The biological correlates of macroscopic breast tumour structure measured using fractal analysis in patients undergoing neoadjuvant chemotherapy

Giovanni, Pierluigi Di; Ahearn, Trevor; Semple, Scott; Lovell, Lesley; Miller, I.D.; Gilbert, Fiona J.; Redpath, Thomas William; Heys, Steven Darryll; Staff, Roger T.

doi:10.1007/s10549-012-2014-8

Cited by 15 publications

(6 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There have been no prior comparable studies specifically implemented to achieve a prediction of osteosarcoma chemotherapy response by fractal or GLCM MR image analysis. A single predictive study employing fractal MR image analysis was performed on breast tumors ( 16 ). A significant prognostic value was thereby achieved, but the quantitative association measures such as accuracy and AUC have not been calculated, thus limiting any direct comparison with our current results.…”

Section: Discussionmentioning

confidence: 99%

Fractal and Gray Level Cooccurrence Matrix Computational Analysis of Primary Osteosarcoma Magnetic Resonance Images Predicts the Chemotherapy Response

et al. 2017

View full text Add to dashboard Cite

The prediction of induction chemotherapy response at the time of diagnosis may improve outcomes in osteosarcoma by allowing for personalized tailoring of therapy. The aim of this study was thus to investigate the predictive potential of the so far unexploited computational analysis of osteosarcoma magnetic resonance (MR) images. Fractal and gray level cooccurrence matrix (GLCM) algorithms were employed in retrospective analysis of MR images of primary osteosarcoma localized in distal femur prior to the OsteoSa induction chemotherapy. The predicted and actual chemotherapy response outcomes were then compared by means of receiver operating characteristic (ROC) analysis and accuracy calculation. Dbin, Λ, and SCN were the standard fractal and GLCM features which significantly associated with the chemotherapy outcome, but only in one of the analyzed planes. Our newly developed normalized fractal dimension, called the space-filling ratio (SFR) exerted an independent and much better predictive value with the prediction significance accomplished in two of the three imaging planes, with accuracy of 82% and area under the ROC curve of 0.20 (95% confidence interval 0–0.41). In conclusion, SFR as the newly designed fractal coefficient provided superior predictive performance in comparison to standard image analysis features, presumably by compensating for the tumor size variation in MR images.

show abstract

Section: Discussionmentioning

confidence: 99%

Fractal and Gray Level Cooccurrence Matrix Computational Analysis of Primary Osteosarcoma Magnetic Resonance Images Predicts the Chemotherapy Response

et al. 2017

View full text Add to dashboard Cite

show abstract

“…The fractal fit of the tumor images showed to be related to the tumor response to chemotherapy [162]. At the moment, it is difficult to explain the genetic or epigenetic equivalent of the variable ‘goodness-of-fit’.…”

Section: Expert Commentarymentioning

confidence: 99%

Fractal dimension of chromatin: potential molecular diagnostic applications for cancer prognosis

Metze

2013

Expert Review of Molecular Diagnostics

View full text Add to dashboard Cite

Fractal characteristics of chromatin, revealed by light or electron microscopy, have been reported during the last 20 years. Fractal features can easily be estimated in digitalized microscopic images and are helpful for diagnosis and prognosis of neoplasias. During carcinogenesis and tumor progression, an increase of the fractal dimension (FD) of stained nuclei has been shown in intraepithelial lesions of the uterine cervix and the anus, oral squamous cell carcinomas or adenocarcinomas of the pancreas. Furthermore, an increased FD of chromatin is an unfavorable prognostic factor in squamous cell carcinomas of the oral cavity and the larynx, melanomas and multiple myelomas. High goodness-of-fit of the regression line of the FD is a favorable prognostic factor in acute leukemias and multiple myelomas. The nucleus has fractal and power-law organization in several different levels, which might in part be interrelated. Some possible relations between modifications of the chromatin organization during carcinogenesis and tumor progression and an increase of the FD of stained chromatin are suggested. Furthermore, increased complexity of the chromatin structure, loss of heterochromatin and a less-perfect self-organization of the nucleus in aggressive neoplasias are discussed.

show abstract

“…It should be noted that there are no general thresholds that define a test to be clinically valid and also no possibility of comparison with any previous similar studies on the validity of MR image analysis in osteosarcoma for prediction of the tumor response to cytotoxic therapy. A comparable study of MR images was however performed on breast tumors with established predictive significance but did not provide any accuracy data (25). The accuracy of 74% obtained in the current study together with the good specificity and sensitivity obtained on individual images (72% sensitivity, 76% specificity; Table 1) and patients (70% sensitivity and 86% specificity for D qmax ; Figure 3) were an indication of good predictive value, comparable to the predictive accuracy of 82% obtained in our previous study of breast cancer patients with the similar multifractal image analysis approach (13).…”

Section: Discussionmentioning

confidence: 99%

Prediction of Chemotherapy Response in Primary Osteosarcoma by Use of the Multifractal Analysis of Magnetic Resonance Images

Djuričić¹,

Vasiljević²,

Ristić³

et al. 2018

Iran J Radiol

View full text Add to dashboard Cite

Background: Due to the high level of cytogenetic heterogeneity in osteosarcoma, personalized treatment is the promising strategy for the improvement in outcomes. This is currently not possible due to the absence of targeted therapies and reliable predictors for response to induction chemotherapy. Objectives: To investigate the predictive value of computational analysis of osteosarcoma magnetic resonance (MR) images. Patients and Methods:Multifractal analysis was performed on MR images of primary osteosarcoma of long tubular bones prior to OsteoSa induction chemotherapy. A total of 900 images derived from 67 good and poor responder patients were classified and compared to the actual retrospective outcome. Results: Among the six calculated multifractal features, Dqmax exerted the highest predictive value with the prediction accuracy of 74.3%, sensitivity of 72.4% and specificity of 76.2%. The obtained classification accuracy was validated by a ten V-fold split sample cross validation. The area under the curve (AUC) value for the best-performing multifractal Dqmax feature was 0.82 (95% confidence interval, 0.70 -0.91). Conclusion: These results suggest for the first time that measuring tumor structure by using multifractal geometry can predict an individual patient response to neoadjuvant cytotoxic therapy. Therefore, it potentially allows precise implementation of alternative treatment options. This predictive approach made use of digital data that is routinely collected but currently still underexploited.

show abstract

The biological correlates of macroscopic breast tumour structure measured using fractal analysis in patients undergoing neoadjuvant chemotherapy

Cited by 15 publications

References 28 publications

Fractal and Gray Level Cooccurrence Matrix Computational Analysis of Primary Osteosarcoma Magnetic Resonance Images Predicts the Chemotherapy Response

Fractal and Gray Level Cooccurrence Matrix Computational Analysis of Primary Osteosarcoma Magnetic Resonance Images Predicts the Chemotherapy Response

Fractal dimension of chromatin: potential molecular diagnostic applications for cancer prognosis

Prediction of Chemotherapy Response in Primary Osteosarcoma by Use of the Multifractal Analysis of Magnetic Resonance Images

Contact Info

Product

Resources

About