Quantitative MRI-based radiomics analysis identifies blood flow feature associated to overall survival for rectal cancer patients.

Radiomics objectively quantifies image information through numerical metrics known as features. In this study, we investigated the stability of magnetic resonance imaging (MRI)-based radiomics features in rectal cancer using both anatomical MRI and quantitative MRI (qMRI), when different methods to define the tumor volume were used. Second, we evaluated the prognostic value of stable features associated to 5-year progression-free survival (PFS) and overall survival (OS). On a 1.5 T MRI scanner, 81 patients underwent diagnostic MRI, an extended diffusion-weighted sequence with calculation of the apparent diffusion coefficient (ADC) and a multiecho dynamic contrast sequence generating both dynamic contrast-enhanced and dynamic susceptibility contrast (DSC) MR, allowing quantification of Ktrans, blood flow (BF) and area under the DSC curve (AUC). Radiomic features were extracted from T2w images and from ADC, Ktrans, BF and AUC maps. Tumor volumes were defined with three methods; machine learning, deep learning and manual delineations. The interclass correlation coefficient (ICC) assessed the stability of features. Internal validation was performed on 1000 bootstrap resamples in terms of discrimination, calibration and decisional benefit. For each combination of image and volume definition, 94 features were extracted. Features from qMRI contained higher prognostic potential than features from anatomical MRI. When stable features (> 90% ICC) were compared with clinical parameters, qMRI features demonstrated the best prognostic potential. A feature extracted from the DSC MRI parameter BF was associated with both PFS (p = 0.004) and OS (p = 0.004). In summary, stable qMRI-based radiomics features was identified, in particular, a feature based on BF from DSC MRI was associated with both PFS and OS.

Imaging the tumour microenvironment in rectal cancer: Decline in tumour blood flow during radiotherapy predicts good outcome.

Background and purpose: Measuring rectal tumour response to radiation is pivotal to restaging patients and for possibly stratification to a watch-and-wait strategy. Recognizing the importance of the tumour microenvironment, we investigated a less explored quantitative imaging marker assessing tumour blood flow (BF) for its potential to predict overall survival (OS). Materials and methods: 24 rectal cancer patients given curative-intent neoadjuvant radiotherapy underwent a multi-echo dynamic magnetic resonance imaging (MRI) sequence with gadolinium contrast for quantification of tumour BF before either 25x2 Gy (n = 18) with concomitant chemotherapy or 5x5 Gy (n = 6). CD34 staining of excised tumour tissue was performed and baseline blood samples were analysed for lactate dehydrogenase (LDH) and angiopoietin-2 (ANGPT-2). Tumour volumes were measured before and after treatment. After subsequent surgery, ypTN scoring assessed tumour response. Cox regression for 5-year OS analysis and t-test for group comparisons were performed. Results: The change in tumour BF (ΔBF) during neoadjuvant radiotherapy was a significant marker of OS, whereas tumour stage and volume were not related to OS. All patients with >20 % decline in BF were long-term survivors. Separating cases in two groups based on ΔBF revealed that patients with increase or a low decrease had higher baseline LDH (p = 0.032) and ANGPT-2 (p = 0.028) levels. Conclusion: MRI-assessed tumour ΔBF during neoadjuvant treatment is a significant predictor of OS in rectal cancer patients, making ΔBF a potential quantitative imaging biomarker for treatment stratification. Blood LDH and ANGPT-2 indicate that non-responding tumours may have a hypoxic microenvironment resistant to radiotherapy.

Semi-automatic tumor segmentation of rectal cancer based on functional magnetic resonance imaging.

Background and purpose: Tumor delineation is required both for radiotherapy planning and quantitative imaging biomarker purposes. It is a manual, time- and labor-intensive process prone to inter- and intraobserver variations. Semi or fully automatic segmentation could provide better efficiency and consistency. This study aimed to investigate the influence of including and combining functional with anatomical magnetic resonance imaging (MRI) sequences on the quality of automatic segmentations. Materials and methods: T2-weighted (T2w), diffusion weighted, multi-echo T2*-weighted, and contrast enhanced dynamic multi-echo (DME) MR images of eighty-one patients with rectal cancer were used in the analysis. Four classical machine learning algorithms; adaptive boosting (ADA), linear and quadratic discriminant analysis and support vector machines, were trained for automatic segmentation of tumor and normal tissue using different combinations of the MR images as input, followed by semi-automatic morphological post-processing. Manual delineations from two experts served as ground truth. The Sørensen-Dice similarity coefficient (DICE) and mean symmetric surface distance (MSD) were used as performance metric in leave-one-out cross validation. Results: Using T2w images alone, ADA outperformed the other algorithms, yielding a median per patient DICE of 0.67 and MSD of 3.6 mm. The performance improved when functional images were added and was highest for models based on either T2w and DME images (DICE: 0.72, MSD: 2.7 mm) or all four MRI sequences (DICE: 0.72, MSD: 2.5 mm). Conclusion: Machine learning models using functional MRI, in particular DME, have the potential to improve automatic segmentation of rectal cancer relative to models using T2w MRI alone.

The circulating soluble form of the CD40 costimulatory immune checkpoint receptor and liver metastasis risk in rectal cancer.

BACKGROUND: In colorectal cancer, the inflamed tumour microenvironment with its angiogenic activities is immune- tolerant and incites progression to liver metastasis. We hypothesised that angiogenic and inflammatory factors in serum samples from patients with non-metastatic rectal cancer could inform on liver metastasis risk. METHODS: We measured 84 angiogenic and inflammatory markers in serum sampled at the time of diagnosis within the population-based cohort of 122 stage I-III patients. In a stepwise manner, the statistically strongest proteins associated with time to development of liver metastasis were analysed in the corresponding serum samples from 273 stage II-III rectal cancer patients in three independent cohorts. RESULTS: We identified the soluble form of the costimulatory immune checkpoint receptor cluster of differentiation molecule 40 (sCD40) as a marker of liver metastasis risk across all patient cohorts-the higher the sCD40 level, the shorter time to liver metastasis. In patients receiving neoadjuvant treatment, the sCD40 value remained an independent variable associated with progression to liver metastasis along with the local treatment response. Of note, serum sCD40 was not associated with progression to lung metastasis. CONCLUSIONS: Circulating sCD40 is a marker of liver metastasis risk in rectal cancer and may be developed for use in clinical practice.

Sex-related differences in primary metastatic site in rectal cancer; associated with hemodynamic factors?

BACKGROUND AND PURPOSE: We investigated how features relating to pelvic cavity anatomy and tumor hemodynamic factors may influence systemic failure in rectal cancer. MATERIALS AND METHODS: Rectal cancer patients (207 women, 343 men), who had been prospectively enrolled onto six cohorts and given curative-intent therapy, were analyzed for the first metastatic event. In one of the cohorts, the diameter of the inferior mesenteric vein (IMV) was assessed on diagnostic abdominal computed tomography images (n = 113). Tumor volume (n = 193) and histologic response to neoadjuvant therapy (n = 445) were recorded from diagnostic magnetic resonance images and surgical specimens, respectively. RESULTS: More women than men developed lung metastasis (p = 0.037), while the opposite was the case for liver metastasis (p = 0.040). Wider IMV diameter correlated with larger tumor volume (r = 0.481, p < 0.001) and male sex (p < 0.001). Female sex was the only adverse prognostic factor for lung metastasis. When sex, tumor volume, and histologic response were taken into consideration, poor tumor response remained the only determinant for liver metastasis (p = 0.002). CONCLUSIONS: In a diverse rectal cancer population given curative-intent treatment, women and men had different outcome with regard to the primary metastatic site. Tumor hemodynamic factors should be considered in rectal cancer risk stratification.

Comparison of Intravoxel incoherent motion imaging and multiecho dynamic contrast-based MRI in rectal cancer.

BACKGROUND: Dynamic contrast-based MRI and intravoxel incoherent motion imaging (IVIM) MRI are both methods showing promise as diagnostic and prognostic tools in rectal cancer. Both methods aim at measuring perfusion-related parameters, but the relationship between them is unclear. PURPOSE: To investigate the relationship between perfusion- and permeability-related parameters obtained by IVIM-MRI, T1 -weighted dynamic contrast-enhanced (DCE)-MRI and T2 *-weighted dynamic susceptibility contrast (DSC)-MRI. STUDY TYPE: Prospective. SUBJECTS: In all, 94 patients with histologically confirmed rectal cancer. FIELD STRENGTH/SEQUENCE: Subjects underwent pretreatment 1.5T clinical procedure MRI, and in addition a study-specific diffusion-weighted sequence (b = 0, 25, 50, 100, 500, 1000, 1300 s/mm2 ) and a multiecho dynamic contrast-based echo-planer imaging sequence. ASSESSMENT: Median tumor values were obtained from IVIM (perfusion fraction [f], pseudodiffusion [D*], diffusion [D]), from the extended Tofts model applied to DCE data (Ktrans , kep , vp , ve ) and from model free deconvolution of DSC (blood flow [BF] and area under curve). A subgroup of the excised tumors underwent immunohistochemistry with quantification of microvessel density and vessel size. STATISTICAL TEST: Spearman's rank correlation test. RESULTS: D* was correlated with BF (rs = 0.47, P < 0.001), and f was negatively correlated with kep (rs = -0.31, P = 0.002). BF was correlated with Ktrans (rs = 0.29, P = 0.004), but this correlation varied extensively when separating tumors into groups of low (rs = 0.62, P < 0.001) and high (rs = -0.06, P = 0.68) BF. Ktrans was negatively correlated with vessel size (rs = -0.82, P = 0.004) in the subgroup of tumors with high BF. DATA CONCLUSION: We found an association between D* from IVIM and BF estimated from DSC-MRI. The relationship between IVIM and DCE-MRI was less clear. Comparing parameters from DSC-MRI and DCE-MRI highlights the importance of the underlying biology for the interpretation of these parameters. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1114-1124.

Diffusion-weighted magnetic resonance imaging of rectal cancer: tumour volume and perfusion fraction predict chemoradiotherapy response and survival.

BACKGROUND: In locally advanced rectal cancer (LARC), responses to preoperative treatment are highly heterogeneous and more accurate diagnostics are likely to enable more individualised treatment approaches with improved responses. We investigated the potential of diffusion-weighted magnetic resonance imaging (DW MRI), with quantification of the apparent diffusion coefficient (ADC) and perfusion fraction (F), as well as volumetry from T2-weighted (T2W) MRI, for prediction of therapeutic outcome. MATERIAL AND METHODS: In 27 LARC patients receiving neoadjuvant chemotherapy (NACT) before chemoradiotherapy (CRT), T2W- and DW MRI were obtained before and after NACT. Tumour volumes were delineated in T2W MRI and ADCs and Fs were estimated from DW MRI using a simplified approach to the intravoxel incoherent motion (IVIM) model. Mean tumour values and histogram analysis of whole-tumour heterogeneity were correlated with histopathologic tumour regression grade (TRG) and 5-year progression-free survival (PFS). RESULTS: At baseline, high tumour F predicted good tumour response (TRG1-2) (AUC = 0.79, p = 0.01), with a sensitivity of 69% and a specificity of 100%. The combination of F and tumour volume (Fpre/Vpre) gave the highest prediction of poor tumour response (AUC = 0.93, p < 0.001) with a sensitivity of 88% and a specificity of 91%, and also predicted PFS (p < 0.01). Baseline tumour ADC was not significantly related to therapeutic outcome, whereas a positive change in ADC from baseline to after NACT, ΔADC, significantly predicted good tumour response (AUC = 0.83, p < 0.01, 83% sensitivity, 73% specificity), but not PFS. CONCLUSIONS: The MRI parameter F/V at baseline was a remarkably strong predictor of both histopathologic tumour response and 5-year PFS in patients with LARC.