Publication
Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI
| Summary: | BACKGROUND: Whole-body diffusion weighted imaging (WB-DWI) has proven value to detect multiple myeloma (MM) lesions. However, the large volume of imaging data and the presence of numerous lesions makes the reading process challenging. The aim of the current study was to develop a semi-automatic lesion segmentation algorithm for WB-DWI images in MM patients and to evaluate this smart-algorithm (SA) performance by comparing it to the manual segmentations performed by radiologists. METHODS: An atlas-based segmentation was developed to remove the high-signal intensity normal tissues on WB-DWI and to restrict the lesion area to the skeleton. Then, an outlier threshold-based segmentation was applied to WB-DWI images, and the segmented area's signal intensity was compared to the average signal intensity of a low-fat muscle on T1-weighted images. This method was validated in 22 whole-body DWI images of patients diagnosed with MM. Dice similarity coefficient (DSC), sensitivity and positive predictive value (PPV) were computed to evaluate the SA performance against the gold standard (GS) and to compare with the radiologists. A non-parametric Wilcoxon test was also performed. Apparent diffusion coefficient (ADC) histogram metrics and lesion volume were extracted for the GS segmentation and for the correctly identified lesions by SA and their correlation was assessed. RESULTS: The mean inter-radiologists DSC was 0.323 ± 0.268. The SA vs GS achieved a DSC of 0.274 ± 0.227, sensitivity of 0.764 ± 0.276 and PPV 0.217 ± 0.207. Its distribution was not significantly different from the mean DSC of inter-radiologist segmentation (p = 0.108, Wilcoxon test). ADC and lesion volume intraclass correlation coefficient (ICC) of the GS and of the correctly identified lesions by the SA was 0.996 for the median and 0.894 for the lesion volume (p < 0.001). The duration of the lesion volume segmentation by the SA was, on average, 10.22 ± 0.86 min, per patient. CONCLUSIONS: The SA provides equally reproducible segmentation results when compared to the manual segmentation of radiologists. Thus, the proposed method offers robust and efficient segmentation of MM lesions on WB-DWI. This method may aid accurate assessment of tumor burden and therefore provide insights to treatment response assessment. |
|---|---|
| Main Authors: | Almeida, Sílvia D. |
| Other Authors: | Santinha, João; Oliveira, Francisco P.M.; Ip, Joana; Lisitskaya, Maria; Lourenço, João; Uysal, Aycan; Matos, Celso; João, Cristina; Papanikolaou, Nikolaos |
| Subject: | Atlas-based segmentation Diffusion weighted imaging Multiple myeloma Semi-automatic segmentation Total lesion burden Radiological and Ultrasound Technology Oncology Radiology Nuclear Medicine and imaging |
| Year: | 2020 |
| Country: | Portugal |
| Document type: | article |
| Access type: | open access |
| Associated institution: | Universidade Nova de Lisboa |
| Language: | English |
| Origin: | Repositório Institucional da UNL |
| _version_ | 1868984041029500928 |
|---|---|
| author | Almeida, Sílvia D. |
| author2 | Santinha, João Oliveira, Francisco P.M. Ip, Joana Lisitskaya, Maria Lourenço, João Uysal, Aycan Matos, Celso João, Cristina Papanikolaou, Nikolaos |
| author2_role | author author author author author author author author author |
| author_facet | Almeida, Sílvia D. Santinha, João Oliveira, Francisco P.M. Ip, Joana Lisitskaya, Maria Lourenço, João Uysal, Aycan Matos, Celso João, Cristina Papanikolaou, Nikolaos |
| author_role | author |
| contributor_name_str_mv | NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM) BioMed Central (BMC) RUN |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Almeida, Sílvia D.\"},{\"Person.name\":\"Santinha, João\"},{\"Person.name\":\"Oliveira, Francisco P.M.\"},{\"Person.name\":\"Ip, Joana\"},{\"Person.name\":\"Lisitskaya, Maria\"},{\"Person.name\":\"Lourenço, João\"},{\"Person.name\":\"Uysal, Aycan\"},{\"Person.name\":\"Matos, Celso\"},{\"Person.name\":\"João, Cristina\"},{\"Person.name\":\"Papanikolaou, Nikolaos\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM) BioMed Central (BMC) RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Almeida, Sílvia D. Santinha, João Oliveira, Francisco P.M. Ip, Joana Lisitskaya, Maria Lourenço, João Uysal, Aycan Matos, Celso João, Cristina Papanikolaou, Nikolaos |
| datacite.date.Accepted.fl_str_mv | 2020-01-13T00:00:00Z |
| datacite.date.available.fl_str_mv | 2020-01-27T23:30:12Z |
| datacite.date.embargoed.fl_str_mv | 2020-01-27T23:30:12Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Atlas-based segmentation Diffusion weighted imaging Multiple myeloma Semi-automatic segmentation Total lesion burden Radiological and Ultrasound Technology Oncology Radiology Nuclear Medicine and imaging |
| datacite.titles.title.fl_str_mv | Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI |
| dc.contributor.none.fl_str_mv | NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM) BioMed Central (BMC) RUN |
| dc.creator.none.fl_str_mv | Almeida, Sílvia D. Santinha, João Oliveira, Francisco P.M. Ip, Joana Lisitskaya, Maria Lourenço, João Uysal, Aycan Matos, Celso João, Cristina Papanikolaou, Nikolaos |
| dc.date.Accepted.fl_str_mv | 2020-01-13T00:00:00Z |
| dc.date.available.fl_str_mv | 2020-01-27T23:30:12Z |
| dc.date.embargoed.fl_str_mv | 2020-01-27T23:30:12Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/91871 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Atlas-based segmentation Diffusion weighted imaging Multiple myeloma Semi-automatic segmentation Total lesion burden Radiological and Ultrasound Technology Oncology Radiology Nuclear Medicine and imaging |
| dc.title.fl_str_mv | Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | BACKGROUND: Whole-body diffusion weighted imaging (WB-DWI) has proven value to detect multiple myeloma (MM) lesions. However, the large volume of imaging data and the presence of numerous lesions makes the reading process challenging. The aim of the current study was to develop a semi-automatic lesion segmentation algorithm for WB-DWI images in MM patients and to evaluate this smart-algorithm (SA) performance by comparing it to the manual segmentations performed by radiologists. METHODS: An atlas-based segmentation was developed to remove the high-signal intensity normal tissues on WB-DWI and to restrict the lesion area to the skeleton. Then, an outlier threshold-based segmentation was applied to WB-DWI images, and the segmented area's signal intensity was compared to the average signal intensity of a low-fat muscle on T1-weighted images. This method was validated in 22 whole-body DWI images of patients diagnosed with MM. Dice similarity coefficient (DSC), sensitivity and positive predictive value (PPV) were computed to evaluate the SA performance against the gold standard (GS) and to compare with the radiologists. A non-parametric Wilcoxon test was also performed. Apparent diffusion coefficient (ADC) histogram metrics and lesion volume were extracted for the GS segmentation and for the correctly identified lesions by SA and their correlation was assessed. RESULTS: The mean inter-radiologists DSC was 0.323 ± 0.268. The SA vs GS achieved a DSC of 0.274 ± 0.227, sensitivity of 0.764 ± 0.276 and PPV 0.217 ± 0.207. Its distribution was not significantly different from the mean DSC of inter-radiologist segmentation (p = 0.108, Wilcoxon test). ADC and lesion volume intraclass correlation coefficient (ICC) of the GS and of the correctly identified lesions by the SA was 0.996 for the median and 0.894 for the lesion volume (p < 0.001). The duration of the lesion volume segmentation by the SA was, on average, 10.22 ± 0.86 min, per patient. CONCLUSIONS: The SA provides equally reproducible segmentation results when compared to the manual segmentation of radiologists. Thus, the proposed method offers robust and efficient segmentation of MM lesions on WB-DWI. This method may aid accurate assessment of tumor burden and therefore provide insights to treatment response assessment. |
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| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/6ea9fab5-aad2-4cac-ac5d-ebfbd3da21af/download |
| id | run_e6e52a85cca856439e97595afc0f5c42 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/91871 |
| inst_facet_str | urn:organizationAcronym:unl{{{_:::_}}}Universidade Nova de Lisboa |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
| network_acronym_str | run |
| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/91871 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Almeida, Sílvia D. Santinha, João Oliveira, Francisco P.M. Ip, Joana Lisitskaya, Maria Lourenço, João Uysal, Aycan Matos, Celso João, Cristina Papanikolaou, Nikolaos |
| publishDate | 2020 |
| repo_facet_str | urn:repositoryAcronym:run{{{_:::_}}}Repositório Institucional da UNL |
| reponame_str | Repositório Institucional da UNL |
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| service_str_mv | urn:repositoryAcronym:run |
| spelling | engenBACKGROUND: Whole-body diffusion weighted imaging (WB-DWI) has proven value to detect multiple myeloma (MM) lesions. However, the large volume of imaging data and the presence of numerous lesions makes the reading process challenging. The aim of the current study was to develop a semi-automatic lesion segmentation algorithm for WB-DWI images in MM patients and to evaluate this smart-algorithm (SA) performance by comparing it to the manual segmentations performed by radiologists. METHODS: An atlas-based segmentation was developed to remove the high-signal intensity normal tissues on WB-DWI and to restrict the lesion area to the skeleton. Then, an outlier threshold-based segmentation was applied to WB-DWI images, and the segmented area's signal intensity was compared to the average signal intensity of a low-fat muscle on T1-weighted images. This method was validated in 22 whole-body DWI images of patients diagnosed with MM. Dice similarity coefficient (DSC), sensitivity and positive predictive value (PPV) were computed to evaluate the SA performance against the gold standard (GS) and to compare with the radiologists. A non-parametric Wilcoxon test was also performed. Apparent diffusion coefficient (ADC) histogram metrics and lesion volume were extracted for the GS segmentation and for the correctly identified lesions by SA and their correlation was assessed. RESULTS: The mean inter-radiologists DSC was 0.323 ± 0.268. The SA vs GS achieved a DSC of 0.274 ± 0.227, sensitivity of 0.764 ± 0.276 and PPV 0.217 ± 0.207. Its distribution was not significantly different from the mean DSC of inter-radiologist segmentation (p = 0.108, Wilcoxon test). ADC and lesion volume intraclass correlation coefficient (ICC) of the GS and of the correctly identified lesions by the SA was 0.996 for the median and 0.894 for the lesion volume (p < 0.001). The duration of the lesion volume segmentation by the SA was, on average, 10.22 ± 0.86 min, per patient. CONCLUSIONS: The SA provides equally reproducible segmentation results when compared to the manual segmentation of radiologists. Thus, the proposed method offers robust and efficient segmentation of MM lesions on WB-DWI. This method may aid accurate assessment of tumor burden and therefore provide insights to treatment response assessment.application/pdfenQuantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWIAlmeida, Sílvia D.Santinha, JoãoOliveira, Francisco P.M.Ip, JoanaLisitskaya, MariaLourenço, JoãoUysal, AycanMatos, CelsoJoão, CristinaPapanikolaou, NikolaosNOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)BioMed Central (BMC)HostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptISSNIsPartOf1470-7330URNIsPartOfPURE: 16530209URNIsPartOfPURE UUID: 66ee9d89-ffa8-4976-af5e-40a56a00246dURNIsPartOfScopus: 85077786257URNIsPartOfPubMed: 31931880URNIsPartOfWOS: 000521263800002DOIIsPartOf10.1186/s40644-020-0286-52020-01-27T23:30:12Z2020-01-132020-01-13T00:00:00ZHandlehttp://hdl.handle.net/10362/91871http://purl.org/coar/access_right/c_abf2open accessAtlas-based segmentationDiffusion weighted imagingMultiple myelomaSemi-automatic segmentationTotal lesion burdenRadiological and Ultrasound TechnologyOncologyRadiology Nuclear Medicine and imaging2489392 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/6ea9fab5-aad2-4cac-ac5d-ebfbd3da21af/download |
| spellingShingle | Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI Almeida, Sílvia D. Atlas-based segmentation Diffusion weighted imaging Multiple myeloma Semi-automatic segmentation Total lesion burden Radiological and Ultrasound Technology Oncology Radiology Nuclear Medicine and imaging |
| status | SINGLETON |
| subject.fl_str_mv | Atlas-based segmentation Diffusion weighted imaging Multiple myeloma Semi-automatic segmentation Total lesion burden Radiological and Ultrasound Technology Oncology Radiology Nuclear Medicine and imaging |
| title | Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI |
| title_full | Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI |
| title_fullStr | Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI |
| title_full_unstemmed | Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI |
| title_short | Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI |
| title_sort | Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI |
| topic | Atlas-based segmentation Diffusion weighted imaging Multiple myeloma Semi-automatic segmentation Total lesion burden Radiological and Ultrasound Technology Oncology Radiology Nuclear Medicine and imaging |
| topic_facet | Atlas-based segmentation Diffusion weighted imaging Multiple myeloma Semi-automatic segmentation Total lesion burden Radiological and Ultrasound Technology Oncology Radiology Nuclear Medicine and imaging |
| url | http://hdl.handle.net/10362/91871 |
| visible | 1 |