1
|
Tagliabue M, Ruju F, Mossinelli C, Gaeta A, Raimondi S, Volpe S, Zaffaroni M, Isaksson LJ, Garibaldi C, Cremonesi M, Rapino A, Chiocca S, Pietrobon G, Alterio D, Trisolini G, Morbini P, Rampinelli V, Grammatica A, Petralia G, Jereczek-Fossa BA, Preda L, Ravanelli M, Maroldi R, Piazza C, Benazzo M, Ansarin M. The prognostic role of MRI-based radiomics in tongue carcinoma: a multicentric validation study. LA RADIOLOGIA MEDICA 2024; 129:1369-1381. [PMID: 39096355 PMCID: PMC11379741 DOI: 10.1007/s11547-024-01859-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 07/17/2024] [Indexed: 08/05/2024]
Abstract
PURPOSE Radiomics is an emerging field that utilizes quantitative features extracted from medical images to predict clinically meaningful outcomes. Validating findings is crucial to assess radiomics applicability. We aimed to validate previously published magnetic resonance imaging (MRI) radiomics models to predict oncological outcomes in oral tongue squamous cell carcinoma (OTSCC). MATERIALS AND METHODS Retrospective multicentric study on OTSCC surgically treated from 2010 to 2019. All patients performed preoperative MRI, including contrast-enhanced T1-weighted (CE-T1), diffusion-weighted sequences and apparent diffusion coefficient map. We evaluated overall survival (OS), locoregional recurrence-free survival (LRRFS), cause-specific mortality (CSM). We elaborated different models based on clinical and radiomic data. C-indexes assessed the prediction accuracy of the models. RESULTS We collected 112 consecutive independent patients from three Italian Institutions to validate the previously published MRI radiomic models based on 79 different patients. The C-indexes for the hybrid clinical-radiomic models in the validation cohort were lower than those in the training cohort but remained > 0.5 in most cases. CE-T1 sequence provided the best fit to the models: the C-indexes obtained were 0.61, 0.59, 0.64 (pretreatment model) and 0.65, 0.69, 0.70 (posttreatment model) for OS, LRRFS and CSM, respectively. CONCLUSION Our clinical-radiomic models retain a potential to predict OS, LRRFS and CSM in heterogeneous cohorts across different centers. These findings encourage further research, aimed at overcoming current limitations, due to the variability of imaging acquisition, processing and tumor volume delineation.
Collapse
Affiliation(s)
- Marta Tagliabue
- Division of Otolaryngology and Head and Neck Surgery, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Francesca Ruju
- Division of Radiology, European Institute of Oncology IRCCS, Milan, Italy
| | - Chiara Mossinelli
- Division of Otolaryngology and Head and Neck Surgery, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy.
| | - Aurora Gaeta
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Via Bicocca Degli Arcimboldi, Milan, Italy
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy
| | - Sara Raimondi
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy
| | - Stefania Volpe
- Division of Radiation Oncology, European Institute of Oncology, IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Mattia Zaffaroni
- Division of Radiation Oncology, European Institute of Oncology, IRCCS, Milan, Italy
| | - Lars Johannes Isaksson
- Division of Radiation Oncology, European Institute of Oncology, IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Cristina Garibaldi
- Unit of Radiation Research, IEO European Institute of Oncology, IRCCS, Milan, Italy
| | - Marta Cremonesi
- Unit of Radiation Research, IEO European Institute of Oncology, IRCCS, Milan, Italy
| | - Anna Rapino
- Postgraduate School of Radiodiagnostic, University of Milan, Milan, Italy
| | - Susanna Chiocca
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy
| | - Giacomo Pietrobon
- Division of Otolaryngology and Head and Neck Surgery, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Daniela Alterio
- Division of Radiation Oncology, European Institute of Oncology, IRCCS, Milan, Italy
| | - Giuseppe Trisolini
- Department of Otorhinolaryngology and Skull Base Microsurgery-Neurosciences, ASST Ospedale Papa Giovanni XXIII, Bergamo, Italy
| | | | - Vittorio Rampinelli
- Unit of Otorhinolaryngology-Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, ASST Spedali Civili of Brescia, University of Brescia, 25123, Brescia, Italy
| | - Alberto Grammatica
- Unit of Otorhinolaryngology-Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, ASST Spedali Civili of Brescia, University of Brescia, 25123, Brescia, Italy
| | - Giuseppe Petralia
- Division of Radiology, European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiation Oncology, European Institute of Oncology, IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Lorenzo Preda
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, Italy
- Radiology Institute, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Marco Ravanelli
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, School of Medicine, Brescia, Italy
| | - Roberto Maroldi
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, School of Medicine, Brescia, Italy
| | - Cesare Piazza
- Unit of Otorhinolaryngology-Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, ASST Spedali Civili of Brescia, University of Brescia, 25123, Brescia, Italy
| | - Marco Benazzo
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, Italy
- Department of Otorhinolaryngology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Mohssen Ansarin
- Division of Otolaryngology and Head and Neck Surgery, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| |
Collapse
|
2
|
Yao Y, Jin X, Peng T, Song P, Ye Y, Song L, Li H, An P. A novel nomogram for predicting overall survival in patients with tongue squamous cell carcinoma using clinical features and MRI radiomics data: a pilot study. World J Surg Oncol 2024; 22:227. [PMID: 39198807 PMCID: PMC11360835 DOI: 10.1186/s12957-024-03508-0] [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: 04/02/2024] [Accepted: 08/25/2024] [Indexed: 09/01/2024] Open
Abstract
OBJECTIVE Tongue squamous cell carcinoma (TSCC) accounts for 43.4% of oral cancers in China and has a poor prognosis. This study aimed to explore whether radiomics features extracted from preoperative magnetic resonance imaging (MRI) could predict overall survival (OS) in patients with TSCC. METHODS The clinical imaging data of 232 patients with pathologically confirmed TSCC at Xiangyang No. 1 People's Hospital were retrospectively analyzed from February 2010 to October 2022. Based on 2-10 years of follow-up, patients were categorized into two groups: control (healthy survival, n = 148) and research (adverse events: recurrence or metastasis-related death, n = 84). A training and a test set were established using a 7:3 ratio and a time node. Radiomics features were extracted from axial T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging (DWI) sequences. The corresponding radiomics scores were generated using the least absolute shrinkage and selection operator algorithm. Kaplan-Meier and multivariate Cox regression analyses were used to screen for independent factors affecting adverse events in patients with TSCC using clinical and pathological results. A novel nomogram was established to predict the probability of adverse events and OS in patients with TSCC. RESULTS The incidence of adverse events within 2-10 years after surgery was 36.21%. Kaplan-Meier analysis revealed that hot pot consumption, betel nut chewing, platelet-lymphocyte ratio, drug use, neutrophil-lymphocyte ratio, Radscore, and other factors impacted TSCC survival. Multivariate Cox regression analysis revealed that the clinical stage (P < 0.001), hot pot consumption (P < 0.001), Radscore 1 (P = 0.01), and Radscore 2 (P < 0.001) were independent factors affecting TSCC-OS. The same result was validated by the XGBoost algorithm. The nomogram based on the aforementioned factors exhibited good discrimination (C-index 0.86/0.81) and calibration (P > 0.05) in the training and test sets, accurately predicting the risk of adverse events and survival. CONCLUSION The nomogram constructed using clinical data and MRI radiomics parameters may accurately predict TSCC-OS noninvasively, thereby assisting clinicians in promptly modifying treatment strategies to improve patient prognosis.
Collapse
Affiliation(s)
- Yongling Yao
- Department of Pain Management and Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China
| | - Xin Jin
- Department of Pain Management and Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China
| | - Tianfang Peng
- Department of Pain Management and Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China
- Department of Emergency, Oncology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, Hubei Province, China
| | - Ping Song
- Department of Pain Management and Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China
| | - Yingjian Ye
- Department of Pain Management and Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China
| | - Lina Song
- Department of Pain Management and Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China
- Department of Emergency, Oncology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, Hubei Province, China
| | - Huantian Li
- Department of Pain Management and Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China.
- Department of Emergency, Oncology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, Hubei Province, China.
| | - Peng An
- Department of Pain Management and Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China.
- Department of Emergency, Oncology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, Hubei Province, China.
- Department of epidemiology, Xiangyang Key Laboratory of Maternal-fetal Medicine on Fetal Congenital Heart Disease, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Hubei, P.R.C, Xiangyang, 441000, Hubei Province, China.
| |
Collapse
|
3
|
Corti A, Cavalieri S, Calareso G, Mattavelli D, Ravanelli M, Poli T, Licitra L, Corino VDA, Mainardi L. MRI radiomics in head and neck cancer from reproducibility to combined approaches. Sci Rep 2024; 14:9451. [PMID: 38658630 PMCID: PMC11043398 DOI: 10.1038/s41598-024-60009-6] [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: 12/30/2023] [Accepted: 04/17/2024] [Indexed: 04/26/2024] Open
Abstract
The clinical applicability of radiomics in oncology depends on its transferability to real-world settings. However, the absence of standardized radiomics pipelines combined with methodological variability and insufficient reporting may hamper the reproducibility of radiomic analyses, impeding its translation to clinics. This study aimed to identify and replicate published, reproducible radiomic signatures based on magnetic resonance imaging (MRI), for prognosis of overall survival in head and neck squamous cell carcinoma (HNSCC) patients. Seven signatures were identified and reproduced on 58 HNSCC patients from the DB2Decide Project. The analysis focused on: assessing the signatures' reproducibility and replicating them by addressing the insufficient reporting; evaluating their relationship and performances; and proposing a cluster-based approach to combine radiomic signatures, enhancing the prognostic performance. The analysis revealed key insights: (1) despite the signatures were based on different features, high correlations among signatures and features suggested consistency in the description of lesion properties; (2) although the uncertainties in reproducing the signatures, they exhibited a moderate prognostic capability on an external dataset; (3) clustering approaches improved prognostic performance compared to individual signatures. Thus, transparent methodology not only facilitates replication on external datasets but also advances the field, refining prognostic models for potential personalized medicine applications.
Collapse
Affiliation(s)
- Anna Corti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133, Milan, Italy.
| | - Stefano Cavalieri
- Head and Neck Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli studi di Milano, Milan, Italy
| | - Giuseppina Calareso
- Radiology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | - Davide Mattavelli
- Unit of Otorhinolaryngology-Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, ASST Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Marco Ravanelli
- Unit of Radiology, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, ASST Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Tito Poli
- Maxillo-Facial Surgery Division, Head and Neck Department, University Hospital of Parma, Parma, Italy
| | - Lisa Licitra
- Head and Neck Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli studi di Milano, Milan, Italy
| | - Valentina D A Corino
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133, Milan, Italy
- Cardiotech Lab, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133, Milan, Italy
| |
Collapse
|
4
|
Ling X, Alexander GS, Molitoris J, Choi J, Schumaker L, Mehra R, Gaykalova DA, Ren L. Identification of CT-based non-invasive radiomic biomarkers for overall survival prediction in oral cavity squamous cell carcinoma. Sci Rep 2023; 13:21774. [PMID: 38066047 PMCID: PMC10709435 DOI: 10.1038/s41598-023-48048-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 11/21/2023] [Indexed: 12/18/2023] Open
Abstract
This study addresses the limited non-invasive tools for Oral Cavity Squamous Cell Carcinoma (OSCC) survival prediction by identifying Computed Tomography (CT)-based biomarkers to improve prognosis prediction. A retrospective analysis was conducted on data from 149 OSCC patients, including CT radiomics and clinical information. An ensemble approach involving correlation analysis, score screening, and the Sparse-L1 algorithm was used to select functional features, which were then used to build Cox Proportional Hazards models (CPH). Our CPH achieved a 0.70 concordance index in testing. The model identified two CT-based radiomics features, Gradient-Neighboring-Gray-Tone-Difference-Matrix-Strength (GNS) and normalized-Wavelet-LLL-Gray-Level-Dependence-Matrix-Large-Dependence-High-Gray-Level-Emphasis (HLE), as well as stage and alcohol usage, as survival biomarkers. The GNS group with values above 14 showed a hazard ratio of 0.12 and a 3-year survival rate of about 90%. Conversely, the GNS group with values less than or equal to 14 had a 49% survival rate. For normalized HLE, the high-end group (HLE > - 0.415) had a hazard ratio of 2.41, resulting in a 3-year survival rate of 70%, while the low-end group (HLE ≤ - 0.415) had a 36% survival rate. These findings contribute to our knowledge of how radiomics can be used to predict the outcome so that treatment plans can be tailored for patients people with OSCC to improve their survival.
Collapse
Affiliation(s)
- Xiao Ling
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Gregory S Alexander
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Jason Molitoris
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jinhyuk Choi
- Department of Breast Surgery, Kosin University Gospel Hospital, Busan, Republic of Korea
| | - Lisa Schumaker
- Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ranee Mehra
- Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Daria A Gaykalova
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
- Department of Otorhinolaryngology-Head and Neck Surgery, Marlene & Stewart Greenebaum Comprehensive Cancer Center, University of Maryland Medical Center, Baltimore, MD, USA.
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA.
| | - Lei Ren
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA.
| |
Collapse
|
5
|
Konishi M, Kakimoto N. Radiomics analysis of intraoral ultrasound images for prediction of late cervical lymph node metastasis in patients with tongue cancer. Head Neck 2023; 45:2619-2626. [PMID: 37584449 DOI: 10.1002/hed.27487] [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: 04/15/2023] [Revised: 07/25/2023] [Accepted: 08/07/2023] [Indexed: 08/17/2023] Open
Abstract
BACKGROUND We investigated the predictability of late cervical lymph node metastasis using radiomics analysis of ultrasonographic images of tongue cancer. METHODS We selected 120 patients with tongue cancer who underwent intraoral ultrasonography, 30 of which had late cervical lymph node metastasis. Radiomics analysis was used to extract and quantify the image features. Bootstrap forest (BF), support vector machine (SVM), and neural tanh boost (NTB) were used as the machine learning models, and receiver operating characteristic curve analysis was conducted to determine diagnostic performance. RESULTS The sensitivity, specificity, accuracy, and AUC in the validation group were, respectively, 0.600, 0.967, 0.875, and 0.923 for the BF model; 0.700, 0.967, 0.900, and 0.950 for the SVM model; and 0.900, 0.967, 0.950, and 0.967 for NTB model. CONCLUSIONS Radiomics analysis and machine learning models using ultrasonographic images of pretreated tongue cancer could predict late cervical lymph node metastasis with high accuracy.
Collapse
Affiliation(s)
- Masaru Konishi
- Department of Oral and Maxillofacial Radiology, Hiroshima University Hospital, Hiroshima, Japan
| | - Naoya Kakimoto
- Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| |
Collapse
|
6
|
Vidiri A, Marzi S, Piludu F, Lucchese S, Dolcetti V, Polito E, Mazzola F, Marchesi P, Merenda E, Sperduti I, Pellini R, Covello R. Magnetic resonance imaging-based prediction models for tumor stage and cervical lymph node metastasis of tongue squamous cell carcinoma. Comput Struct Biotechnol J 2023; 21:4277-4287. [PMID: 37701020 PMCID: PMC10493896 DOI: 10.1016/j.csbj.2023.08.020] [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: 06/13/2023] [Revised: 08/10/2023] [Accepted: 08/22/2023] [Indexed: 09/14/2023] Open
Abstract
Purpose To evaluate the ability of preoperative MRI-based measurements to predict the pathological T (pT) stage and cervical lymph node metastasis (CLNM) via machine learning (ML)-driven models trained in oral tongue squamous cell carcinoma (OTSCC). Materials and methods 108 patients with a new diagnosis of OTSCC were enrolled. The preoperative MRI study included post-contrast high-resolution T1-weighted images acquired in all patients. MRI-based depth of invasion (DOI) and tumor dimension-together with shape-based and intensity-based features-were extracted from the lesion volume segmentation. The entire dataset was randomly divided into a training set and a validation set, and the performances of different types of ML algorithms were evaluated and compared. Results MRI-based DOI and tumor dimension together with several shape-based and intensity-based signatures significantly discriminated the pT stage and LN status. The overall accuracy of the model for predicting the pT stage was 0.86 (95%CI, 0.78-0.92) and 0.81 (0.64-0.91) in the training and validation sets, respectively. There was no improvement in the model performance upon including shape-based and intensity-based features. The model for predicting CLNM based on DOI and tumor dimensions had a fair accuracy of 0.68 (0.57-0.78) and 0.69 (0.51-0.84) in the training and validation sets, respectively. The shape-based and intensity-based signatures have shown potential for improving the model sensitivity, with a comparable accuracy. Conclusion MRI-based models driven by ML algorithms could stratify patients with OTSCC according to the pT stages. They had a moderate ability to predict cervical lymph node metastasis.
Collapse
Affiliation(s)
- Antonello Vidiri
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome,Italy
| | - Simona Marzi
- Medical Physics Laboratory, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 0 0144 Rome, Italy
| | - Francesca Piludu
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome,Italy
| | - Sonia Lucchese
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome,Italy
- Scuola di Specializzazione in Radiodiagnostica, Sapienza University of Rome, Policlinico Umberto I, Viale Regina Elena 324, 00161 Rome, Italy
| | - Vincenzo Dolcetti
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome,Italy
- Scuola di Specializzazione in Radiodiagnostica, Sapienza University of Rome, Policlinico Umberto I, Viale Regina Elena 324, 00161 Rome, Italy
| | - Eleonora Polito
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome,Italy
| | - Francesco Mazzola
- Department of Otolaryngology and Head and Neck Surgery, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy
| | - Paolo Marchesi
- Department of Otolaryngology and Head and Neck Surgery, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy
| | - Elisabetta Merenda
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Policlinico Umberto I, 00161 Rome, Italy
| | - Isabella Sperduti
- Biostatistics Unit, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy
| | - Raul Pellini
- Department of Otolaryngology and Head and Neck Surgery, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy
| | - Renato Covello
- Department of Pathology, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy
| |
Collapse
|
7
|
Jiang S, Locatello LG, Maggiore G, Gallo O. Radiomics-Based Analysis in the Prediction of Occult Lymph Node Metastases in Patients with Oral Cancer: A Systematic Review. J Clin Med 2023; 12:4958. [PMID: 37568363 PMCID: PMC10419487 DOI: 10.3390/jcm12154958] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/23/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND Tumor extension and metastatic cervical lymph nodes' (LNs) number and dimensions are major prognostic factors in patients with oral squamous cell carcinoma (OSCC). Radiomics-based models are being integrated into clinical practice in the prediction of LN status prior to surgery in order to optimize the treatment, yet their value is still debated. METHODS A systematic review of the literature was conducted according to the PRISMA guideline. Baseline study characteristics, and methodological items were extracted and summarized. RESULTS A total of 10 retrospective studies were included into the present study, each of them exploiting a single imaging modality. Data from a cohort of 1489 patients were analyzed: the highest AUC value was 99.5%, ACC ranges from 68% to 97.5%, and sensibility and specificity were over 0.65 and 0.70, respectively. CONCLUSION Radiomics may be a noninvasive tool to predict occult LN metastases (LNM) in OSCC patients prior to treatment; further prospective studies are warranted to create a reproducible and reliable method for the detection of LNM in OSCC.
Collapse
Affiliation(s)
- Serena Jiang
- Department of Otorhinolaryngology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Luca Giovanni Locatello
- Department of Otorhinolaryngology, University Hospital “Santa Maria Della Misericordia”, Azienda Sanitaria Universitaria Friuli Centrale (ASUFC), 33100 Udine, Italy;
| | - Giandomenico Maggiore
- Department of Otorhinolaryngology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Oreste Gallo
- Department of Otorhinolaryngology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| |
Collapse
|
8
|
Corti A, De Cecco L, Cavalieri S, Lenoci D, Pistore F, Calareso G, Mattavelli D, de Graaf P, Leemans CR, Brakenhoff RH, Ravanelli M, Poli T, Licitra L, Corino V, Mainardi L. MRI-based radiomic prognostic signature for locally advanced oral cavity squamous cell carcinoma: development, testing and comparison with genomic prognostic signatures. Biomark Res 2023; 11:69. [PMID: 37455307 DOI: 10.1186/s40364-023-00494-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 05/03/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND . At present, the prognostic prediction in advanced oral cavity squamous cell carcinoma (OCSCC) is based on the tumor-node-metastasis (TNM) staging system, and the most used imaging modality in these patients is magnetic resonance image (MRI). With the aim to improve the prediction, we developed an MRI-based radiomic signature as a prognostic marker for overall survival (OS) in OCSCC patients and compared it with published gene expression signatures for prognosis of OS in head and neck cancer patients, replicated herein on our OCSCC dataset. METHODS For each patient, 1072 radiomic features were extracted from T1 and T2-weighted MRI (T1w and T2w). Features selection was performed, and an optimal set of five of them was used to fit a Cox proportional hazard regression model for OS. The radiomic signature was developed on a multi-centric locally advanced OCSCC retrospective dataset (n = 123) and validated on a prospective cohort (n = 108). RESULTS The performance of the signature was evaluated in terms of C-index (0.68 (IQR 0.66-0.70)), hazard ratio (HR 2.64 (95% CI 1.62-4.31)), and high/low risk group stratification (log-rank p < 0.001, Kaplan-Meier curves). When tested on a multi-centric prospective cohort (n = 108), the signature had a C-index of 0.62 (IQR 0.58-0.64) and outperformed the clinical and pathologic TNM stage and six out of seven gene expression prognostic signatures. In addition, the significant difference of the radiomic signature between stages III and IVa/b in patients receiving surgery suggests a potential association of MRI features with the pathologic stage. CONCLUSIONS Overall, the present study suggests that MRI signatures, containing non-invasive and cost-effective remarkable information, could be exploited as prognostic tools.
Collapse
Affiliation(s)
- Anna Corti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
| | - Loris De Cecco
- Integrated Biology of Rare Tumors, Department of Research, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | - Stefano Cavalieri
- Head and Neck Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli studi di Milano, Milan, Italy
| | - Deborah Lenoci
- Integrated Biology of Rare Tumors, Department of Research, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | - Federico Pistore
- Head and Neck Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | - Giuseppina Calareso
- Radiology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | - Davide Mattavelli
- Unit of Otorhinolaryngology-Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, ASST Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Pim de Graaf
- Amsterdam UMC location Vrije Universiteit, Radiology and Nuclear Medicine, de Boelelaan 1117, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - C René Leemans
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Amsterdam UMC location Vrije Universiteit, Otolaryngology-Head and Neck Surgery, de Boelelaan 1117, Amsterdam, The Netherlands
| | - Ruud H Brakenhoff
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Amsterdam UMC location Vrije Universiteit, Otolaryngology-Head and Neck Surgery, de Boelelaan 1117, Amsterdam, The Netherlands
| | - Marco Ravanelli
- Unit of Radiology, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, ASST Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Tito Poli
- Maxillo-Facial Surgery Division, Head and Neck Department, University Hospital of Parma, Parma, Italy
| | - Lisa Licitra
- Head and Neck Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli studi di Milano, Milan, Italy
| | - Valentina Corino
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Cardiotech Lab, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| |
Collapse
|
9
|
Liu J, Song L, Zhou J, Yu M, Hu Y, Zhang J, Song P, Ye Y, Wang J, Feng G, Guo H, An P. Prediction of Prognosis of Tongue Squamous Cell Carcinoma Based on Clinical MR Imaging Data Modeling. Technol Cancer Res Treat 2023; 22:15330338231207006. [PMID: 37872687 PMCID: PMC10594972 DOI: 10.1177/15330338231207006] [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: 07/03/2023] [Revised: 09/04/2023] [Accepted: 09/19/2023] [Indexed: 10/25/2023] Open
Abstract
Objective: Tongue squamous cell carcinoma (TSCC) is one of the most common and poor prognosis head and neck tumors. The purpose of this study is to establish a model for predicting TSCC prognosis based on clinical and MR radiomics data and to develop a nomogram. Methods: A retrospective analysis was performed on the clinical and imaging data of 211 patients with pathologically confirmed TSCC who underwent radical surgery at xx hospital from February 2011 to January 2020. Patients were divided into a study group (recurrence, metastasis, and death, n = 76) and a control group (normal survival, n = 135) according to 1 to 6 years of follow-up. A training set and a test set were established based on a ratio of 7:3 and a time point. In the training set, 3 prediction models (clinical data model, imaging model, and combined model) were established based on the MR radiomics score (Radscore) combined with clinical features. The predictive performance of these models was compared using the Delong curve, and the clinical net benefit of the model was tested using the decision curve. Then, the external validation of the model was performed in the test set, and a nomogram for predicting TSCC prognosis was developed. Results: Univariate analysis confirmed that betel nut consumption, spicy hot pot or pickled food, unclean oral sex, drug use, platelet/lymphocyte ratio (PLR), neutrophil/lymphocyte ratio (NLR), depth of invasion (DOI), low differentiation, clinical stage, and Radscore were factors that affected TSCC prognosis (P < .05). In the test set, the combined model based on these factors had the highest predictive performance for TSCC prognosis (area under curve (AUC) AUC: 0.870, 95% CI [0.761-0.942]), which was significantly higher than the clinical model (AUC: 0.730, 95% CI [0.602-0.835], P = .033) and imaging model (AUC: 0.765, 95% CI [0.640-0.863], P = .074). The decision curve also confirmed the higher clinical net benefit of the combined model, and these results were validated in the test set. The nomogram developed based on the combined model received good evaluation in clinical application. Conclusion: MR-LASSO extracted texture parameters can help improve the performance of TSCC prognosis models. The combined model and nomogram provide support for postoperative clinical treatment management of TSCC.
Collapse
Affiliation(s)
- Junjie Liu
- Department of Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Depatment of Radiology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, China
- Department of Oncology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Lina Song
- Department of Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Depatment of Radiology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, China
- Department of Oncology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Jingran Zhou
- Depatment of Radiology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, China
- Department of Pharmacy and Laboratory, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Mengxing Yu
- Depatment of Radiology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, China
- Department of Pharmacy and Laboratory, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Yan Hu
- Depatment of Radiology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, China
- Department of Pharmacy and Laboratory, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Junyan Zhang
- Department of Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Pharmacy and Laboratory, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Ping Song
- Department of Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Oncology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Yingjian Ye
- Department of Pharmacy and Laboratory, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Gynaecology and Obstetrics, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Jinsong Wang
- Depatment of Radiology and Pathology, Hubei Province Clinical Research Center of Parkinson's Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, China
- Department of Gynaecology and Obstetrics, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Guoyan Feng
- Department of Pharmacy and Laboratory, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Hubei Provincial Clinical Research Center for Accurate Fetus Malformation Diagnosis, Hubei University of Medicine, Xiangyang, Hubei Province, China
| | - Hongyan Guo
- Department of Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Hubei Provincial Clinical Research Center for Accurate Fetus Malformation Diagnosis, Hubei University of Medicine, Xiangyang, Hubei Province, China
| | - Peng An
- Department of Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Gynaecology and Obstetrics, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| |
Collapse
|