Association between textural and morphological tumor indices on baseline PET‐CT and early metabolic response on interim PET‐CT in bulky malignant lymphomas

Abstract

The combination of usual prognostic factors with appropriately chosen textural and shape parameters evaluated on baseline PET-CT improves the prediction of early metabolic response in bulky lymphoma.

Tumour functional sphericity from PET images: prognostic value in NSCLC and impact of delineation method

“…A metric similar to asphericity was also found moderately correlated (r=0.53) with heterogeneity visual scoring and associated with recurrence in 83 lung cancer patients [12]. In 57 lymphoma patients PET uptakes larger than 50 cm 3 were characterized with numerous radiomics features including asphericity, after segmentation using two different fixed threshold (31 and 40% of SUV max ) [13]. Asphericity was not found to be predictive of metabolic response, contrary to other radiomics features.…”

“…Asphericity has up to now been evaluated mostly by one group [7][8][9][10], using a segmentation method based on adaptive thresholding taking into account the background activity concentration [11]. Two other groups investigated asphericity or a similar metric in squamous cell lung carcinoma [12] and in lymphoma [13]. Despite the early promising results associated with the use of asphericity as a PET image biomarker, the dependency of this parameter and its associated prognostic value on the choice of the segmentation methodology has not been investigated.…”

Predicting the pathological response to chemoradiotherapy of non-mucinous rectal cancer using pretreatment texture features based on intravoxel incoherent motion diffusion-weighted imaging

“…As a widely used texture analysis algorithm, gray-level co-occurrence matrix (GLCM) can provide second-order texture features that reflect the location of the pixels and the spatial interrelationship between gray values [34,35]. GLCM features are found promising in the prediction of therapeutic response in many kinds of tumors [18,23,36,37]. In this study, there were significant differences in the GLCM features, rather than in the first-order texture features, between the pCR and non-pCR groups, revealing that second-order texture features may have advantages over the first-order features obtained from IVIM-DWI maps in predicting the pathological response to nCRT in LARC.…”

“…These findings revealed that baseline GLCM features derived from IVIM-DWI could act as imaging predictors of pCR in patients with LARC. Among these GLCM features, Correlation and SumAverg are not directly related to heterogeneity, whereas Contrast and DifVarnc are measures of tissue heterogeneity [39], that is to say, lower Contrast or DifVarnc means less heterogeneity [35,36,39,40]. In the present study, all the values of Contrast and Dif-Varnc derived from the ADC, D, and D* maps in the pCR group were lower than those in the non-pCR group, implying that higher heterogeneity on IVIM-DWI maps may be associated with poorer therapeutic effect.…”

FDG-PET in PMBCL: which heterogeneity?

“…8 In 57 DLBCL and HL patients, they were not able to correlate the AUC-CSH of the largest tumor mass to the response at end-induction but showed that some textural spatial heterogeneity indices, such as the level of granularity of the tumor, were highly predictive. It will also be important to determine if the 8 patients who progressed in the high-risk group in the Ceriani et al study are those already identified in a previous study of this group with both high TLG and poor response at the end of induction immunochemotherapy.…”