Advanced Ovarian Cancer: Multiparametric MR Imaging Demonstrates Response- and Metastasis-specific Effects

Sala, Evis; Kataoka, Masako; Priest, Andrew N.; Gill, Andrew B.; McLean, Mary A.; Joubert, Ilse; Graves, Martin J.; Crawford, Robin; Jimenez-Liñan, Mercedes; Earl, Helena; Hodgkin, Charlotte; Griffiths, John R.; Lomas, David J.; Brenton, James D.

doi:10.1148/radiol.11110175

Cited by 91 publications

(68 citation statements)

References 37 publications

(40 reference statements)

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“…There is a fairly small number of published studies applying conventional FFT-based in vivo MRS to the ovary [47][48][49][50][51][52][53][54][55][56][57][58][59][60]. In Ref.…”

Section: In Vivo Encoded Mrs Time Signals Using Conventional Fourier-mentioning

confidence: 99%

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Synergism of spectra averaging and extrapolation for quantification of in vivo MRS time signals encoded from the ovary

Belkić

2017

J Math Chem

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The fast Padé transform (FPT) is further optimized for encoded in vivo MRS time signals. This is achieved by a judicious combination of spectra averaging and time signal extrapolation. The motivation is in strengthening suppression of the over-sensitivity of signal processing to changes in model order K . Implementation is carried out in the FPT variant FPT (+) which, by numerically performed analytical continuation, converts divergent into convergent series. Convergence of reconstructions is monitored for a sequence of successive values of K . Comparison is made with the corresponding retrieval without spectra averaging and time signal extrapolation. Variances are dramatically reduced for the reconstructed parameters (complex frequencies and complex amplitudes) when spectra averaging and extrapolation are performed. Negligible variances imply convergence, which is accomplished herein using a single averaging procedure (no iterations). This has important implications in practice with encoded MRS data, providing remarkable efficiency, robustness and accuracy of Padé-based quantification. Algorithmically, spectra averaging and time signal extrapolation consist of four steps. First, the encoded time signal is used to compute total shape spectra (envelopes) for a sequence of model orders K , which is the number of resonances. Second, these envelopes are averaged (spectra averaging). All the spectra are computed at the same sweep frequencies whose number considerably exceeds the number of data points in the encoded time signal. Third, the complex average envelope is inverted to yield a new time signal which is longer than the encoded data (time signal extrapolation). Fourth, the Padé extrapolated time signal is quantified for a sequence of K to monitor convergence of the reconstructed parameters. All four steps are applied to in vivo MRS data encoded on a 3 T scanner from a borderline serous cystic ovarian tumor. We focus on this clinical problem to help develop effective methods for early detection of ovarian cancer, in order to improve survival for women afflicted with this malignancy. It is anticipated that the presently refined, multi-purpose Padé-based methodology with its practical advantages for in vivo MRS can contribute to this goal.

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“…There is a fairly small number of published studies applying conventional FFT-based in vivo MRS to the ovary [47][48][49][50][51][52][53][54][55][56][57][58][59][60]. In Ref.…”

Section: In Vivo Encoded Mrs Time Signals Using Conventional Fourier-mentioning

confidence: 99%

“…For all these in vivo studies, the 1 H MRS time signals were processed by the FFT, with fitting-based post-processing in Refs. [49,55,56,58,59].…”

Section: In Vivo Encoded Mrs Time Signals Using Conventional Fourier-mentioning

confidence: 99%

Synergism of spectra averaging and extrapolation for quantification of in vivo MRS time signals encoded from the ovary

Belkić

2017

J Math Chem

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“…With the addition of diffusion-weighted imaging (DWI) further improvement in specificity can be gained, although a substantial number of false positive findings still occur [38,39]. There is also some evidence that DWI may be helpful in reflecting response of advanced stage ovarian cancer to neoadjuvant chemotherapy [40].…”

Section: Possible Added Value Of Magnetic Resonance Imagingmentioning

confidence: 99%

“…Several investigative teams have applied in vivo MRS with the aim of improving ovarian cancer diagnostics. We performed a systematic search 3 of the literature and found published results for 134 malignant ovarian lesions, 114 benign ovarian lesions and 3 lesions of the ovary that were classified as "borderline" (BL), using in vivo proton MRS time signals encoded via clinical (1.5 or 3 T) magnetic resonance (MR) scanners [40,[43][44][45][46][47][48][49][50][51][52][53][54][55]. Appendix 1 provides a summary of the clinical aspects and design of these studies, with salient information concerning the reported MRS data.…”

Section: In Vivo Magnetic Resonance Spectroscopy Of the Ovary: The Prmentioning

confidence: 99%

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In vivo magnetic resonance spectroscopy for ovarian cancer diagnostics: quantification by the fast Padé transform

Belkić

2016

J Math Chem

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Early-stage ovarian cancer has an excellent prognosis, but due mainly to late detection, ovarian cancer remains a major cause of cancer deaths among women. In vivo magnetic resonance spectroscopy (MRS) would be an excellent candidate for early ovarian cancer detection, being non-invasive, surpassing anatomic imaging to identify metabolic features of cancer, and free of ionizing radiation. However, the present meta-analysis of 13 studies indicates that with conventional Fourier-based processing, in vivo MRS insufficiently distinguished 134 cancerous from 114 benign ovarian lesions. The fast Padé transform (FPT), an advanced signal processor with high-resolution and parametric (quantification-equipped) capabilities is best qualified for MRS time signals from the ovary, as demonstrated in our earlier proof-of-concept studies. We now apply the FPT to MRS time signals encoded in vivo on a 3 T scanner, echo time of 30 ms, from a borderline serous cystic ovarian tumor. The FPT-produced total shape spectrum was better resolved than with Fourier processing. Spectra averaging through the FPT generated a denoised total shape spectrum. Subsequent parametric analysis reconstructed dense component spectra in the "usual" mode: absorption and dispersion components mixed and "ersatz" mode: reconstructed phases set to zero, thus eliminating interference effects. Numerous metabolites, including potential cancer biomarkers, were identified and quantified by the FPT, including isoleucine, valine, lipids, lactate, alanine, lysine, N-acetyl aspartate, N-acetylneuraminic acid, gluta- mine, choline, phosphocholine, myoinositol. Many of these are difficult or impossible to detect with Fourier plus fitting techniques for in vivo MRS of the ovary. These Padé-generated results are promising, overcoming major barriers hindering MRS from becoming a key method for non-invasively assessing ovarian lesions.

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Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

Napel

Jardim‐Perassi

et al. 2018

Cancer

141

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Although cancer often is referred to as “a disease of the genes,” it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as “radiomics,” can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1‐2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of “deep learning,” wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions (“habitats”) within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology.

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Advanced Ovarian Cancer: Multiparametric MR Imaging Demonstrates Response- and Metastasis-specific Effects

Cited by 91 publications

References 37 publications

Synergism of spectra averaging and extrapolation for quantification of in vivo MRS time signals encoded from the ovary

Synergism of spectra averaging and extrapolation for quantification of in vivo MRS time signals encoded from the ovary

In vivo magnetic resonance spectroscopy for ovarian cancer diagnostics: quantification by the fast Padé transform

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

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