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Gitto S, Annovazzi A, Nulle K, Interlenghi M, Salvatore C, Anelli V, Baldi J, Messina C, Albano D, Di Luca F, Armiraglio E, Parafioriti A, Luzzati A, Biagini R, Castiglioni I, Sconfienza LM. X-rays radiomics-based machine learning classification of atypical cartilaginous tumour and high-grade chondrosarcoma of long bones. EBioMedicine 2024; 101:105018. [PMID: 38377797 PMCID: PMC10884340 DOI: 10.1016/j.ebiom.2024.105018] [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: 11/05/2023] [Revised: 02/03/2024] [Accepted: 02/04/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND Atypical cartilaginous tumour (ACT) and high-grade chondrosarcoma (CS) of long bones are respectively managed with active surveillance or curettage and wide resection. Our aim was to determine diagnostic performance of X-rays radiomics-based machine learning for classification of ACT and high-grade CS of long bones. METHODS This retrospective, IRB-approved study included 150 patients with surgically treated and histology-proven lesions at two tertiary bone sarcoma centres. At centre 1, the dataset was split into training (n = 71 ACT, n = 24 high-grade CS) and internal test (n = 19 ACT, n = 6 high-grade CS) cohorts, respectively, based on the date of surgery. At centre 2, the dataset constituted the external test cohort (n = 12 ACT, n = 18 high-grade CS). Manual segmentation was performed on frontal view X-rays, using MRI or CT for preliminary identification of lesion margins. After image pre-processing, radiomic features were extracted. Dimensionality reduction included stability, coefficient of variation, and mutual information analyses. In the training cohort, after class balancing, a machine learning classifier (Support Vector Machine) was automatically tuned using nested 10-fold cross-validation. Then, it was tested on both the test cohorts and compared to two musculoskeletal radiologists' performance using McNemar's test. FINDINGS Five radiomic features (3 morphology, 2 texture) passed dimensionality reduction. After tuning on the training cohort (AUC = 0.75), the classifier had 80%, 83%, 79% and 80%, 89%, 67% accuracy, sensitivity, and specificity in the internal (temporally independent) and external (geographically independent) test cohorts, respectively, with no difference compared to the radiologists (p ≥ 0.617). INTERPRETATION X-rays radiomics-based machine learning accurately differentiates between ACT and high-grade CS of long bones. FUNDING AIRC Investigator Grant.
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Affiliation(s)
- Salvatore Gitto
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy; Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy
| | - Alessio Annovazzi
- Nuclear Medicine Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Kitija Nulle
- Radiology Department, Riga East Clinical University Hospital, Riga, Latvia
| | | | - Christian Salvatore
- DeepTrace Technologies s.r.l., Milan, Italy; Department of Science, Technology and Society, University School for Advanced Studies IUSS Pavia, Pavia, Italy
| | - Vincenzo Anelli
- Radiology and Diagnostic Imaging Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Jacopo Baldi
- Oncological Orthopaedics Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Carmelo Messina
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy; Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy; Dipartimento di Scienze Biomediche, Chirurgiche ed Odontoiatriche, Università degli Studi di Milano, Milan, Italy
| | - Filippo Di Luca
- Scuola di Specializzazione in Radiodiagnostica, Università degli Studi di Milano, Milan, Italy
| | | | | | | | - Roberto Biagini
- Oncological Orthopaedics Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Isabella Castiglioni
- Department of Physics "G. Occhialini", Università degli Studi di Milano-Bicocca, Milan, Italy
| | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy; Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy.
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Tsukamoto S, Mavrogenis AF, Nitta Y, Righi A, Masunaga T, Honoki K, Fujii H, Kido A, Tanaka Y, Tanaka Y, Errani C. A Systematic Review of Adjuvant Chemotherapy in Localized Dedifferentiated Chondrosarcoma. Curr Oncol 2024; 31:566-578. [PMID: 38275833 PMCID: PMC10813944 DOI: 10.3390/curroncol31010040] [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: 12/01/2023] [Revised: 01/12/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024] Open
Abstract
Dedifferentiated chondrosarcoma (DDCS) is a high-grade subtype of chondrosarcoma with the bimorphic histological appearance of a conventional chondrosarcoma component with abrupt transition to a high-grade, non-cartilaginous sarcoma. DDCS can be radiographically divided into central and peripheral types. Wide resection is currently the main therapeutic option for localized DDCS. Moreover, the effectiveness of adjuvant chemotherapy remains controversial. Therefore, we performed a systematic review of available evidence to evaluate the effect of adjuvant chemotherapy on localized DDCS. The purpose was to compare the 5-year survival rate among patients treated with surgery plus adjuvant chemotherapy or surgery alone for localized DDCS. The search was conducted in PubMed, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) databases. Of the 217 studies shortlisted, 11 retrospective non-randomized studies (comprising 556 patients with localized DDCS) were selected. The 5-year survival rates were similar between the two treatment groups (28.2% (51/181) vs. 24.0% (90/375), respectively). The overall pooled odds ratio was 1.25 (95% confidence interval: 0.80-1.94; p = 0.324), and heterogeneity I2 was 2%. However, when limited to peripheral DDCS, adjuvant chemotherapy was associated with prolonged survival (p = 0.03). Due to the paucity of included studies and the absence of prospective comparative studies, no conclusions can be drawn regarding the effectiveness or ineffectiveness of adjuvant chemotherapy for localized DDCS.
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Affiliation(s)
- Shinji Tsukamoto
- Department of Orthopaedic Surgery, Nara Medical University, 840, Shijo-cho, Kashihara 634-8521, Japan; (T.M.); (K.H.); (H.F.); (Y.T.)
| | - Andreas F. Mavrogenis
- First Department of Orthopaedics, School of Medicine, National and Kapodistrian University of Athens, 41 Ventouri Street, Holargos, 15562 Athens, Greece;
| | - Yuji Nitta
- Department of Diagnostic Pathology, Nara Medical University, 840, Shijo-cho, Kashihara 634-8521, Japan;
| | - Alberto Righi
- Department of Pathology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Rizzoli Orthopaedic Institute, Via di Barbiano 1/10, 40136 Bologna, Italy;
| | - Tomoya Masunaga
- Department of Orthopaedic Surgery, Nara Medical University, 840, Shijo-cho, Kashihara 634-8521, Japan; (T.M.); (K.H.); (H.F.); (Y.T.)
| | - Kanya Honoki
- Department of Orthopaedic Surgery, Nara Medical University, 840, Shijo-cho, Kashihara 634-8521, Japan; (T.M.); (K.H.); (H.F.); (Y.T.)
| | - Hiromasa Fujii
- Department of Orthopaedic Surgery, Nara Medical University, 840, Shijo-cho, Kashihara 634-8521, Japan; (T.M.); (K.H.); (H.F.); (Y.T.)
| | - Akira Kido
- Department of Rehabilitation Medicine, Nara Medical University, 840, Shijo-cho, Kashihara 634-8521, Japan;
| | - Yuu Tanaka
- Department of Rehabilitation Medicine, Wakayama Professional University of Rehabilitation, 3-1, Minamoto-cho, Wakayama 640-8222, Japan;
| | - Yasuhito Tanaka
- Department of Orthopaedic Surgery, Nara Medical University, 840, Shijo-cho, Kashihara 634-8521, Japan; (T.M.); (K.H.); (H.F.); (Y.T.)
| | - Costantino Errani
- Department of Orthopaedic Oncology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Rizzoli Orthopaedic Institute, Via Pupilli 1, 40136 Bologna, Italy;
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