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Zheng X, Shen F, Chen W, Ren W, Tang S. Integrated pretreatment diffusion kurtosis imaging and serum squamous cell carcinoma antigen levels: a biomarker strategy for early assessment of radiotherapy outcomes in cervical cancer. Abdom Radiol (NY) 2024; 49:1502-1511. [PMID: 38536425 DOI: 10.1007/s00261-024-04270-3] [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: 12/03/2023] [Revised: 02/22/2024] [Accepted: 02/25/2024] [Indexed: 05/22/2024]
Abstract
OBJECTIVE This study aims to explore the utility of pretreatment DKI parameters and serum SCC-Ag in evaluating the early therapeutic response of cervical cancer to radiotherapy. MATERIALS AND METHODS A total of 33 patients diagnosed with cervical cancer, including 31 cases of cervical squamous cell carcinoma and two cases of adenosquamous carcinoma, participated in the study. All patients underwent conventional MRI and DKI scans on a 3T magnetic resonance scanner before radiotherapy and after ten sessions of radiotherapy. The therapeutic response was evaluated based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Patients were categorized into a response group (RG), comprising Complete Remission (CR) and Partial Remission (PR), and a non-response group (NRG), comprising Stable Disease (SD) and Progressive Disease (PD). LASSO was employed to select pretreatment DKI parameters, and ROC curves were generated for the selected parameters and serum SCC-Ag. RESULTS Significant differences were observed in pretreatment MD, Da, Dr, MK, Ka, Kr, and SCC-Ag between the RG and NRG groups (P < 0.01). However, no significant differences were noted for FA and FAK (P = 0.441&0.928). The two selected parameters (MD and MK) demonstrated area under the curve (AUC), sensitivity, and specificity of 0.810, 0.769, 0.850 and 0.827, 0.846, 0.750, respectively. The combination of MD and MK exhibited an improved AUC of 0.901, sensitivity of 0.692, and specificity of 1.000, with a higher Youden index compared to the individual parameters. Conversely, the AUC, sensitivity, and specificity of the combination of MD, MK, and SCC-Ag were 0.852, 0.615, and 1.000, with a Youden index of 0.615. CONCLUSION Pretreatment MD, MK, and SCC-Ag demonstrate potential clinical utility, with the combined application of MD and MK showing enhanced efficacy in assessing the early therapeutic response of cervical cancer to radiotherapy. The addition of SCC-Ag did not contribute further to the assessment efficacy.
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Affiliation(s)
- Xiang Zheng
- Department of Radiologic Diagnosis, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420 Fuma Road, Fuzhou, 350014, Fujian, China.
| | - Fangmin Shen
- Department of Radiologic Diagnosis, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420 Fuma Road, Fuzhou, 350014, Fujian, China
| | - Wenjuan Chen
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China
| | - Wang Ren
- Department of Radiologic Diagnosis, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420 Fuma Road, Fuzhou, 350014, Fujian, China
| | - Shaoliang Tang
- School of Medical Imaging, Fujian Medical University, Fuzhou, 350122, China
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Barham M, Kuroda M, Yoshimura Y, Hamada K, Khasawneh A, Sugimoto K, Konishi K, Tekiki N, Sugianto I, Bamgbose BO, Ishizaka H, Shimizu Y, Nakamitsu Y, Al-Hammad WE, Kamizaki R, Kurozumi A, Matsushita T, Ohno S, Asaumi J. Evaluation of calculation processes of apparent diffusion coefficient subtraction method (ASM) imaging. PLoS One 2023; 18:e0282462. [PMID: 36848353 PMCID: PMC9970062 DOI: 10.1371/journal.pone.0282462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 02/16/2023] [Indexed: 03/01/2023] Open
Abstract
A number of restricted diffusion (RD) imaging techniques, such as diffusion kurtosis (DK) imaging and Q space imaging, have been developed and proven to be useful for the diagnosis of diseases, including cerebral gliomas and cerebrovascular infarction. In particular, apparent diffusion coefficient (ADC) subtraction method (ASM) imaging has become available recently as a novel RD imaging technique. ASM is based on the difference between the ADC values in an image pair of two ADC maps, ADC basic (ADCb) and ADC modify (ADCm), which are created from diffusion-weighted images taken using short and long effective diffusion times, respectively. The present study aimed to assess the potential of different types of ASM imaging by comparing them with DK imaging which is the gold-standard RD imaging technique. In the present basic study using both polyethylene glycol phantom and cell-containing bio-phantom, three different types of ASM images were created using different calculation processes. ASM/A is an image calculated by dividing the absolute difference between ADCb and ADCm by ADCb several times. By contrast, ASM/S is an image created by dividing the absolute difference between ADCb and ADCm by the standard deviation of ADCb several times. As for positive ASM/A image (PASM/A), the positive image, which was resultant after subtracting ADCb from ADCm, was divided by ADCb several times. A comparison was made between the types of ASM and DK images. The results showed the same tendency between ASM/A in addition to both ASM/S and PASM/A. By increasing the number of divisions by ADCb from three to five times, ASM/A images transformed from DK-mimicking to more RD-sensitive images compared with DK images. These observations suggest that ASM/A images may prove useful for future clinical applications in RD imaging protocols for the diagnosis of diseases.
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Affiliation(s)
- Majd Barham
- Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Masahiro Kuroda
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama, Japan
- * E-mail:
| | - Yuuki Yoshimura
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama, Japan
- Radiology Diagnosis, Okayama Saiseikai General Hospital, Okayama, Japan
| | - Kentaro Hamada
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama, Japan
| | - Abdullah Khasawneh
- Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Kohei Sugimoto
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama, Japan
| | - Kohei Konishi
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama, Japan
| | - Nouha Tekiki
- Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Irfan Sugianto
- Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Babatunde O. Bamgbose
- Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Hinata Ishizaka
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama, Japan
| | - Yudai Shimizu
- Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Yuki Nakamitsu
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama, Japan
| | - Wlla E. Al-Hammad
- Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Ryo Kamizaki
- Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama, Japan
| | - Akira Kurozumi
- Central Division of Radiology, Okayama University Hospital, Okayama, Japan
| | - Toshi Matsushita
- Central Division of Radiology, Okayama University Hospital, Okayama, Japan
| | - Seiichiro Ohno
- Central Division of Radiology, Okayama University Hospital, Okayama, Japan
| | - Junichi Asaumi
- Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
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Iliadou V, Kakkos I, Karaiskos P, Kouloulias V, Platoni K, Zygogianni A, Matsopoulos GK. Early Prediction of Planning Adaptation Requirement Indication Due to Volumetric Alterations in Head and Neck Cancer Radiotherapy: A Machine Learning Approach. Cancers (Basel) 2022; 14:cancers14153573. [PMID: 35892831 PMCID: PMC9331795 DOI: 10.3390/cancers14153573] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/14/2022] [Accepted: 07/20/2022] [Indexed: 11/16/2022] Open
Abstract
Background: During RT cycles, the tumor response pattern could affect tumor coverage and may lead to organs at risk of overdose. As such, early prediction of significant volumetric changes could therefore reduce potential radiation-related adverse effects. Nevertheless, effective machine learning approaches based on the radiomic features of the clinically used CBCT images to determine the tumor volume variations due to RT not having been implemented so far. Methods: CBCT images from 40 HN cancer patients were collected weekly during RT treatment. From the obtained images, the Clinical Target Volume (CTV) and Parotid Glands (PG) regions of interest were utilized to calculate 104 delta-radiomics features. These features were fed on a feature selection and classification procedure for the early prediction of significant volumetric alterations. Results: The proposed framework was able to achieve 0.90 classification performance accuracy while detecting a small subset of discriminative characteristics from the 1st week of RT. The selected features were further analyzed regarding their effects on temporal changes in anatomy and tumor response modeling. Conclusion: The use of machine learning algorithms offers promising perspectives for fast and reliable early prediction of large volumetric deviations as a result of RT treatment, exploiting hidden patterns in the overall anatomical characteristics.
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Affiliation(s)
- Vasiliki Iliadou
- School of Electrical and Computer Engineering, National Technical University of Athens, 157 73 Athens, Greece; (I.K.); (G.K.M.)
- Correspondence: ; Tel.: +30-21-0772-3577
| | - Ioannis Kakkos
- School of Electrical and Computer Engineering, National Technical University of Athens, 157 73 Athens, Greece; (I.K.); (G.K.M.)
- Department of Biomedical Engineering, University of West Attica, 122 43 Athens, Greece
| | - Pantelis Karaiskos
- Medical Physics Laboratory, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece;
| | - Vassilis Kouloulias
- 2nd Department of Radiology, Radiotherapy Unit, ATTIKON University Hospital, 124 62 Athens, Greece; (V.K.); (K.P.)
| | - Kalliopi Platoni
- 2nd Department of Radiology, Radiotherapy Unit, ATTIKON University Hospital, 124 62 Athens, Greece; (V.K.); (K.P.)
| | - Anna Zygogianni
- 1st Department of Radiology, Radiotherapy Unit, ARETAIEION University Hospital, 115 28 Athens, Greece;
| | - George K. Matsopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, 157 73 Athens, Greece; (I.K.); (G.K.M.)
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