Sex Differences and Tumor Blood Flow from Dynamic Susceptibility Contrast MRI Are Associated with Treatment Response after Chemoradiation and Long-term Survival in Rectal Cancer

Photon Counting Computed Tomography in Rectal Cancer: Associations Between Iodine Concentration, Histopathology and Treatment Response: A Pilot Study

RATIONALE AND OBJECTIVES

Common computed tomography (CT) investigation plays a limited role in characterizing and assessing the response of rectal cancer (RC) to neoadjuvant radiochemotherapy (NARC). Photon counting computed tomography (PCCT) improves the imaging quality and can provide multiparametric spectral image information including iodine concentration (IC). Our purpose was to analyze associations between IC and histopathology in RC and to evaluate the role of IC in response prediction to NARC.

MATERIALS AND METHODS

Overall, 41 patients were included into the study, 14 women and 27 men, mean age, 65.5 years. PCCT in a portal venous phase of the abdomen was performed. In every case, a polygonal region of interest (ROI) was manually drawn on iodine maps. Normalized IC (NIC) was also calculated. Tumor stage, grade, lymphovascular invasion, circumferential resection margin, and tumor markers were analyzed. Tumor regression grade (absence/presence of tumor cells) after NARC was analyzed. NIC values in groups were compared to Mann-Whitney-U tests. Sensitivity, specificity, and area under the curve values were calculated. Intraclass correlation coefficient (ICC) was calculated.

RESULTS

ICC was 0.93, 95%CI= (0.88; 0.96). Tumors with lymphovascular invasion showed higher NIC values in comparison to those without (p = 0.04). Tumors with response grade 2-4 showed higher pretreatment NIC values in comparison to lesions with response grade 0-1 (p = 0.01). A NIC value of 0.36 and higher can predict response grade 2-4 (sensitivity, 73.9%; specificity, 91.7%; area under the curve, 0.85).

CONCLUSION

NIC values showed an excellent interreader agreement in RC. NIC can predict treatment response to NARC.

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.

Disparities in the Demographic Composition of The Cancer Imaging Archive

Purpose To characterize the demographic distribution of The Cancer Imaging Archive (TCIA) studies and compare them with those of the U.S. cancer population. Materials and Methods In this retrospective study, data from TCIA studies were examined for the inclusion of demographic information. Of 189 studies in TCIA up until April 2023, a total of 83 human cancer studies were found to contain supporting demographic data. The median patient age and the sex, race, and ethnicity proportions of each study were calculated and compared with those of the U.S. cancer population, provided by the Surveillance, Epidemiology, and End Results Program and the Centers for Disease Control and Prevention U.S. Cancer Statistics Data Visualizations Tool. Results The median age of TCIA patients was found to be 6.84 years lower than that of the U.S. cancer population (P = .047) and contained more female than male patients (53% vs 47%). American Indian and Alaska Native, Black or African American, and Hispanic patients were underrepresented in TCIA studies by 47.7%, 35.8%, and 14.7%, respectively, compared with the U.S. cancer population. Conclusion The results demonstrate that the patient demographics of TCIA data sets do not reflect those of the U.S. cancer population, which may decrease the generalizability of artificial intelligence radiology tools developed using these imaging data sets. Keywords: Ethics, Meta-Analysis, Health Disparities, Cancer Health Disparities, Machine Learning, Artificial Intelligence, Race, Ethnicity, Sex, Age, Bias Published under a CC BY 4.0 license.

Multiphase and multiparameter MRI-based radiomics for prediction of tumor response to neoadjuvant therapy in locally advanced rectal cancer

BACKGROUND

To develop and validate radiomics models for prediction of tumor response to neoadjuvant therapy (NAT) in patients with locally advanced rectal cancer (LARC) using both pre-NAT and post-NAT multiparameter magnetic resonance imaging (mpMRI).

METHODS

In this multicenter study, a total of 563 patients were included from two independent centers. 453 patients from center 1 were split into training and testing cohorts, the remaining 110 from center 2 served as an external validation cohort. Pre-NAT and post-NAT mpMRI was collected for feature extraction. The radiomics models were constructed using machine learning from a training cohort. The accuracy of the models was verified in a testing cohort and an independent external validation cohort. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value.

RESULTS

The model constructed with pre-NAT mpMRI had favorable accuracy for prediction of non-response to NAT in the training cohort (AUC = 0.84), testing cohort (AUC = 0.81), and external validation cohort (AUC = 0.79). The model constructed with both pre-NAT and post-NAT mpMRI had powerful diagnostic value for pathologic complete response in the training cohort (AUC = 0.86), testing cohort (AUC = 0.87), and external validation cohort (AUC = 0.87).

CONCLUSIONS

Models constructed with multiphase and multiparameter MRI were able to predict tumor response to NAT with high accuracy and robustness, which may assist in individualized management of LARC.

Prognostic value of multi b-value DWI in patients with locally advanced rectal cancer

OBJECTIVES

To evaluate the potential of multi b-value DWI in predicting the prognosis of patients with locally advanced rectal cancer (LARC).

METHODS

From 2015 to 2019, a total of 161 patients with LARC were enrolled and randomly sampled into a training set (n = 113) and validation set (n = 48). Multi b-value DWI (b = 0~1500 s/mm²) scans were postprocessed to generate functional parameters, including apparent diffusion coefficient (ADC), Dt, Dp, f, distributed diffusion coefficient (DDC), and α. Histogram features of each functional parameter were submitted into Least absolute shrinkage and selection operator (LASSO) and stepwise multivariate COX analysis to generate DWI_score based on the training set. The prognostic model was constructed with functional parameter, DWI_score, and clinicopathologic factors by using univariate and multivariate COX analysis on the training set and verified on the validation set.

RESULTS

Multivariate COX analysis revealed that DWI_score was an independent indicator for 5-year progression-free survival (PFS, HR = 5.573, p < 0.001), but not for overall survival (OS, HR = 2.177, p = 0.051). No mean value of functional parameters was correlated with PFS or OS. Prognostic model for 5-year PFS based on DWI_score, TNM-stage, mesorectal fascia (MRF), and extramural venous invasion (EMVI) showed good performance both in the training set (AUC = 0.819) and validation set (AUC = 0.815).

CONCLUSIONS

The DWI_score based on histogram features of multi b-value DWI functional parameters was an independent factor for PFS of LARC and the prognostic model with a combination of DWI_score and clinicopathologic factors could indicate the progression risk before treatment.

KEY POINTS

• Mean value of functional parameters obtained from multi b-value DWI might not be useful to assess the prognosis of LARC. • The DWI_score based on histogram features of multi b-value DWI functional parameters was an independent prognosis factor for PFS of LARC. • Prognostic model based on DWI_score and clinicopathologic factors could indicate the progression risk of LARC before treatment.

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.

Dynamic Contrast-enhanced Magnetic Resonance Imaging Evaluation of Whole Tumour Perfusion Heterogeneity Predicts Distant Disease-free Survival in Locally Advanced Rectal Cancer

AIMS

To evaluate diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging for the prediction of disease-free survival (DFS) in patients with locally advanced rectal cancer.

MATERIALS AND METHODS

Patients with stage II or III rectal adenocarcinoma undergoing neoadjuvant chemoradiotherapy (CRT) and surgery were eligible. Patients underwent multi-parametric magnetic resonance imaging (diffusion-weighted imaging and dynamic contrast-enhanced) before CRT, during CRT (week 3) and after CRT (1 week prior to surgery). Whole tumour apparent diffusion coefficient (ADC) and K^trans histogram quantiles (10th, 25th, 50th, 75th, 90th) were extracted for analysis. The associations between ADC and K^trans at three timepoints with time to relapse were analysed as a continuous variable using a Cox proportional hazard model.

RESULTS

Thirty-three patients were included in this analysis. The median follow-up was 4.4 years. No patient had locoregional relapse. Nine patients developed distant metastases. The hazard ratios for after CRT K^trans 10th (P = 0.035), 25th (P = 0.048), 50th (P = 0.046) and 75th (P = 0.045) quantiles were statistically significant for DFS. The best K^trans cut-off point after CRT for predicting relapse was 28 × 10^-3 mL/g/min (10th quantile), with a higher K^trans value predicting distant relapse. The 4-year DFS probability was 0.93 for patients with after CRT K^trans value ≤28 × 10^-3 mL/g/min versus 0.45 for patients with after CRT K^trans value >28 × 10^-3 mL/g/min. ADC was not able to predict DFS.

CONCLUSIONS

Patients with higher K^trans values after CRT (before surgery) in a histogram analysis of whole tumour heterogeneity had a significantly lower 4-year distant DFS and could be considered for more intense systemic therapy.

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

•

Machine learning on magnetic resonance images (MRI) was used for tumor segmentation.

•

Voxelwise machine learning with morphological post-processing achieved good segmentation results.

•

Combining T2-weighted with functional MRI improved semi-automatic tumor segmentation.

•

Dynamic contrast enhanced MRI was the most valuable functional MRI information.

•

Tumor volume and interobserver variation were linked to measured segmentation quality.

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.

MRI-based automatic segmentation of rectal cancer using 2D U-Net on two independent cohorts

BACKGROUND

Tumor delineation is time- and labor-intensive and prone to inter- and intraobserver variations. Magnetic resonance imaging (MRI) provides good soft tissue contrast, and functional MRI captures tissue properties that may be valuable for tumor delineation. We explored MRI-based automatic segmentation of rectal cancer using a deep learning (DL) approach. We first investigated potential improvements when including both anatomical T2-weighted (T2w) MRI and diffusion-weighted MR images (DWI). Secondly, we investigated generalizability by including a second, independent cohort.

MATERIAL AND METHODS

Two cohorts of rectal cancer patients (C1 and C2) from different hospitals with 109 and 83 patients, respectively, were subject to 1.5 T MRI at baseline. T2w images were acquired for both cohorts and DWI (b-value of 500 s/mm²) for patients in C1. Tumors were manually delineated by three radiologists (two in C1, one in C2). A 2D U-Net was trained on T2w and T2w + DWI. Optimal parameters for image pre-processing and training were identified on C1 using five-fold cross-validation and patient Dice similarity coefficient (DSC_p) as performance measure. The optimized models were evaluated on a C1 hold-out test set and the generalizability was investigated using C2.

RESULTS

For cohort C1, the T2w model resulted in a median DSC_p of 0.77 on the test set. Inclusion of DWI did not further improve the performance (DSC_p 0.76). The T2w-based model trained on C1 and applied to C2 achieved a DSC_p of 0.59.

CONCLUSION

T2w MR-based DL models demonstrated high performance for automatic tumor segmentation, at the same level as published data on interobserver variation. DWI did not improve results further. Using DL models on unseen cohorts requires caution, and one cannot expect the same performance.

A Nomogram of Combining IVIM-DWI and MRI Radiomics From the Primary Lesion of Rectal Adenocarcinoma to Assess Nonenlarged Lymph Node Metastasis Preoperatively

BACKGROUND

Lymph node (LN) staging plays an important role in treatment decision-making. Current problem is that preoperative detection of LN involvement is always highly challenging for radiologists.

PURPOSE

To explore the value of the nomogram model combining intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and radiomics features from the primary lesion of rectal adenocarcinoma in assessing the non-enlarged lymph node metastasis (N-LNM) preoperatively.

STUDY TYPE

Retrospective.

POPULATION

A total of 126 patients (43% female) comprising a training group (n = 87) and a validation group (n = 39) with pathologically confirmed rectal adenocarcinoma.

FIELD STRENGTH/SEQUENCE

A 3.0 Tesla (T); T₂ -weighted imaging (T₂ WI) with fast spin-echo (FSE) sequence; IVIM-DWI spin-echo echo-planar imaging sequence.

ASSESSMENT

Based on pathological analysis of the surgical specimen, patients were classified into negative LN (LN-) and positive LN (LN+) groups. Apparent diffusion coefficient (ADC), diffusion coefficient (D), pseudo diffusion coefficient (D*) and microvascular volume fraction (f) values of primary lesion of rectal adenocarcinoma were measured. Three-dimensional (3D) radiomics features were measured on T₂ WI and IVIM-DWI. A nomogram model including IVIM-DWI and radiomics features was developed.

STATISTICAL TESTS

General_univariate_analysis and multivariate logistic regression were used for radiomics features selection. The performance of the nomogram was assessed by the receiver operating characteristic (ROC) curve, calibration, and decision curve analysis (DCA).

RESULTS

The LN+ group had a significantly lower D* value ([13.20 ± 13.66 vs. 23.25 ± 18.71] × 10^-3  mm² /sec) and a higher f value (0.43 ± 0.12 vs. 0.34 ± 0.10) than the LN- group in the training cohort. The nomogram model combined D*, f, and radiomics features had a better evaluated performance (AUC = 0.864) than any other model in the training cohort.

DATE CONCLUSION

The nomogram model including IVIM-DWI and MRI radiomics features in the primary lesion of rectal adenocarcinoma was associated with the N-LNM.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Sex disparities in vitamin D status and the impact on systemic inflammation and survival in rectal cancer

BACKGROUND

We reported previously that rectal cancer patients given curative-intent chemotherapy, radiation, and surgery for non-metastatic disease had enhanced risk of metastatic progression and death if circulating levels of 25-hydroxyvitamin D [25(OH) D] were low. Here we investigated whether the association between the vitamin D status and prognosis pertains to the general, unselected population of rectal cancer patients.

METHODS

Serum 25(OH) D at the time of diagnosis was assessed in 129 patients, enrolled 2013-2017 and representing the entire range of rectal cancer stages, and analyzed with respect to season, sex, systemic inflammation, and survival.

RESULTS

In the population-based cohort residing at latitude 60°N, 25(OH) D varied according to season in men only, who were overrepresented among the vitamin D-deficient (< 50 nmol/L) patients. Consistent with our previous findings, the individuals presenting with T4 disease had significantly reduced 25(OH) D levels. Low vitamin D was associated with systemic inflammation, albeit with distinct modes of presentation. While men with low vitamin D showed circulating markers typical for the systemic inflammatory response (e.g., elevated erythrocyte sedimentation rate), the corresponding female patients had elevated serum levels of interleukin-6 and the chemokine (C-X-C motif) ligand 7. Despite disparities in vitamin D status and the potential effects on disease attributes, significantly shortened cancer-specific survival was observed in vitamin D-deficient patients irrespective of sex.

CONCLUSION

This unselected rectal cancer cohort confirmed the interconnection of low vitamin D, more advanced disease presentation, and poor survival, and further suggested it may be conditional on disparate modes of adverse systemic inflammation in men and women.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01816607 ; registration date: 22 March 2013.