This work seeks to evaluate the combatting batch effect (ComBat) harmonization algorithm’s ability to reduce the variation in radiomic features arising from different imaging protocols and independently verify published results. The Gammex computed tomography (CT) electron density phantom and Quasar body phantom were imaged using 32 different chest imaging protocols. 107 radiomic features were extracted from 15 spatially varying spherical contours between 1.5 cm and 3 cm in each of the lung300 density, lung450 density, and wood inserts. The Kolmogorov–Smirnov test was used to determine significant differences in the distribution of the features and the concordance correlation coefficient (CCC) was used to measure the repeatability of the features from each protocol variation class (kVp, pitch, etc) before and after ComBat harmonization. P-values were corrected for multiple comparisons using the Benjamini–Hochberg–Yekutieli procedure. Finally, the ComBat algorithm was applied to human subject data using six different thorax imaging protocols with 135 patients. Spherical contours of un-irradiated lung (2 cm) and vertebral bone (1 cm) were used for radiomic feature extraction. ComBat harmonization reduced the percentage of features from significantly different distributions to 0%–2% or preserved 0% across all protocol variations for the lung300, lung450 and wood inserts. For the human subject data, ComBat harmonization reduced the percentage of significantly different features from 0%–59% for bone and 0%–19% for lung to 0% for both. This work verifies previously published results and demonstrates that ComBat harmonization is an effective means to harmonize radiomic features extracted from different imaging protocols to allow comparisons in large multi-institution datasets. Biological variation can be explicitly preserved by providing the ComBat algorithm with clinical or biological variables to protect. ComBat harmonization should be tested for its effect on predictive models.

To evaluate the repeatability of MRI and CT derived texture features and to investigate the feasibility of use in predictive single and multi-modality models for radiotherapy of non-small cell lung cancer, 59 texture features were extracted from unfiltered and wavelet filtered images. Repeatability of test-retest features from helical 4D CT scans, true fast MRI with steady state precession (TRUFISP), and volumetric interpolation breath-hold examination (VIBE) was determined by the concordance correlation coefficient (CCC). A workflow was developed to predict overall survival at 12, 18, and 24 months and tumour response at end of treatment for tumour features, and normal muscle tissue features as a control. Texture features were reduced to repeatable and stable features before clustering. Cluster representative feature selection was performed by univariate or medoid analysis before model selection. P-values were corrected for false discovery rate. Repeatable (CCC  ⩾  0.9) features were found for both tumour and normal muscle tissue: CT: 54.4% for tumour and 78.5% for normal tissue, TRUFISP: 64.4% for tumour and 67.8% for normal tissue, and VIBE: 52.6% for tumour and 72.9% for normal muscle tissue. Muscle tissue control analysis found seven significant models with six of seven models utilizing the univariate representative feature selection technique. Tumour analysis revealed 12 significant models for overall survival and none for tumour response at end of treatment. The accuracy of significant single modality was about the same for MR and CT. Multi-modality tumour models had comparable performance to single modality models. MR derived texture features may add value to predictive models and should be investigated in a larger cohort. Control analysis demonstrated that the medoid representative feature selection method may result in more robust models.

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Radiolabeled liposomes have been employed as diagnostic tools to monitor in vivo distribution of liposomes in real-time, which helps in optimizing the therapeutic efficacy of the liposomal drug delivery. This work utilizes the platform of [111In]-Liposome as a drug delivery vehicle, encapsulating a novel 18F-labeled carboplatin drug derivative ([18F]-FCP) as a dual-molecular imaging tool as both a radiolabeled drug and radiolabeled carrier. The approach has the potential for clinical translation in individual patients using a dual modal approach of clinically-relevant radionuclides of 18F positron emission tomography (PET) and 111In single photon emission computed tomography (SPECT). [111In]-Liposome was synthesized and evaluated in vivo by biodistribution and SPECT imaging. The [18F]-FCP encapsulated [111In]-Liposome nano-construct was investigated, in vivo, using an optimized dual-tracer PET and SPECT imaging in a nude mouse. The biodistribution data and SPECT imaging showed spleen and liver uptake of [111In]-Liposome and the subsequent clearance of activity with time. Dual-modality imaging of [18F]-FCP encapsulated [111In]-Liposome showed significant uptake in liver and spleen in both PET and SPECT images. Qualitative analysis of SPECT images and quantitative analysis of PET images showed the same pattern of activity during the imaging period and demonstrated the feasibility of dual-tracer imaging of a single dual-labeled nano-construct.