1
|
Comes MC, Fanizzi A, Bove S, Didonna V, Diotiaiuti S, Fadda F, La Forgia D, Giotta F, Latorre A, Nardone A, Palmiotti G, Ressa CM, Rinaldi L, Rizzo A, Talienti T, Tamborra P, Zito A, Lorusso V, Massafra R. Explainable 3D CNN based on baseline breast DCE-MRI to give an early prediction of pathological complete response to neoadjuvant chemotherapy. Comput Biol Med 2024; 172:108132. [PMID: 38508058 DOI: 10.1016/j.compbiomed.2024.108132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 01/29/2024] [Accepted: 02/12/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND So far, baseline Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has played a key role for the application of sophisticated artificial intelligence-based models using Convolutional Neural Networks (CNNs) to extract quantitative imaging information as earlier indicators of pathological Complete Response (pCR) achievement in breast cancer patients treated with neoadjuvant chemotherapy (NAC). However, these models did not exploit the DCE-MRI exams in their full geometry as 3D volume but analysed only few individual slices independently, thus neglecting the depth information. METHOD This study aimed to develop an explainable 3D CNN, which fulfilled the task of pCR prediction before the beginning of NAC, by leveraging the 3D information of post-contrast baseline breast DCE-MRI exams. Specifically, for each patient, the network took in input a 3D sequence containing the tumor region, which was previously automatically identified along the DCE-MRI exam. A visual explanation of the decision-making process of the network was also provided. RESULTS To the best of our knowledge, our proposal is competitive than other models in the field, which made use of imaging data alone, reaching a median AUC value of 81.8%, 95%CI [75.3%; 88.3%], a median accuracy value of 78.7%, 95%CI [74.8%; 82.5%], a median sensitivity value of 69.8%, 95%CI [59.6%; 79.9%] and a median specificity value of 83.3%, 95%CI [82.6%; 84.0%], respectively. The median and CIs were computed according to a 10-fold cross-validation scheme for 5 rounds. CONCLUSION Finally, this proposal holds high potential to support clinicians on non-invasively early pursuing or changing patient-centric NAC pathways.
Collapse
Affiliation(s)
- Maria Colomba Comes
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Annarita Fanizzi
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy.
| | - Samantha Bove
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy.
| | - Vittorio Didonna
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Sergio Diotiaiuti
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Federico Fadda
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Daniele La Forgia
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Francesco Giotta
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Agnese Latorre
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Annalisa Nardone
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Gennaro Palmiotti
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Cosmo Maurizio Ressa
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Lucia Rinaldi
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Alessandro Rizzo
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Tiziana Talienti
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Pasquale Tamborra
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Alfredo Zito
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Vito Lorusso
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Raffaella Massafra
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| |
Collapse
|
2
|
Amoroso N, Quarto S, La Rocca M, Tangaro S, Monaco A, Bellotti R. An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease. Front Aging Neurosci 2023; 15:1238065. [PMID: 37719873 PMCID: PMC10501457 DOI: 10.3389/fnagi.2023.1238065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Accepted: 08/08/2023] [Indexed: 09/19/2023] Open
Abstract
The advent of eXplainable Artificial Intelligence (XAI) has revolutionized the way human experts, especially from non-computational domains, approach artificial intelligence; this is particularly true for clinical applications where the transparency of the results is often compromised by the algorithmic complexity. Here, we investigate how Alzheimer's disease (AD) affects brain connectivity within a cohort of 432 subjects whose T1 brain Magnetic Resonance Imaging data (MRI) were acquired within the Alzheimer's Disease Neuroimaging Initiative (ADNI). In particular, the cohort included 92 patients with AD, 126 normal controls (NC) and 214 subjects with mild cognitive impairment (MCI). We show how graph theory-based models can accurately distinguish these clinical conditions and how Shapley values, borrowed from game theory, can be adopted to make these models intelligible and easy to interpret. Explainability analyses outline the role played by regions like putamen, middle and superior temporal gyrus; from a class-related perspective, it is possible to outline specific regions, such as hippocampus and amygdala for AD and posterior cingulate and precuneus for MCI. The approach is general and could be adopted to outline how brain connectivity affects specific brain regions.
Collapse
Affiliation(s)
- Nicola Amoroso
- Dipartimento di Farmacia-Scienze del Farmaco, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Silvano Quarto
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Marianna La Rocca
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| |
Collapse
|
3
|
Massafra R, Fanizzi A, Amoroso N, Bove S, Comes MC, Pomarico D, Didonna V, Diotaiuti S, Galati L, Giotta F, La Forgia D, Latorre A, Lombardi A, Nardone A, Pastena MI, Ressa CM, Rinaldi L, Tamborra P, Zito A, Paradiso AV, Bellotti R, Lorusso V. Analyzing breast cancer invasive disease event classification through explainable artificial intelligence. Front Med (Lausanne) 2023; 10:1116354. [PMID: 36817766 PMCID: PMC9932275 DOI: 10.3389/fmed.2023.1116354] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 01/13/2023] [Indexed: 02/05/2023] Open
Abstract
Introduction Recently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable. Methods Thus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori "Giovanni Paolo II" in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis. Results Age, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames. Discussion Thus, our framework aims at shortening the distance between AI and clinical practice.
Collapse
Affiliation(s)
| | | | - Nicola Amoroso
- INFN, Sezione di Bari, Bari, Italy
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Samantha Bove
- IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | | | - Domenico Pomarico
- INFN, Sezione di Bari, Bari, Italy
- Dipartimento di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | | | | | - Luisa Galati
- International Agency for Research on Cancer, Lyon, France
| | | | | | | | - Angela Lombardi
- Dipartimento di Ingegneria Elettrica e dell'Informazione, Politecnico di Bari, Bari, Italy
| | | | | | | | - Lucia Rinaldi
- IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | | | - Alfredo Zito
- IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | | | - Roberto Bellotti
- INFN, Sezione di Bari, Bari, Italy
- Dipartimento di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Vito Lorusso
- IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| |
Collapse
|
4
|
Massafra R, Comes MC, Bove S, Didonna V, Gatta G, Giotta F, Fanizzi A, La Forgia D, Latorre A, Pastena MI, Pomarico D, Rinaldi L, Tamborra P, Zito A, Lorusso V, Paradiso AV. Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy. J Pers Med 2022; 12:jpm12060953. [PMID: 35743737 PMCID: PMC9225219 DOI: 10.3390/jpm12060953] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 05/24/2022] [Accepted: 06/07/2022] [Indexed: 02/04/2023] Open
Abstract
To date, some artificial intelligence (AI) methods have exploited Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to identify finer tumor properties as potential earlier indicators of pathological Complete Response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). However, they work either for sagittal or axial MRI protocols. More flexible AI tools, to be used easily in clinical practice across various institutions in accordance with its own imaging acquisition protocol, are required. Here, we addressed this topic by developing an AI method based on deep learning in giving an early prediction of pCR at various DCE-MRI protocols (axial and sagittal). Sagittal DCE-MRIs refer to 151 patients (42 pCR; 109 non-pCR) from the public I-SPY1 TRIAL database (DB); axial DCE-MRIs are related to 74 patients (22 pCR; 52 non-pCR) from a private DB provided by Istituto Tumori “Giovanni Paolo II” in Bari (Italy). By merging the features extracted from baseline MRIs with some pre-treatment clinical variables, accuracies of 84.4% and 77.3% and AUC values of 80.3% and 78.0% were achieved on the independent tests related to the public DB and the private DB, respectively. Overall, the presented method has shown to be robust regardless of the specific MRI protocol.
Collapse
Affiliation(s)
- Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Maria Colomba Comes
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Samantha Bove
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Gianluca Gatta
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (G.G.); (A.L.)
| | - Francesco Giotta
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (V.L.)
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
- Correspondence: (A.F.); (D.L.F.)
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
- Correspondence: (A.F.); (D.L.F.)
| | - Agnese Latorre
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (G.G.); (A.L.)
| | - Maria Irene Pastena
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.I.P.); (A.Z.)
| | - Domenico Pomarico
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Lucia Rinaldi
- Struttura Semplice Dipartimentale di Oncologia Per la Presa in Carico Globale del Paziente, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Pasquale Tamborra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Alfredo Zito
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.I.P.); (A.Z.)
| | - Vito Lorusso
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (V.L.)
| | - Angelo Virgilio Paradiso
- Oncologia Sperimentale e Biobanca, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| |
Collapse
|
5
|
Bove S, Comes MC, Lorusso V, Cristofaro C, Didonna V, Gatta G, Giotta F, La Forgia D, Latorre A, Pastena MI, Petruzzellis N, Pomarico D, Rinaldi L, Tamborra P, Zito A, Fanizzi A, Massafra R. A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients. Sci Rep 2022; 12:7914. [PMID: 35552476 PMCID: PMC9098914 DOI: 10.1038/s41598-022-11876-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 04/29/2022] [Indexed: 12/19/2022] Open
Abstract
In breast cancer patients, an accurate detection of the axillary lymph node metastasis status is essential for reducing distant metastasis occurrence probabilities. In case of patients resulted negative at both clinical and instrumental examination, the nodal status is commonly evaluated performing the sentinel lymph-node biopsy, that is a time-consuming and expensive intraoperative procedure for the sentinel lymph-node (SLN) status assessment. The aim of this study was to predict the nodal status of 142 clinically negative breast cancer patients by means of both clinical and radiomic features extracted from primary breast tumor ultrasound images acquired at diagnosis. First, different regions of interest (ROIs) were segmented and a radiomic analysis was performed on each ROI. Then, clinical and radiomic features were evaluated separately developing two different machine learning models based on an SVM classifier. Finally, their predictive power was estimated jointly implementing a soft voting technique. The experimental results showed that the model obtained by combining clinical and radiomic features provided the best performances, achieving an AUC value of 88.6%, an accuracy of 82.1%, a sensitivity of 100% and a specificity of 78.2%. The proposed model represents a promising non-invasive procedure for the SLN status prediction in clinically negative patients.
Collapse
Affiliation(s)
- Samantha Bove
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Maria Colomba Comes
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Vito Lorusso
- Unità Operativa Complessa Di Oncologia Medica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Cristian Cristofaro
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Gianluca Gatta
- Dipartimento Di Medicina Di Precisione, Università Della Campania "Luigi Vanvitelli", 80131, Napoli, Italy
| | - Francesco Giotta
- Unità Operativa Complessa Di Oncologia Medica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale Di Radiologia Senologica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy.
| | - Agnese Latorre
- Unità Operativa Complessa Di Oncologia Medica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Maria Irene Pastena
- Unità Operativa Complessa Di Anatomia Patologica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Nicole Petruzzellis
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Domenico Pomarico
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy.
| | - Lucia Rinaldi
- Struttura Semplice Dipartimentale Di Oncologia Per La Presa in Carico Globale del Paziente, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Pasquale Tamborra
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Alfredo Zito
- Unità Operativa Complessa Di Anatomia Patologica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| |
Collapse
|
6
|
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
|
7
|
Safavian N, Batouli SAH, Oghabian MA. An automatic level set method for hippocampus segmentation in MR images. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2019. [DOI: 10.1080/21681163.2019.1706054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Nazanin Safavian
- Neuroimaging and Analysis Group (NIAG), Tehran University of Medical Sciences, Tehran, Iran
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Seyed Amir Hossein Batouli
- Neuroimaging and Analysis Group (NIAG), Tehran University of Medical Sciences, Tehran, Iran
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Oghabian
- Neuroimaging and Analysis Group (NIAG), Tehran University of Medical Sciences, Tehran, Iran
- Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
8
|
Zhu H, Tang Z, Cheng H, Wu Y, Fan Y. Multi-atlas label fusion with random local binary pattern features: Application to hippocampus segmentation. Sci Rep 2019; 9:16839. [PMID: 31727982 PMCID: PMC6856174 DOI: 10.1038/s41598-019-53387-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 10/30/2019] [Indexed: 01/15/2023] Open
Abstract
Automatic and reliable segmentation of the hippocampus from magnetic resonance (MR) brain images is extremely important in a variety of neuroimage studies. To improve the hippocampus segmentation performance, a local binary pattern based feature extraction method is developed for machine learning based multi-atlas hippocampus segmentation. Under the framework of multi-atlas image segmentation (MAIS), a set of selected atlases are registered to images to be segmented using a non-linear image registration algorithm. The registered atlases are then used as training data to build linear regression models for segmenting the images based on the image features, referred to as random local binary pattern (RLBP), extracted using a novel image feature extraction method. The RLBP based MAIS algorithm has been validated for segmenting hippocampus based on a data set of 135 T1 MR images which are from the Alzheimer’s Disease Neuroimaging Initiative database (adni.loni.usc.edu). By using manual segmentation labels produced by experienced tracers as the standard of truth, six segmentation evaluation metrics were used to evaluate the image segmentation results by comparing automatic segmentation results with the manual segmentation labels. We further computed Cohen’s d effect size to investigate the sensitivity of each segmenting method in detecting volumetric differences of the hippocampus between different groups of subjects. The evaluation results showed that our method was competitive to state-of-the-art label fusion methods in terms of accuracy. Hippocampal volumetric analysis showed that the proposed RLBP method performed well in detecting the volumetric differences of the hippocampus between groups of Alzheimer’s disease patients, mild cognitive impairment subjects, and normal controls. These results have demonstrated that the RLBP based multi-atlas image segmentation method could facilitate efficient and accurate extraction of the hippocampus and may help predict Alzheimer’s disease. The codes of the proposed method is available (https://www.nitrc.org/frs/?group_id=1242).
Collapse
Affiliation(s)
- Hancan Zhu
- School of Mathematics Physics and Information, Shaoxing University, Shaoxing, Zhejiang, 312000, China
| | - Zhenyu Tang
- Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100191, China
| | - Hewei Cheng
- Department of Biomedical Engineering, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Yihong Wu
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| |
Collapse
|
9
|
Pang S, Lu Z, Jiang J, Zhao L, Lin L, Li X, Lian T, Huang M, Yang W, Feng Q. Hippocampus Segmentation Based on Iterative Local Linear Mapping With Representative and Local Structure-Preserved Feature Embedding. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:2271-2280. [PMID: 30908202 DOI: 10.1109/tmi.2019.2906727] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Hippocampus segmentation plays a significant role in mental disease diagnoses, such as Alzheimer's disease, epilepsy, and so on. Patch-based multi-atlas segmentation (PBMAS) approach is a popular method for hippocampus segmentation and has achieved a promising result. However, the PBMAS approach needs high computation cost due to registration and the segmentation accuracy is subject to the registration accuracy. In this paper, we propose a novel method based on iterative local linear mapping (ILLM) with the representative and local structure-preserved feature embedding to achieve accurate and robust hippocampus segmentation with no need for registration. In the proposed approach, semi-supervised deep autoencoder (SSDA) exploits unsupervised deep autoencoder and local structure-preserved manifold regularization to nonlinearly transform the extracted magnetic resonance (MR) patch to embedded feature manifold, whose adjacent relationship is similar to the signed distance map (SDM) patch manifold. Local linear mapping is used to preliminarily predict SDM patch corresponding to the MR patch. Subsequently, threshold segmentation generates a preliminary segmentation. The ILLM refines the segmentation result iteratively by ensuring the local constraints of embedded feature manifold and SDM patch manifold using a space-constrained dictionary update. Thus, a refined segmentation is obtained with no need for registration. The experiments on 135 subjects from ADNI dataset show that the proposed approach is superior to the state-of-the-art PBMAS and classification-based approaches with mean Dice similarity coefficients of 0.8852±0.0203 and 0.8783 ± 0.0251 for bilateral hippocampus segmentation of 1.5T and 3.0T datasets, respectively.
Collapse
|
10
|
Amoroso N, Diacono D, La Rocca M, Bellotti R, Tangaro S. Salient networks: a novel application to study Alzheimer disease. Biomed Eng Online 2018; 17:162. [PMID: 30458801 PMCID: PMC6245497 DOI: 10.1186/s12938-018-0566-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Background Extracting fundamental information from data, thus underlining hidden structures or removing noisy information, is one of the most important aims in different scientific fields especially in biological and medical sciences. In this article, we propose an innovative complex network application able to identify salient links for detecting the effect of Alzheimer’s disease on brain connectivity. We first build a network model of brain connectivity from structural Magnetic Resonance Imaging (MRI) data, then we study salient networks retrieved from the original ones. Results Investigating informative power of the salient skeleton features in combination with those of the original networks we obtain an accuracy of \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$0.91 \pm 0.01$$\end{document}0.91±0.01 for the distinction of Alzheimer disease (AD) patients from normal controls (NC). This performance significantly overcomes accuracy of the original network features. Moreover salient networks are able to correctly discriminate normal controls (NC) from AD patients and NC from subjects with mild cognitive impairment that will convert to AD (cMCI). These evaluations, performed on an independent dataset, give an accuracy of \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$0.79 \pm 0.01$$\end{document}0.79±0.01 and \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$0.76 \pm 0.01$$\end{document}0.76±0.01 respectively for NC-AD and NC-cMCI classifications. Therefore, most of the informative content of the original networks is kept after the 92 \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$\%$$\end{document}% and 82 \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$\%$$\end{document}% reduction respectively in the number of nodes and links. In addition, the present approach, applied to a publicly available MRI dataset from the Alzheimer Disease Neuroimaging Initiative (ADNI), brings out also some interesting aspects related to the topologies and hubs of the networks. Conclusions The experimental results demonstrate how salient networks can highlight important brain network characteristics and structural pathological changes, while reducing considerably data complexity and computational requirements.
Collapse
Affiliation(s)
- Nicola Amoroso
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli Studi di Bari "A. Moro", Via Giovanni Amendola 173, 70125, Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via Orabona 4, 70123, Bari, Italy
| | - Domenico Diacono
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via Orabona 4, 70123, Bari, Italy
| | - Marianna La Rocca
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli Studi di Bari "A. Moro", Via Giovanni Amendola 173, 70125, Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via Orabona 4, 70123, Bari, Italy
| | - Roberto Bellotti
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli Studi di Bari "A. Moro", Via Giovanni Amendola 173, 70125, Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via Orabona 4, 70123, Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via Orabona 4, 70123, Bari, Italy.
| | | |
Collapse
|
11
|
Amoroso N, La Rocca M, Bruno S, Maggipinto T, Monaco A, Bellotti R, Tangaro S. Multiplex Networks for Early Diagnosis of Alzheimer's Disease. Front Aging Neurosci 2018; 10:365. [PMID: 30487745 PMCID: PMC6247675 DOI: 10.3389/fnagi.2018.00365] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Accepted: 10/23/2018] [Indexed: 12/18/2022] Open
Abstract
Analysis and quantification of brain structural changes, using Magnetic Resonance Imaging (MRI), are increasingly used to define novel biomarkers of brain pathologies, such as Alzheimer's disease (AD). Several studies have suggested that brain topological organization can reveal early signs of AD. Here, we propose a novel brain model which captures both intra- and inter-subject information within a multiplex network approach. This model localizes brain atrophy effects and summarizes them with a diagnostic score. On an independent test set, our multiplex-based score segregates (i) normal controls (NC) from AD patients with a 0.86±0.01 accuracy and (ii) NC from mild cognitive impairment (MCI) subjects that will convert to AD (cMCI) with an accuracy of 0.84±0.01. The model shows that illness effects are maximally detected by parceling the brain in equal volumes of 3, 000 mm3 ("patches"), without any a priori segmentation based on anatomical features. The multiplex approach shows great sensitivity in detecting anomalous changes in the brain; the robustness of the obtained results is assessed using both voxel-based morphometry and FreeSurfer morphological features. Because of its generality this method can provide a reliable tool for clinical trials and a disease signature of many neurodegenerative pathologies.
Collapse
Affiliation(s)
- Nicola Amoroso
- Dipartimento Interateneo di Fisica “M. Merlin”, Università degli studi di Bari “A. Moro”, Bari, Italy
- Dipartimento Interateneo di Fisica “M. Merlin”, Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Marianna La Rocca
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Stefania Bruno
- Blackheath Brain Injury Rehabilitation Centre, London, United Kingdom
| | - Tommaso Maggipinto
- Dipartimento Interateneo di Fisica “M. Merlin”, Università degli studi di Bari “A. Moro”, Bari, Italy
- Dipartimento Interateneo di Fisica “M. Merlin”, Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Alfonso Monaco
- Dipartimento Interateneo di Fisica “M. Merlin”, Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Roberto Bellotti
- Dipartimento Interateneo di Fisica “M. Merlin”, Università degli studi di Bari “A. Moro”, Bari, Italy
- Dipartimento Interateneo di Fisica “M. Merlin”, Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Sabina Tangaro
- Dipartimento Interateneo di Fisica “M. Merlin”, Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| |
Collapse
|
12
|
Zheng Q, Wu Y, Fan Y. Integrating Semi-supervised and Supervised Learning Methods for Label Fusion in Multi-Atlas Based Image Segmentation. Front Neuroinform 2018; 12:69. [PMID: 30364123 PMCID: PMC6191508 DOI: 10.3389/fninf.2018.00069] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Accepted: 09/18/2018] [Indexed: 11/26/2022] Open
Abstract
A novel label fusion method for multi-atlas based image segmentation method is developed by integrating semi-supervised and supervised machine learning techniques. Particularly, our method is developed in a pattern recognition based multi-atlas label fusion framework. We build random forests classification models for each image voxel to be segmented based on its corresponding image patches of atlas images that have been registered to the image to be segmented. The voxelwise random forests classification models are then applied to the image to be segmented to obtain a probabilistic segmentation map. Finally, a semi-supervised label propagation method is adapted to refine the probabilistic segmentation map by propagating its reliable voxelwise segmentation labels, taking into consideration consistency of local and global image appearance of the image to be segmented. The proposed method has been evaluated for segmenting the hippocampus in MR images and compared with alternative machine learning based multi-atlas based image segmentation methods. The experiment results have demonstrated that our method could obtain competitive segmentation performance (average Dice index > 0.88), compared with alternative multi-atlas based image segmentation methods under comparison. Source codes of the methods under comparison are publicly available at www.nitrc.org/frs/?group_id=1242.
Collapse
Affiliation(s)
- Qiang Zheng
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.,School of Computer and Control Engineering Yantai University, Yantai, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yihong Wu
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| |
Collapse
|
13
|
The PERSON project: a serious brain-computer interface game for treatment in cognitive impairment. HEALTH AND TECHNOLOGY 2018. [DOI: 10.1007/s12553-018-0258-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
14
|
Zheng Q, Fan Y. INTEGRATING SEMI-SUPERVISED LABEL PROPAGATION AND RANDOM FORESTS FOR MULTI-ATLAS BASED HIPPOCAMPUS SEGMENTATION. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2018; 2018:154-157. [PMID: 30079126 DOI: 10.1109/isbi.2018.8363544] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A novel multi-atlas based image segmentation method is proposed by integrating a semi-supervised label propagation method and a supervised random forests method in a pattern recognition based label fusion framework. The semi-supervised label propagation method takes into consideration local and global image appearance of images to be segmented and segments the images by propagating reliable segmentation results obtained by the supervised random forests method. Particularly, the random forests method is used to train a regression model based on image patches of atlas images for each voxel of the images to be segmented. The regression model is used to obtain reliable segmentation results to guide the label propagation for the segmentation. The proposed method has been compared with state-of-the-art multi-atlas based image segmentation methods for segmenting the hippocampus in MR images. The experiment results have demonstrated that our method obtained superior segmentation performance.
Collapse
Affiliation(s)
- Qiang Zheng
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,School of Computer and Control Engineering, Yantai University, Yantai, 264005, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| |
Collapse
|
15
|
Kong Y, Chen X, Wu J, Zhang P, Chen Y, Shu H. Automatic brain tissue segmentation based on graph filter. BMC Med Imaging 2018; 18:9. [PMID: 29739350 PMCID: PMC5941431 DOI: 10.1186/s12880-018-0252-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Accepted: 04/30/2018] [Indexed: 01/24/2023] Open
Abstract
Background Accurate segmentation of brain tissues from magnetic resonance imaging (MRI) is of significant importance in clinical applications and neuroscience research. Accurate segmentation is challenging due to the tissue heterogeneity, which is caused by noise, bias filed and partial volume effects. Methods To overcome this limitation, this paper presents a novel algorithm for brain tissue segmentation based on supervoxel and graph filter. Firstly, an effective supervoxel method is employed to generate effective supervoxels for the 3D MRI image. Secondly, the supervoxels are classified into different types of tissues based on filtering of graph signals. Results The performance is evaluated on the BrainWeb 18 dataset and the Internet Brain Segmentation Repository (IBSR) 18 dataset. The proposed method achieves mean dice similarity coefficient (DSC) of 0.94, 0.92 and 0.90 for the segmentation of white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) for BrainWeb 18 dataset, and mean DSC of 0.85, 0.87 and 0.57 for the segmentation of WM, GM and CSF for IBSR18 dataset. Conclusions The proposed approach can well discriminate different types of brain tissues from the brain MRI image, which has high potential to be applied for clinical applications.
Collapse
Affiliation(s)
- Youyong Kong
- Laboratory of Image Science and Technology, Key Laboratory of Computer Network and Information Integration, School of Computer Science and Engineering, Southeast University, Nanjing, People's Republic of China. .,International Joint Laboratory of Information Display and Visualization, Nanjing, People's Republic of China.
| | - Xiaopeng Chen
- Laboratory of Image Science and Technology, Key Laboratory of Computer Network and Information Integration, School of Computer Science and Engineering, Southeast University, Nanjing, People's Republic of China.,International Joint Laboratory of Information Display and Visualization, Nanjing, People's Republic of China
| | - Jiasong Wu
- Laboratory of Image Science and Technology, Key Laboratory of Computer Network and Information Integration, School of Computer Science and Engineering, Southeast University, Nanjing, People's Republic of China.,International Joint Laboratory of Information Display and Visualization, Nanjing, People's Republic of China
| | - Pinzheng Zhang
- Laboratory of Image Science and Technology, Key Laboratory of Computer Network and Information Integration, School of Computer Science and Engineering, Southeast University, Nanjing, People's Republic of China.,International Joint Laboratory of Information Display and Visualization, Nanjing, People's Republic of China
| | - Yang Chen
- Laboratory of Image Science and Technology, Key Laboratory of Computer Network and Information Integration, School of Computer Science and Engineering, Southeast University, Nanjing, People's Republic of China.,International Joint Laboratory of Information Display and Visualization, Nanjing, People's Republic of China
| | - Huazhong Shu
- Laboratory of Image Science and Technology, Key Laboratory of Computer Network and Information Integration, School of Computer Science and Engineering, Southeast University, Nanjing, People's Republic of China.,International Joint Laboratory of Information Display and Visualization, Nanjing, People's Republic of China
| |
Collapse
|
16
|
Amoroso N, Diacono D, Fanizzi A, La Rocca M, Monaco A, Lombardi A, Guaragnella C, Bellotti R, Tangaro S. Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge. J Neurosci Methods 2018; 302:3-9. [DOI: 10.1016/j.jneumeth.2017.12.011] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 12/18/2017] [Accepted: 12/20/2017] [Indexed: 01/18/2023]
|
17
|
Alzheimer's disease diagnosis based on the Hippocampal Unified Multi-Atlas Network (HUMAN) algorithm. Biomed Eng Online 2018; 17:6. [PMID: 29357893 PMCID: PMC5778685 DOI: 10.1186/s12938-018-0439-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Accepted: 01/10/2018] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Hippocampal atrophy is a supportive feature for the diagnosis of probable Alzheimer's disease (AD). However, even for an expert neuroradiologist, tracing the hippocampus and measuring its volume is a time consuming and extremely challenging task. Accordingly, the development of reliable fully-automated segmentation algorithms is of paramount importance. MATERIALS AND METHODS The present study evaluates (i) the precision and the robustness of the novel Hippocampal Unified Multi-Atlas Network (HUMAN) segmentation algorithm and (ii) its clinical reliability for AD diagnosis. For these purposes, we used a mixed cohort of 456 subjects and their T1 weighted magnetic resonance imaging (MRI) brain scans. The cohort included 145 controls (CTRL), 217 mild cognitive impairment (MCI) subjects and 94 AD patients from Alzheimer's Disease Neuroimaging Initiative (ADNI). For each subject the baseline, repeat, 12 and 24 month follow-up scans were available. RESULTS HUMAN provides hippocampal volumes with a 3% precision; volume measurements effectively reveal AD, with an area under the curve (AUC) AUC1 = 0.08 ± 0.02. Segmented volumes can also reveal the subtler effects present in MCI subjects, AUC2 = 0.76 ± 0.05. The algorithm is stable and reproducible over time, even for 24 month follow-up scans. CONCLUSIONS The experimental results demonstrate HUMAN is a precise segmentation algorithm, besides hippocampal volumes, provided by HUMAN, can effectively support the diagnosis of Alzheimer's disease and become a useful tool for other neuroimaging applications.
Collapse
|
18
|
Rasero J, Amoroso N, La Rocca M, Tangaro S, Bellotti R, Stramaglia S. Multivariate regression analysis of structural MRI connectivity matrices in Alzheimer's disease. PLoS One 2017; 12:e0187281. [PMID: 29135998 PMCID: PMC5685585 DOI: 10.1371/journal.pone.0187281] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Accepted: 10/17/2017] [Indexed: 01/21/2023] Open
Abstract
Alzheimer’s disease (AD) is the most common form of dementia among older people and increasing longevity ensures its prevalence will rise even further. Whether AD originates by disconnecting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. An important related challenge is to predict whether a given subject, with a mild cognitive impairment (MCI), will convert or not to AD. Here, our aim is to characterize the structural connectivity pattern of MCI and AD subjects using the multivariate distance matrix regression (MDMR) analysis, and to compare it to those of healthy subjects. MDMR is a technique developed in genomics that has been recently applied to functional brain network data, and here applied to identify brain nodes with different connectivity patterns, in controls and patients, because of brain atrophy. We address this issue at the macroscale by looking to differences in individual structural MRI brain networks, obtained from MR images according to a recently proposed definition of connectivity which measures the image similarity between patches at different locations in the brain. In particular, using data from ADNI, we selected four groups of subjects (all of them matched by age and sex): HC (healthy control participants), ncMCI (mild cognitive impairment not converting to AD), cMCI (mild cognitive impairment converting to AD) and AD. Next, we built structural MRI brain networks and performed group comparison for all the pairs of groups. Our results were three-fold: (i) considering the comparison of HC with the three other groups, the number of significant brain regions was 4 for ncMCI, 290 for cMCI and 74 for AD, out of a total of 549 regions; hence, in terms of the structural MRI connectivity here adopted, cMCI subjects have the maximal altered pattern w.r.t. healthy conditions. (ii) Eight and seven nodes were significant for the comparisons AD-ncMCI and AD-cMCI, respectively; six nodes, among them, were significant in both comparisons and these nodes form a connected brain region (corresponding to hippocampus, amygdala, Parahippocampal Gyrus, Planum Polare, Frontal Orbital Cortex, Temporal Pole and subcallosal cortex) showing reduced strength of connectivity in the MCI stages; (iii) The connectivity maps of cMCI and ncMCI subjects significantly differ from the connectome of healthy subjects in three regions all corresponding to Frontal Orbital Cortex.
Collapse
Affiliation(s)
- Javier Rasero
- Biocruces Health Research Institute. Hospital Universitario de Cruces. E-48903, Barakaldo, Spain
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
| | - Nicola Amoroso
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
| | - Marianna La Rocca
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
| | | | - Roberto Bellotti
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
- TIRES-Center of Innovative Technologies for Signal Detection and Processing, Università degli Studi Aldo Moro, Bari, Italy
| | - Sebastiano Stramaglia
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
- TIRES-Center of Innovative Technologies for Signal Detection and Processing, Università degli Studi Aldo Moro, Bari, Italy
- * E-mail:
| | | |
Collapse
|
19
|
Tangaro S, Fanizzi A, Amoroso N, Bellotti R. A fuzzy-based system reveals Alzheimer’s Disease onset in subjects with Mild Cognitive Impairment. Phys Med 2017; 38:36-44. [DOI: 10.1016/j.ejmp.2017.04.027] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Revised: 03/18/2017] [Accepted: 04/27/2017] [Indexed: 01/18/2023] Open
|
20
|
Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Saykin AJ, Shaw LM, Toga AW, Trojanowski JQ. Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. Alzheimers Dement 2017; 13:e1-e85. [PMID: 28342697 DOI: 10.1016/j.jalz.2016.11.007] [Citation(s) in RCA: 170] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 11/21/2016] [Accepted: 11/28/2016] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS We used standard searches to find publications using ADNI data. RESULTS (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
Collapse
Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - John C Morris
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | |
Collapse
|
21
|
Amoroso N, Monaco A, Tangaro S, Neuroimaging Initiative AD. Topological Measurements of DWI Tractography for Alzheimer's Disease Detection. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017; 2017:5271627. [PMID: 28352290 PMCID: PMC5352968 DOI: 10.1155/2017/5271627] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 10/27/2016] [Indexed: 12/20/2022]
Abstract
Neurodegenerative diseases affect brain morphology and connectivity, making complex networks a suitable tool to investigate and model their effects. Because of its stereotyped pattern Alzheimer's disease (AD) is a natural benchmark for the study of novel methodologies. Several studies have investigated the network centrality and segregation changes induced by AD, especially with a single subject approach. In this work, a holistic perspective based on the application of multiplex network concepts is introduced. We define and assess a diagnostic score to characterize the brain topology and measure the disease effects on a mixed cohort of 52 normal controls (NC) and 47 AD patients, from Alzheimer's Disease Neuroimaging Initiative (ADNI). The proposed topological score allows an accurate NC-AD classification: the average area under the curve (AUC) is 95% and the 95% confidence interval is 92%-99%. Besides, the combination of topological information and structural measures, such as the hippocampal volumes, was also investigated. Topology is able to capture the disease signature of AD and, as the methodology is general, it can find interesting applications to enhance our insight into disease with more heterogeneous patterns.
Collapse
Affiliation(s)
- Nicola Amoroso
- Università degli Studi di Bari “A. Moro”, Via Orabona 4, 70123 Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via Orabona 4, 70123 Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via Orabona 4, 70123 Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via Orabona 4, 70123 Bari, Italy
| | | |
Collapse
|
22
|
Maggipinto T, Bellotti R, Amoroso N, Diacono D, Donvito G, Lella E, Monaco A, Antonella Scelsi M, Tangaro S. DTI measurements for Alzheimer’s classification. Phys Med Biol 2017; 62:2361-2375. [DOI: 10.1088/1361-6560/aa5dbe] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
|