predicted LVI were associated with poor OS (p < 0.05). CONCLUSIONS CT-based radiomics nomogram can predict LVI and OS in patients with NSCLC and may help in making personalized treatment strategies before surgery. KEY POINTS • Compared with GTV, GPTV3, and GPTV6 radiomics models, GPTV9 radiomics model showed better prediction performance for LVI status in NSCLC. • The radiomics nomogram based on GPTV9 radiomics features and clinical independent predictors could effectively predict LVI status and OS in NSCLC and outperformed the clinical model. • The radiomics nomogram had a wider scope of clinical application.
Collapse
|
15
|
Bicci E, Cozzi D, Cavigli E, Ruzga R, Bertelli E, Danti G, Bettarini S, Tortoli P, Mazzoni LN, Busoni S, Miele V. Reproducibility of CT radiomic features in lung neuroendocrine tumours (NETs) patients: analysis in a heterogeneous population. LA RADIOLOGIA MEDICA 2023; 128:203-211. [PMID: 36637739 PMCID: PMC9938819 DOI: 10.1007/s11547-023-01592-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 01/04/2023] [Indexed: 01/14/2023]
Abstract
BACKGROUND The aim is to find a correlation between texture features extracted from neuroendocrine (NET) lung cancer subtypes, both Ki-67 index and the presence of lymph-nodal mediastinal metastases detected while using different computer tomography (CT) scanners. METHODS Sixty patients with a confirmed pulmonary NET histological diagnosis, a known Ki-67 status and metastases, were included. After subdivision of primary lesions in baseline acquisition and venous phase, 107 radiomic features of first and higher orders were extracted. Spearman's correlation matrix with Ward's hierarchical clustering was applied to confirm the absence of bias due to the database heterogeneity. Nonparametric tests were conducted to identify statistically significant features in the distinction between patient groups (Ki-67 < 3-Group 1; 3 ≤ Ki-67 ≤ 20-Group 2; and Ki-67 > 20-Group 3, and presence of metastases). RESULTS No bias arising from sample heterogeneity was found. Regarding Ki-67 groups statistical tests, seven statistically significant features (p value < 0.05) were found in post-contrast enhanced CT; three in baseline acquisitions. In metastasis classes distinction, three features (first-order class) were statistically significant in post-contrast acquisitions and 15 features (second-order class) in baseline acquisitions, including the three features distinguishing between Ki-67 groups in baseline images (MCC, ClusterProminence and Strength). CONCLUSIONS Some radiomic features can be used as a valid and reproducible tool for predicting Ki-67 class and hence the subtype of lung NET in baseline and post-contrast enhanced CT images. In particular, in baseline examination three features can establish both tumour class and aggressiveness.
Collapse
Affiliation(s)
- Eleonora Bicci
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Diletta Cozzi
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Edoardo Cavigli
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Ron Ruzga
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Elena Bertelli
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Ginevra Danti
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Silvia Bettarini
- Department of Health Physics, L.Go Brambilla, Careggi University Hospital, 50134 Florence, Italy
| | - Paolo Tortoli
- Department of Health Physics, L.Go Brambilla, Careggi University Hospital, 50134 Florence, Italy
| | - Lorenzo Nicola Mazzoni
- Department of Health Physics, AUSL Toscana Centro, Via Ciliegiole 97, 51100 Pistoia, Italy
| | - Simone Busoni
- Department of Health Physics, L.Go Brambilla, Careggi University Hospital, 50134 Florence, Italy
| | - Vittorio Miele
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| |
Collapse
|
16
|
He Z, Chen J, Yang F, Pan X, Liu C. Computed tomography-based texture assessment for the differentiation of benign, borderline, and early-stage malignant ovarian neoplasms. J Int Med Res 2023; 51:3000605221150139. [PMID: 36688472 PMCID: PMC9893092 DOI: 10.1177/03000605221150139] [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] [Indexed: 01/24/2023] Open
Abstract
OBJECTIVE This study was performed to examine the value of computed tomography-based texture assessment for characterizing different types of ovarian neoplasms. METHODS This retrospective study involved 225 patients with histopathologically confirmed ovarian tumors after surgical resection. Two different data sets of thick (5-mm) slices (during regular and portal venous phases) were analyzed. Raw data analysis, principal component analysis, linear discriminant analysis, and nonlinear discriminant analysis were performed to classify ovarian tumors. The radiologist's misclassification rate was compared with the MaZda (texture analysis software) findings. The results were validated with the neural network classifier. Receiver operating characteristic curves were analyzed to determine the performances of different parameters. RESULTS Nonlinear discriminant analysis had a lower misclassification rate than the other analyses. Thirty texture parameters significantly differed between the two groups. In the training set, WavEnLH_s-3 and WavEnHL_s-3 were the optimal texture features during the regular phase, while WavEnHH_s-4 and Kurtosis seemed to be the most discriminative features during the portal venous phase. In the validation test, benign versus malignant tumors and benign versus borderline lesions were well-distinguished. CONCLUSIONS Computed tomography-based texture features provide a useful imaging signature that may assist in differentiating benign, borderline, and early-stage ovarian cancer.
Collapse
Affiliation(s)
- Ziying He
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Jia Chen
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Fei Yang
- Department of Clinical Medical, Guangxi Medical University, Nanning, China
| | - Xinwei Pan
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Chanzhen Liu
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, Nanning, China,Chanzhen Liu, Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Qingxiu District, Nanning 530021, China.
| |
Collapse
|
17
|
Ge G, Zhang J. Feature selection methods and predictive models in CT lung cancer radiomics. J Appl Clin Med Phys 2023; 24:e13869. [PMID: 36527376 PMCID: PMC9860004 DOI: 10.1002/acm2.13869] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/31/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.
Collapse
Affiliation(s)
- Gary Ge
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
| | - Jie Zhang
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
| |
Collapse
|
18
|
Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models. J Clin Med 2022; 11:jcm11247334. [PMID: 36555950 PMCID: PMC9784875 DOI: 10.3390/jcm11247334] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/05/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
Radiomics investigates the predictive role of quantitative parameters calculated from radiological images. In oncology, tumour segmentation constitutes a crucial step of the radiomic workflow. Manual segmentation is time-consuming and prone to inter-observer variability. In this study, a state-of-the-art deep-learning network for automatic segmentation (nnU-Net) was applied to computed tomography images of lung tumour patients, and its impact on the performance of survival radiomic models was assessed. In total, 899 patients were included, from two proprietary and one public datasets. Different network architectures (2D, 3D) were trained and tested on different combinations of the datasets. Automatic segmentations were compared to reference manual segmentations performed by physicians using the DICE similarity coefficient. Subsequently, the accuracy of radiomic models for survival classification based on either manual or automatic segmentations were compared, considering both hand-crafted and deep-learning features. The best agreement between automatic and manual contours (DICE = 0.78 ± 0.12) was achieved averaging 2D and 3D predictions and applying customised post-processing. The accuracy of the survival classifier (ranging between 0.65 and 0.78) was not statistically different when using manual versus automatic contours, both with hand-crafted and deep features. These results support the promising role nnU-Net can play in automatic segmentation, accelerating the radiomic workflow without impairing the models' accuracy. Further investigations on different clinical endpoints and populations are encouraged to confirm and generalise these findings.
Collapse
|
19
|
Huang YM, Wang TE, Chen MJ, Lin CC, Chang CW, Tai HC, Hsu SM, Chen YJ. Radiomics-based nomogram as predictive model for prognosis of hepatocellular carcinoma with portal vein tumor thrombosis receiving radiotherapy. Front Oncol 2022; 12:906498. [PMID: 36203419 PMCID: PMC9530279 DOI: 10.3389/fonc.2022.906498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 08/26/2022] [Indexed: 12/04/2022] Open
Abstract
Background This study aims to establish and validate a predictive model based on radiomics features, clinical features, and radiation therapy (RT) dosimetric parameters for overall survival (OS) in hepatocellular carcinoma (HCC) patients treated with RT for portal vein tumor thrombosis (PVTT). Methods We retrospectively reviewed 131 patients. Patients were randomly divided into the training (n = 105) and validation (n = 26) cohorts. The clinical target volume was contoured on pre-RT computed tomography images and 48 textural features were extracted. The least absolute shrinkage and selection operator regression was used to determine the radiomics score (rad-score). A nomogram based on rad-score, clinical features, and dosimetric parameters was developed using the results of multivariate regression analysis. The predictive nomogram was evaluated using Harrell’s concordance index (C-index), area under the curve (AUC), and calibration curve. Results Two radiomics features were extracted to calculate the rad-score for the prediction of OS. The radiomics-based nomogram had better performance than the clinical nomogram for the prediction of OS, with a C-index of 0.73 (95% CI, 0.67–0.79) and an AUC of 0.71 (95% CI, 0.62–0.79). The predictive accuracy was assessed by a calibration curve. Conclusion The radiomics-based predictive model significantly improved OS prediction in HCC patients treated with RT for PVTT.
Collapse
Affiliation(s)
- Yu-Ming Huang
- Department of Radiation Oncology, Taipei Hospital, Ministry of Health and Welfare, New Taipei City, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Tsang-En Wang
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Artificial Intelligence and Medical Application, MacKay Junior College of Medicine, Nursing, and Management, New Taipei City, Taiwan
| | - Ming-Jen Chen
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Artificial Intelligence and Medical Application, MacKay Junior College of Medicine, Nursing, and Management, New Taipei City, Taiwan
| | - Ching-Chung Lin
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Artificial Intelligence and Medical Application, MacKay Junior College of Medicine, Nursing, and Management, New Taipei City, Taiwan
| | - Ching-Wei Chang
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Artificial Intelligence and Medical Application, MacKay Junior College of Medicine, Nursing, and Management, New Taipei City, Taiwan
| | - Hung-Chi Tai
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Radiation Oncology, MacKay Memorial Hospital, Taipei, Taiwan
| | - Shih-Ming Hsu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
- *Correspondence: Yu-Jen Chen, ; Shih-Ming Hsu,
| | - Yu-Jen Chen
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- Department of Artificial Intelligence and Medical Application, MacKay Junior College of Medicine, Nursing, and Management, New Taipei City, Taiwan
- Department of Radiation Oncology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medical Research, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
- *Correspondence: Yu-Jen Chen, ; Shih-Ming Hsu,
| |
Collapse
|
20
|
Zheng X, He B, Hu Y, Ren M, Chen Z, Zhang Z, Ma J, Ouyang L, Chu H, Gao H, He W, Liu T, Li G. Diagnostic Accuracy of Deep Learning and Radiomics in Lung Cancer Staging: A Systematic Review and Meta-Analysis. Front Public Health 2022; 10:938113. [PMID: 35923964 PMCID: PMC9339706 DOI: 10.3389/fpubh.2022.938113] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 06/15/2022] [Indexed: 12/24/2022] Open
Abstract
BackgroundArtificial intelligence has far surpassed previous related technologies in image recognition and is increasingly used in medical image analysis. We aimed to explore the diagnostic accuracy of the models based on deep learning or radiomics for lung cancer staging.MethodsStudies were systematically reviewed using literature searches from PubMed, EMBASE, Web of Science, and Wanfang Database, according to PRISMA guidelines. Studies about the diagnostic accuracy of radiomics and deep learning, including the identifications of lung cancer, tumor types, malignant lung nodules and lymph node metastase, were included. After identifying the articles, the methodological quality was assessed using the QUADAS-2 checklist. We extracted the characteristic of each study; the sensitivity, specificity, and AUROC for lung cancer diagnosis were summarized for subgroup analysis.ResultsThe systematic review identified 19 eligible studies, of which 14 used radiomics models and 5 used deep learning models. The pooled AUROC of 7 studies to determine whether patients had lung cancer was 0.83 (95% CI 0.78–0.88). The pooled AUROC of 9 studies to determine whether patients had NSCLC was 0.78 (95% CI 0.73–0.83). The pooled AUROC of the 6 studies that determined patients had malignant lung nodules was 0.79 (95% CI 0.77–0.82). The pooled AUROC of the other 6 studies that determined whether patients had lymph node metastases was 0.74 (95% CI 0.66–0.82).ConclusionThe models based on deep learning or radiomics have the potential to improve diagnostic accuracy for lung cancer staging.Systematic Review Registrationhttps://inplasy.com/inplasy-2022-3-0167/, identifier: INPLASY202230167.
Collapse
Affiliation(s)
- Xiushan Zheng
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Bo He
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Yunhai Hu
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Min Ren
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Zhiyuan Chen
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Zhiguang Zhang
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Jun Ma
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Lanwei Ouyang
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Hongmei Chu
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Huan Gao
- Department of Thoracic Surgery, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
| | - Wenjing He
- School of Electronic Engineering, Chengdu University of Technology, Chengdu, China
| | - Tianhu Liu
- Department of Cardiology, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
- *Correspondence: Tianhu Liu
| | - Gang Li
- Department of Cardiology, The 3rd Affiliated Hospital of Chengdu Medical College, Pidu District People's Hospital, Chengdu, China
- Gang Li
| |
Collapse
|
21
|
Caruso D, Polici M, Zerunian M, Del Gaudio A, Parri E, Giallorenzi MA, De Santis D, Tarantino G, Tarallo M, Dentice di Accadia FM, Iannicelli E, Garbarino GM, Canali G, Mercantini P, Fiori E, Laghi A. Radiomic Cancer Hallmarks to Identify High-Risk Patients in Non-Metastatic Colon Cancer. Cancers (Basel) 2022; 14:cancers14143438. [PMID: 35884499 PMCID: PMC9319440 DOI: 10.3390/cancers14143438] [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: 05/28/2022] [Revised: 07/07/2022] [Accepted: 07/13/2022] [Indexed: 11/16/2022] Open
Abstract
The study was aimed to develop a radiomic model able to identify high-risk colon cancer by analyzing pre-operative CT scans. The study population comprised 148 patients: 108 with non-metastatic colon cancer were retrospectively enrolled from January 2015 to June 2020, and 40 patients were used as the external validation cohort. The population was divided into two groups—High-risk and No-risk—following the presence of at least one high-risk clinical factor. All patients had baseline CT scans, and 3D cancer segmentation was performed on the portal phase by two expert radiologists using open-source software (3DSlicer v4.10.2). Among the 107 radiomic features extracted, stable features were selected to evaluate the inter-class correlation (ICC) (cut-off ICC > 0.8). Stable features were compared between the two groups (T-test or Mann−Whitney), and the significant features were selected for univariate and multivariate logistic regression to build a predictive radiomic model. The radiomic model was then validated with an external cohort. In total, 58/108 were classified as High-risk and 50/108 as No-risk. A total of 35 radiomic features were stable (0.81 ≤ ICC < 0.92). Among these, 28 features were significantly different between the two groups (p < 0.05), and only 9 features were selected to build the radiomic model. The radiomic model yielded an AUC of 0.73 in the internal cohort and 0.75 in the external cohort. In conclusion, the radiomic model could be seen as a performant, non-invasive imaging tool to properly stratify colon cancers with high-risk disease.
Collapse
Affiliation(s)
- Damiano Caruso
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (M.P.); (M.Z.); (A.D.G.); (E.P.); (M.A.G.); (D.D.S.); (E.I.); (A.L.)
- Correspondence: ; Tel.: +39-0633775285
| | - Michela Polici
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (M.P.); (M.Z.); (A.D.G.); (E.P.); (M.A.G.); (D.D.S.); (E.I.); (A.L.)
| | - Marta Zerunian
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (M.P.); (M.Z.); (A.D.G.); (E.P.); (M.A.G.); (D.D.S.); (E.I.); (A.L.)
| | - Antonella Del Gaudio
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (M.P.); (M.Z.); (A.D.G.); (E.P.); (M.A.G.); (D.D.S.); (E.I.); (A.L.)
| | - Emanuela Parri
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (M.P.); (M.Z.); (A.D.G.); (E.P.); (M.A.G.); (D.D.S.); (E.I.); (A.L.)
| | - Maria Agostina Giallorenzi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (M.P.); (M.Z.); (A.D.G.); (E.P.); (M.A.G.); (D.D.S.); (E.I.); (A.L.)
| | - Domenico De Santis
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (M.P.); (M.Z.); (A.D.G.); (E.P.); (M.A.G.); (D.D.S.); (E.I.); (A.L.)
| | - Giulia Tarantino
- Surgery Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (G.T.); (G.M.G.); (G.C.); (P.M.)
| | - Mariarita Tarallo
- Department of Surgery “Pietro Valdoni”, Sapienza University of Rome, 00161 Rome, Italy; (M.T.); (F.M.D.d.A.); (E.F.)
| | | | - Elsa Iannicelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (M.P.); (M.Z.); (A.D.G.); (E.P.); (M.A.G.); (D.D.S.); (E.I.); (A.L.)
| | - Giovanni Maria Garbarino
- Surgery Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (G.T.); (G.M.G.); (G.C.); (P.M.)
| | - Giulia Canali
- Surgery Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (G.T.); (G.M.G.); (G.C.); (P.M.)
| | - Paolo Mercantini
- Surgery Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (G.T.); (G.M.G.); (G.C.); (P.M.)
| | - Enrico Fiori
- Department of Surgery “Pietro Valdoni”, Sapienza University of Rome, 00161 Rome, Italy; (M.T.); (F.M.D.d.A.); (E.F.)
| | - Andrea Laghi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (M.P.); (M.Z.); (A.D.G.); (E.P.); (M.A.G.); (D.D.S.); (E.I.); (A.L.)
| |
Collapse
|
22
|
Wang L, Liu A, Wang Z, Xu N, Zhou D, Qu T, Liu G, Wang J, Yang F, Guo X, Chi W, Xue F. A Prognostic Model of Non-Small Cell Lung Cancer With a Radiomics Nomogram in an Eastern Chinese Population. Front Oncol 2022; 12:816766. [PMID: 35774128 PMCID: PMC9237399 DOI: 10.3389/fonc.2022.816766] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 05/11/2022] [Indexed: 12/21/2022] Open
Abstract
Background The aim of this study was to build and validate a radiomics nomogram by integrating the radiomics features extracted from the CT images and known clinical variables (TNM staging, etc.) to individually predict the overall survival (OS) of patients with non-small cell lung cancer (NSCLC). Methods A total of 1,480 patients with clinical data and pretreatment CT images during January 2013 and May 2018 were enrolled in this study. We randomly assigned the patients into training (N = 1036) and validation cohorts (N = 444). We extracted 1,288 quantitative features from the CT images of each patient. The Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model was applied in feature selection and radiomics signature building. The radiomics nomogram used for the prognosis prediction was built by combining the radiomics signature and clinical variables that were derived from clinical data. Calibration ability and discrimination ability were analyzed in both training and validation cohorts. Results Eleven radiomics features were selected by LASSO Cox regression derived from CT images, and the radiomics signature was built in the training cohort. The radiomics signature was significantly associated with NSCLC patients’ OS (HR = 3.913, p < 0.01). The radiomics nomogram combining the radiomics signature with six clinical variables (age, sex, chronic obstructive pulmonary disease, T stage, N stage, and M stage) had a better prognostic performance than the clinical nomogram both in the training cohort (C-index, 0.861, 95% CI: 0.843–0.879 vs. C-index, 0.851, 95% CI: 0.832–0.870; p < 0.001) and in the validation cohort (C-index, 0.868, 95% CI: 0.841–0.896 vs. C-index, 0.854, 95% CI: 0.824–0.884; p = 0.002). The calibration curves demonstrated optimal alignment between the prediction and actual observation. Conclusion The established radiomics nomogram could act as a noninvasive prediction tool for individualized survival prognosis estimation in patients with NSCLC. The radiomics signature derived from CT images may help clinicians in decision-making and hold promise to be adopted in the patient care setting as well as the clinical trial setting.
Collapse
Affiliation(s)
- Lijie Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ailing Liu
- Department of Pulmonary and Critical Care Medicine, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Zhiheng Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong Provincial Key Laboratory of Immunohematology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ning Xu
- Department of Pulmonary and Critical Care Medicine, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Dandan Zhou
- Department of Pulmonary and Critical Care Medicine, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Tao Qu
- Department of Pulmonary and Critical Care Medicine, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Guiyuan Liu
- Department of Radiology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Jingtao Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Department of Hematology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Fujun Yang
- Department of Oncology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Xiaolei Guo
- The Department for Chronic and Non-Communicable Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, China
| | - Weiwei Chi
- National Administration of Health Data, Jinan, China
- *Correspondence: Weiwei Chi, ; Fuzhong Xue,
| | - Fuzhong Xue
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Shandong University, Jinan, China
- *Correspondence: Weiwei Chi, ; Fuzhong Xue,
| |
Collapse
|
23
|
Buonsenso D, De Rose C, Morello R, Lazzareschi I, Valentini P. Aspiration pneumonia in children with neurological disorders: a new indication for lung ultrasound? A case series. J Ultrasound 2022; 25:325-331. [PMID: 32757145 PMCID: PMC9148346 DOI: 10.1007/s40477-020-00520-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 07/28/2020] [Indexed: 12/27/2022] Open
Abstract
Aspiration pneumonia is a common cause of morbidity and mortality in both adults and children that, however, is difficult to accurately diagnose. In current literature, there are no reports or clinical research study focused on the possible use of lung ultrasound (LUS) in the diagnosis and follow-up of aspiration pneumonia in children. In this case series, we describe clinical, laboratory, radiological results as well as detailed lung ultrasound findings of three children with severe disability and diagnosed with aspiration pneumonia. In these three cases, albeit at different times, LUS played an important role in both the initial diagnostic process and follow-up.
Collapse
Affiliation(s)
- Danilo Buonsenso
- Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario "A. Gemelli", IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Cristina De Rose
- Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario "A. Gemelli", IRCCS, Rome, Italy.
| | - Rosa Morello
- Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario "A. Gemelli", IRCCS, Rome, Italy
| | - Ilaria Lazzareschi
- Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario "A. Gemelli", IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Piero Valentini
- Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario "A. Gemelli", IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| |
Collapse
|
24
|
Rinaldi L, Pezzotta F, Santaniello T, De Marco P, Bianchini L, Origgi D, Cremonesi M, Milani P, Mariani M, Botta F. HeLLePhant: A phantom mimicking non-small cell lung cancer for texture analysis in CT images. Phys Med 2022; 97:13-24. [PMID: 35334407 DOI: 10.1016/j.ejmp.2022.03.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 02/01/2022] [Accepted: 03/14/2022] [Indexed: 01/06/2023] Open
Abstract
PURPOSE Phantoms mimicking human tissue heterogeneity and intensity are required to establish radiomic features robustness in Computed Tomography (CT) images. We developed inserts with two different techniques for the radiomic study of Non-Small Cell Lung Cancer (NSCLC) lesions. METHODS We developed two insert prototypes: two 3D-printed made of glycol-modified polyethylene terephthalate (PET-G), and nine with sodium polyacrylate plus iodinated contrast medium. The inserts were put in a handcraft phantom (HeLLePhant). We also analysed four materials of a commercial homogeneous phantom (Catphan® 424) and collected 29 NSCLC patients for comparison. All the CT acquisitions were performed with the same clinical protocol and scanner at 120kVp. The HeLLePhant phantom was scanned ten times in fixed condition at 120kVp and 100kVp for repeatability investigation. We extracted 153 radiomic features using Pyradiomics. To compare the features between phantoms and patients, we computed how many phantom features fell in the range between 10th and 90th percentile of the corresponding patient values. We deemed repeatable the features with a coefficient of variation (CV) less than or equal to 0.10. RESULTS The best similarity with the patients was obtained with the polyacrylate inserts (55.6-90.2%), the worst with Catphan (15.7-19.0%). For the PET-G inserts 35.3% and 36.6% of the features match the patient range. We found high repeatability for all the inserts of the HeLLePhant phantom (74.3-100% at 120kVp, 75.7-97.9% at 100kVp), and observed a texture dependency in repeatability. CONCLUSIONS Our study shows a promising way to construct heterogeneous inserts mimicking a target tissue for radiomic studies.
Collapse
Affiliation(s)
- Lisa Rinaldi
- Department of Physics, Università degli Studi di Pavia and INFN, via Bassi 6, 27100 Pavia, Italy; Radiation Research Unit, IEO, European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy.
| | - Federico Pezzotta
- CIMaINa, Department of Physics, Università degli Studi di Milano, via Celoria 16, 20133, Milan, Italy
| | - Tommaso Santaniello
- CIMaINa, Department of Physics, Università degli Studi di Milano, via Celoria 16, 20133, Milan, Italy
| | - Paolo De Marco
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Linda Bianchini
- Department of Physics, Università degli Studi di Milano, via Celoria 16, 20133, Milan, Italy
| | - Daniela Origgi
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Marta Cremonesi
- Radiation Research Unit, IEO, European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Paolo Milani
- CIMaINa, Department of Physics, Università degli Studi di Milano, via Celoria 16, 20133, Milan, Italy
| | - Manuel Mariani
- Department of Physics, Università degli Studi di Pavia and INFN, via Bassi 6, 27100 Pavia, Italy
| | - Francesca Botta
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| |
Collapse
|
25
|
Reproducibility of radiomic features in CT images of NSCLC patients: an integrative analysis on the impact of acquisition and reconstruction parameters. Eur Radiol Exp 2022; 6:2. [PMID: 35075539 PMCID: PMC8786992 DOI: 10.1186/s41747-021-00258-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 12/16/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND We investigated to what extent tube voltage, scanner model, and reconstruction algorithm affect radiomic feature reproducibility in a single-institution retrospective database of computed tomography images of non-small-cell lung cancer patients. METHODS This study was approved by the Institutional Review Board (UID 2412). Images of 103 patients were considered, being acquired on either among two scanners, at 100 or 120 kVp. For each patient, images were reconstructed with six iterative blending levels, and 1414 features were extracted from each reconstruction. At univariate analysis, Wilcoxon-Mann-Whitney test was applied to evaluate feature differences within scanners and voltages, whereas the impact of the reconstruction was established with the overall concordance correlation coefficient (OCCC). A multivariable mixed model was also applied to investigate the independent contribution of each acquisition/reconstruction parameter. Univariate and multivariable analyses were combined to analyse feature behaviour. RESULTS Scanner model and voltage did not affect features significantly. The reconstruction blending level showed a significant impact at both univariate analysis (154/1414 features yielding an OCCC < 0.85) and multivariable analysis, with most features (1042/1414) revealing a systematic trend with the blending level (multiple comparisons adjusted p < 0.05). Reproducibility increased in association to image processing with smooth filters, nonetheless specific investigation in relation to clinical endpoints should be performed to ensure that textural information is not removed. CONCLUSIONS Combining univariate and multivariable models is allowed to identify features for which corrections may be applied to reduce the trend with the algorithm and increase reproducibility. Subsequent clustering may be applied to eliminate residual redundancy.
Collapse
|
26
|
Le VH, Kha QH, Hung TNK, Le NQK. Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer. Cancers (Basel) 2021; 13:cancers13143616. [PMID: 34298828 PMCID: PMC8304936 DOI: 10.3390/cancers13143616] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/14/2021] [Accepted: 07/16/2021] [Indexed: 12/17/2022] Open
Abstract
Simple Summary Despite recent advancements in lung cancer treatment, individuals with lung cancer have a dismal 5-year survival rate of only 15%. In patients with non-small cell lung cancer (NSCLC), medical images have lately been employed as a valuable marker for predicting overall survival. The primary goal of this study was to develop a risk score based on computed tomography (CT) based radiomics feature signatures that may be used to predict survival in NSCLC patients. After analyzing 577 NSCLC patients from two data sets, we discovered that the risk score model’s prediction ability as a prognostic indicator was superior to other clinical indicators (age, stage, and gender), and the possibility of patient risk stratification with survival was evaluated using a risk score representation of 10 radiomics signatures. According to this study, the risk score generated using CT-based radiomics signatures promises to predict overall survival in NSCLC patients. Abstract This study aimed to create a risk score generated from CT-based radiomics signatures that could be used to predict overall survival in patients with non-small cell lung cancer (NSCLC). We retrospectively enrolled three sets of NSCLC patients (including 336, 84, and 157 patients for training, testing, and validation set, respectively). A total of 851 radiomics features for each patient from CT images were extracted for further analyses. The most important features (strongly linked with overall survival) were chosen by pairwise correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and univariate Cox proportional hazard regression. Multivariate Cox proportional hazard model survival analysis was used to create risk scores for each patient, and Kaplan–Meier was used to separate patients into two groups: high-risk and low-risk, respectively. ROC curve assessed the prediction ability of the risk score model for overall survival compared to clinical parameters. The risk score, which developed from ten radiomics signatures model, was found to be independent of age, gender, and stage for predicting overall survival in NSCLC patients (HR, 2.99; 95% CI, 2.27–3.93; p < 0.001) and overall survival prediction ability was 0.696 (95% CI, 0.635–0.758), 0.705 (95% CI, 0.649–0.762), 0.657 (95% CI, 0.589–0.726) (AUC) for 1, 3, and 5 years, respectively, in the training set. The risk score is more likely to have a better accuracy in predicting survival at 1, 3, and 5 years than clinical parameters, such as age 0.57 (95% CI, 0.499–0.64), 0.552 (95% CI, 0.489–0.616), 0.621 (95% CI, 0.544–0.689) (AUC); gender 0.554, 0.546, 0.566 (AUC); stage 0.527, 0.501, 0.459 (AUC), respectively, in 1, 3 and 5 years in the training set. In the training set, the Kaplan–Meier curve revealed that NSCLC patients in the high-risk group had a lower overall survival time than the low-risk group (p < 0.001). We also had similar results that were statistically significant in the testing and validation set. In conclusion, risk scores developed from ten radiomics signatures models have great potential to predict overall survival in NSCLC patients compared to the clinical parameters. This model was able to stratify NSCLC patients into high-risk and low-risk groups regarding the overall survival prediction.
Collapse
Affiliation(s)
- Viet-Huan Le
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan; (V.-H.L.); (Q.-H.K.); (T.N.K.H.)
- Department of Thoracic Surgery, Khanh Hoa General Hospital, Nha Trang City 65000, Vietnam
| | - Quang-Hien Kha
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan; (V.-H.L.); (Q.-H.K.); (T.N.K.H.)
| | - Truong Nguyen Khanh Hung
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan; (V.-H.L.); (Q.-H.K.); (T.N.K.H.)
- Department of Orthopedic and Trauma, Cho Ray Hospital, Ho Chi Minh City 70000, Vietnam
| | - Nguyen Quoc Khanh Le
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan; (V.-H.L.); (Q.-H.K.); (T.N.K.H.)
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 106, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan
- Correspondence: ; Tel.: +886-2-66382736 (ext. 1992); Fax: +886-02-27321956
| |
Collapse
|
27
|
Manganaro L, Nicolino GM, Dolciami M, Martorana F, Stathis A, Colombo I, Rizzo S. Radiomics in cervical and endometrial cancer. Br J Radiol 2021; 94:20201314. [PMID: 34233456 PMCID: PMC9327743 DOI: 10.1259/bjr.20201314] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Radiomics is an emerging field of research that aims to find associations between quantitative information extracted from imaging examinations and clinical data to support the best clinical decision. In the last few years, some papers have been evaluating the role of radiomics in gynecological malignancies, mainly focusing on ovarian cancer. Nonetheless, cervical cancer is the most frequent gynecological malignancy in developing countries and endometrial cancer is the most common in western countries. The purpose of this narrative review is to give an overview of the latest published papers evaluating the role of radiomics in cervical and endometrial cancer, mostly evaluating association with tumor prognostic factors, with response to therapy and with prediction of recurrence and distant metastasis.
Collapse
Affiliation(s)
- Lucia Manganaro
- Department of Radiological, Oncological and Pathological Sciences; University of Rome Sapienza (IT), Rome, Italy
| | - Gabriele Maria Nicolino
- Post-graduate School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, Milan, Italy
| | - Miriam Dolciami
- Department of Radiological, Oncological and Pathological Sciences; University of Rome Sapienza (IT), Rome, Italy
| | - Federica Martorana
- Oncology Institute of Southern Switzerland, San Giovanni Hospital, 6500 Bellinzona, (CH), Switzerland
| | - Anastasios Stathis
- Oncology Institute of Southern Switzerland, San Giovanni Hospital, 6500 Bellinzona, (CH), Switzerland.,Facoltà di Scienze biomediche, Università della Svizzera italiana (USI), Via Buffi 13, 6900, Lugano (CH), Switzerland
| | - Ilaria Colombo
- Oncology Institute of Southern Switzerland, San Giovanni Hospital, 6500 Bellinzona, (CH), Switzerland
| | - Stefania Rizzo
- Facoltà di Scienze biomediche, Università della Svizzera italiana (USI), Via Buffi 13, 6900, Lugano (CH), Switzerland.,Istituto di Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale, Via Tesserete 46, Lugano (CH), Switzerland
| |
Collapse
|
28
|
Liu G, Xu Z, Zhang Y, Jiang B, Zhang L, Wang L, de Bock GH, Vliegenthart R, Xie X. Machine-Learning-Derived Nomogram Based on 3D Radiomic Features and Clinical Factors Predicts Progression-Free Survival in Lung Adenocarcinoma. Front Oncol 2021; 11:692329. [PMID: 34249741 PMCID: PMC8260977 DOI: 10.3389/fonc.2021.692329] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 06/07/2021] [Indexed: 01/06/2023] Open
Abstract
Background To establish a machine-learning-derived nomogram based on radiomic features and clinical factors to predict post-surgical 2-year progression-free survival (PFS) in patients with lung adenocarcinoma. Methods Patients with >2 years post-surgical prognosis results of lung adenocarcinoma were included in Hospital-1 for model training (n = 100) and internal validation (n = 50), and in Hospital-2 for external testing (n = 50). A total of 1,672 radiomic features were extracted from 3D segmented CT images. The Rad-score was established using random survival forest by accumulating and weighting the top-20 imaging features contributive to PFS. A nomogram for predicting PFS was established, which comprised the Rad-score and clinical factors highly relevant to PFS. Results In the training, internal validation, and external test groups, 69/100 (69%), 37/50 (74%) and 36/50 (72%) patients were progression-free at two years, respectively. According to the Rad-score, the integral of area under the curve (iAUC) for discriminating high and low risk of progression was 0.92 (95%CI: 0.77-1.0), 0.70 (0.41-0.98) and 0.90 (0.65-1.0), respectively. The C-index of Rad-score was 0.781 and 0.860 in the training and external test groups, higher than 0.707 and 0.606 for TNM stage, respectively. The nomogram integrating Rad-score and clinical factors (lung nodule type, cM stage and histological type) achieved a C-index of 0.845 and 0.837 to predict 2-year PFS, respectively, significantly higher than by only radiomic features (all p < 0.01). Conclusion The nomogram comprising CT-derived radiomic features and risk factors showed a high performance in predicting post-surgical 2-year PFS of patients with lung adenocarcinoma, which may help personalize the treatment decisions.
Collapse
Affiliation(s)
- Guixue Liu
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhihan Xu
- DI CT Collaboration, Siemens Healthineers Ltd., Shanghai, China
| | - Yaping Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Beibei Jiang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lu Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lingyun Wang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Geertruida H de Bock
- Department of Epidemiology, Hanzeplein 1, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, Hanzeplein 1, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Xueqian Xie
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| |
Collapse
|
29
|
Corso F, Tini G, Lo Presti G, Garau N, De Angelis SP, Bellerba F, Rinaldi L, Botta F, Rizzo S, Origgi D, Paganelli C, Cremonesi M, Rampinelli C, Bellomi M, Mazzarella L, Pelicci PG, Gandini S, Raimondi S. The Challenge of Choosing the Best Classification Method in Radiomic Analyses: Recommendations and Applications to Lung Cancer CT Images. Cancers (Basel) 2021; 13:cancers13123088. [PMID: 34205631 PMCID: PMC8234634 DOI: 10.3390/cancers13123088] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 06/11/2021] [Accepted: 06/15/2021] [Indexed: 12/22/2022] Open
Abstract
Radiomics uses high-dimensional sets of imaging features to predict biological characteristics of tumors and clinical outcomes. The choice of the algorithm used to analyze radiomic features and perform predictions has a high impact on the results, thus the identification of adequate machine learning methods for radiomic applications is crucial. In this study we aim to identify suitable approaches of analysis for radiomic-based binary predictions, according to sample size, outcome balancing and the features-outcome association strength. Simulated data were obtained reproducing the correlation structure among 168 radiomic features extracted from Computed Tomography images of 270 Non-Small-Cell Lung Cancer (NSCLC) patients and the associated to lymph node status. Performances of six classifiers combined with six feature selection (FS) methods were assessed on the simulated data using AUC (Area Under the Receiver Operating Characteristics Curves), sensitivity, and specificity. For all the FS methods and regardless of the association strength, the tree-based classifiers Random Forest and Extreme Gradient Boosting obtained good performances (AUC ≥ 0.73), showing the best trade-off between sensitivity and specificity. On small samples, performances were generally lower than in large-medium samples and with larger variations. FS methods generally did not improve performances. Thus, in radiomic studies, we suggest evaluating the choice of FS and classifiers, considering specific sample size, balancing, and association strength.
Collapse
Affiliation(s)
- Federica Corso
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, via Adamello 16, 20139 Milan, Italy; (F.C.); (G.T.); (L.M.); (P.G.P.)
- Department of Mathematics (DMAT), Politecnico di Milano, via Edoardo Bonardi 9, 20133 Milan, Italy
- Centre for Analysis, Decision and Society (CADS), Human Technopole, via Cristina Belgioioso 171, 20157 Milan, Italy
| | - Giulia Tini
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, via Adamello 16, 20139 Milan, Italy; (F.C.); (G.T.); (L.M.); (P.G.P.)
| | - Giuliana Lo Presti
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy; (G.L.P.); (F.B.); (D.O.)
| | - Noemi Garau
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, via Ponzio 34, 20133 Milan, Italy; (N.G.); (C.P.)
- Division of Radiology, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy; (C.R.); (M.B.)
| | - Simone Pietro De Angelis
- Molecular and Pharmaco-Epidemiology Unit, Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, via Adamello 16, 20139 Milan, Italy; (S.P.D.A.); (F.B.); (S.G.)
| | - Federica Bellerba
- Molecular and Pharmaco-Epidemiology Unit, Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, via Adamello 16, 20139 Milan, Italy; (S.P.D.A.); (F.B.); (S.G.)
| | - Lisa Rinaldi
- Radiation Research Unit, IEO European Institute of Oncology IRCCS, via Giuseppe Ripamonti 435, 20141 Milan, Italy; (L.R.); (M.C.)
- Department of Physics, University of Pavia, via Bassi 6, 27100 Pavia, Italy
| | - Francesca Botta
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy; (G.L.P.); (F.B.); (D.O.)
| | - Stefania Rizzo
- Clinica di Radiologia EOC, Istituto Imaging della Svizzera Italiana (IIMSI), via Tesserete 46, 6900 Lugano, Switzerland;
| | - Daniela Origgi
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy; (G.L.P.); (F.B.); (D.O.)
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, via Ponzio 34, 20133 Milan, Italy; (N.G.); (C.P.)
| | - Marta Cremonesi
- Radiation Research Unit, IEO European Institute of Oncology IRCCS, via Giuseppe Ripamonti 435, 20141 Milan, Italy; (L.R.); (M.C.)
| | - Cristiano Rampinelli
- Division of Radiology, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy; (C.R.); (M.B.)
| | - Massimo Bellomi
- Division of Radiology, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy; (C.R.); (M.B.)
| | - Luca Mazzarella
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, via Adamello 16, 20139 Milan, Italy; (F.C.); (G.T.); (L.M.); (P.G.P.)
- Division of Early Drug Development for Innovative Therapies, IEO European Institute of Experimental Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Pier Giuseppe Pelicci
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, via Adamello 16, 20139 Milan, Italy; (F.C.); (G.T.); (L.M.); (P.G.P.)
- Department of Oncology and Hematology-Oncology, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy
| | - Sara Gandini
- Molecular and Pharmaco-Epidemiology Unit, Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, via Adamello 16, 20139 Milan, Italy; (S.P.D.A.); (F.B.); (S.G.)
| | - Sara Raimondi
- Molecular and Pharmaco-Epidemiology Unit, Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, via Adamello 16, 20139 Milan, Italy; (S.P.D.A.); (F.B.); (S.G.)
- Correspondence:
| |
Collapse
|
30
|
Role of lung ultrasound for the etiological diagnosis of acute lower respiratory tract infection (ALRTI) in children: a prospective study. J Ultrasound 2021; 25:185-197. [PMID: 34146336 PMCID: PMC8213536 DOI: 10.1007/s40477-021-00600-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 05/29/2021] [Indexed: 12/19/2022] Open
Abstract
Objective and design Our prospective study assesses the role of detailed lung ultrasound (LUS) features to discriminate the etiological diagnosis of acute lower respiratory tract infection (ALRTI) in children. Methodology We analyzed patients aged from 1 month to 17 years admitted between March 2018 and April 2020 who were hospitalized for ALRTI. For all patients, history, clinical parameters, microbiological data, and lung ultrasound data were collected. Patients were stratified into three main groups (“bacterial”, “viral”, “atypical”) according to the presumed microbial etiology and LUS findings evaluated according to the etiological group. Nasopharyngeal swabs were obtained from all patients. A qualitative diagnostic test developed by Nurex S.r.l. was used for identification of bacterial and fungal DNA in respiratory samples. The Seegene Allplex™ Respiratory assays were used for the molecular diagnosis of viral respiratory pathogens. In addition, bacterial culture of blood and respiratory samples were performed, when indicated. Results A total of 186 children with suspected ALRTI (44% female) with an average age of 6 were enrolled in the study. We found that some ultrasound findings as size, number and distribution of consolidations, the position and motion of air bronchograms, pleural effusions and distribution of vertical artifacts significantly differ (p < 0.05) in children with bacterial, viral and atypical ALRTI. Conclusion Our study provides a detailed analysis of LUS features able to predict the ALRTI ethology in children. These findings may help the physicians to better manage a child with ALRTI and to offer personalized approach, from diagnosis to treatment and follow-up.
Collapse
|
31
|
Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy. Phys Med 2021; 83:221-241. [DOI: 10.1016/j.ejmp.2021.04.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 03/31/2021] [Accepted: 04/03/2021] [Indexed: 02/06/2023] Open
|
32
|
Artificial Intelligence and the Medical Physicist: Welcome to the Machine. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11041691] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.
Collapse
|
33
|
Rizzo S, Manganaro L, Dolciami M, Gasparri ML, Papadia A, Del Grande F. Computed Tomography Based Radiomics as a Predictor of Survival in Ovarian Cancer Patients: A Systematic Review. Cancers (Basel) 2021; 13:cancers13030573. [PMID: 33540655 PMCID: PMC7867247 DOI: 10.3390/cancers13030573] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 01/27/2021] [Accepted: 01/29/2021] [Indexed: 12/13/2022] Open
Abstract
Simple Summary Ovarian cancer represents the most lethal gynecological malignancy. Since many new drugs have been recently introduced as adjunctive treatments for this pathology, an early prediction of outcome might be helpful to further improve outcomes. Radiomics represents a recent advancement, relying on extraction of quantitative features from imaging examinations. Indeed, clinical images, such as computed tomography images, may contain quantitative information, reflecting the underlying pathophysiology of a tumoral tissue. Radiomic analyses can be performed in tumor regions and metastatic lesions, as well as in normal tissues. The radiomic process relies on quantitative features, usually extracted by dedicated software, and then culminates in analysis and model building, according to a defined clinical question. This systematic review aims to evaluate association between radiomics based on computed tomography images and survival (in terms of overall survival and progression free survival) in ovarian cancer patients. Abstract The objective of this systematic review was to assess the results of radiomics for prediction of overall survival (OS) and progression free survival (PFS) in ovarian cancer (OC) patients. A secondary objective was to evaluate the findings of papers that based their analyses on inter-site heterogeneity. This systematic review was conducted according to the PRISMA statement. After the initial retrieval of 145 articles, the final systematic review comprised six articles. Association between radiomic features and OS was evaluated in 3/6 studies (50%); all articles showed a significant association between radiomic features and OS. Association with PFS was evaluated in 5/6 (83%) articles; the period of follow-up ranged between six and 36 months. All the articles showed significant association between radiomic models and PFS. Inter-site textural features were used for analysis in 2/6 (33%) articles. They demonstrated that high levels of inter-site textural heterogeneity were significantly associated with incomplete surgical resection in breast cancer gene-negative patients, and that lower heterogeneity was associated with complete resectability. There were some differences among papers in methodology; for example, only 3/6 (50%) articles included validation cohorts. In conclusion, radiomic models have demonstrated promising results as predictors of survival in OC patients, although larger studies are needed to allow clinical applicability.
Collapse
Affiliation(s)
- Stefania Rizzo
- Istituto di Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900 Lugano, Switzerland;
- Facoltà di Scienze Biomediche, Università della Svizzera Italiana, Via Buffi 13, 6900 Lugano, Switzerland; (M.L.G.); (A.P.)
- Correspondence: ; Tel.: +41-91-811-6676
| | - Lucia Manganaro
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, 00185 Rome, Italy; (L.M.); (M.D.)
| | - Miriam Dolciami
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, 00185 Rome, Italy; (L.M.); (M.D.)
| | - Maria Luisa Gasparri
- Facoltà di Scienze Biomediche, Università della Svizzera Italiana, Via Buffi 13, 6900 Lugano, Switzerland; (M.L.G.); (A.P.)
- Department of Gynecology and Obstetrics, Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900 Lugano, Switzerland
| | - Andrea Papadia
- Facoltà di Scienze Biomediche, Università della Svizzera Italiana, Via Buffi 13, 6900 Lugano, Switzerland; (M.L.G.); (A.P.)
- Department of Gynecology and Obstetrics, Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900 Lugano, Switzerland
| | - Filippo Del Grande
- Istituto di Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900 Lugano, Switzerland;
- Facoltà di Scienze Biomediche, Università della Svizzera Italiana, Via Buffi 13, 6900 Lugano, Switzerland; (M.L.G.); (A.P.)
| |
Collapse
|