The promise of multiparametric imaging in oncology: how do we move forward?

Even, Aniek J.G.; Ruysscher, Dirk De; Elmpt, Wouter van

doi:10.1007/s00259-016-3361-1

Cited by 8 publications

(6 citation statements)

References 30 publications

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“…Recent advances in artificial intelligence (AI), and more specifically in machine and deep learning technology, enable computer-aided diagnosis (CAD), where computer algorithms support or perform part of the image interpretation [2,3] . This is especially useful in multiparametric imaging, in which multiple modalities or techniques are combined to make the final diagnosis [4] .…”

Section: Computer-aided Diagnosis and Prostate Cancermentioning

confidence: 99%

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Wildeboer

Sloun

Wijkstra

et al. 2020

Computer Methods and Programs in Biomedicine

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DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

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Section: Computer-aided Diagnosis and Prostate Cancermentioning

confidence: 99%

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Wildeboer

Sloun

Wijkstra

et al. 2020

Computer Methods and Programs in Biomedicine

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“…Due to the limitations of the invasive medical interventions (biopsy) which can’t always detect heterogeneity of intra-tumor structures from a single attempt, as well as non-rational to perform multiple biopsies for the patient or performing biopsies for all suspected lesions in the individual, [ 5 ] more realistic diagnostic methods were needed to provide a more inclusive view of tumor texture and biology including internal tumor characteristics. Previous studies have shown that multiparametric imaging can provide precise data for tumor biology and heterogeneity [ 6 , 7 ]. CT and MRI are very useful imaging modalities in the initial assessment and diagnosis of head and neck cancers and it has been widely used for therapy delineation as well as surveillance and post-therapy follow up [ 8 – 10 ].…”

Section: Introductionmentioning

confidence: 99%

Diffusion-Weighted Imaging (DWI) derived from PET/MRI for lymph node assessment in patients with Head and Neck Squamous Cell Carcinoma (HNSCC)

Freihat

Pintér²,

Kedves

et al. 2020

Cancer Imaging

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Background To determine the usefulness of Diffusion Weighted Imaging (DWI) derived from PET/MRI in discriminating normal from metastatic lymph nodes and the correlation between the metastatic lymph nodes with the grade and the localization of the primary tumor. Methods Retrospective study of 90 lymph nodes from 90 subjects; 65 patients who had proven histopathological metastatic lymph nodes from (HNSCC) who had undergone 18 F- PET/MRI for clinical staging and assessment and twenty-five lymph nodes were chosen from 25 healthy subjects. Apparent Diffusion Coefficient (ADC) map was generated from DWI with b values (0 and 800 s/mm 2 ). ADC values of the metastatic lymph nodes were calculated and compared to the normal lymph nodes ADC values, ROC was used to determine the best cut-off values to differentiate between the two group. Metastatic lymph nodes ADC mean values were compared to primary tumor grade and localization. Results ADCmean value of the metastatic lymph nodes in the overall sample (0.899 ± 0.98*10 − 3 mm 2 /sec) was significantly lower than the normal lymph nodes’ ADCmean value (1.267 ± 0.88*10 − 3 mm 2 /sec); ( P = 0.001). The area under the curve (AUC) was 98.3%, sensitivity and specificity were 92.3 and 98.6%, respectively, when using a threshold value of (1.138 ± 0.75*10 − 3 mm 2 /sec) to differentiate between both groups. Significant difference was found between metastatic lymph nodes (short-axis diameter < 10 mm), ADCmean (0.898 ± 0.72*10 − 3 mm 2 /sec), and the benign lymph nodes ADCmean, ( P = 0.001). No significant difference was found between ADCmean of the metastatic lymph nodes < 10 mm and the metastatic lymph nodes > 10 mm, ADCmean (0.899 ± 0.89*10 − 3 mm 2 /sec), ( P = 0.967). No significant differences were found between metastatic lymph nodes ADCmean values and different primary tumor grades or different primary tumor localization, ( P > 0.05). Conclusion DWI-ADC is an effective and efficient imaging technique in differentiating between normal and malignant lymph nodes, and might be helpful to discriminate sub-centimeters lymph nodes. Trial registration The trial is registered in clinical trials under ID: NCT04360993 , registration date: 17/04/2020.

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“…While escalating the dose to the entire PTV appears to be of little benefit to the patient, more innovative escalation strategies could provide us with some benefit. With the widespread implementation of functional imaging techniques and highly conformal delivery techniques, we have both the rationale and the tools to selectively escalate the dose within the tumor, while maintaining the dose to the surrounding tumor or the reverse strategy, selectively sparing functional areas within an organ [24][25][26]. These so-called dose painting strategies have sparked considerable interest in the lung cancer community, and several PET tracers have been advocated and investigated, including FDG, however to date these strategies have failed to produce meaningful clinical results.…”

Section: Radiotherapy For Stage III Nsclcmentioning

confidence: 99%

Standard of care in high-dose radiotherapy for localized non-small cell lung cancer

et al. 2017

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The promise of multiparametric imaging in oncology: how do we move forward?

Cited by 8 publications

References 30 publications

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Diffusion-Weighted Imaging (DWI) derived from PET/MRI for lymph node assessment in patients with Head and Neck Squamous Cell Carcinoma (HNSCC)

Standard of care in high-dose radiotherapy for localized non-small cell lung cancer

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