Purpose
To test the performances of native and tumour to liver ratio (TLR) radiomic features extracted from pre-treatment 2-
18
F fluoro-2-deoxy-D-glucose (
18
FFDG) PET/CT and combined with machine ...learning (ML) for predicting cancer recurrence in patients with locally advanced cervical cancer (LACC).
Methods
One hundred fifty-eight patients with LACC from multiple centers were retrospectively included in the study. Tumours were segmented using the Fuzzy Local Adaptive Bayesian (FLAB) algorithm. Radiomic features were extracted from the tumours and from regions drawn over the normal liver. Cox proportional hazard model was used to test statistical significance of clinical and radiomic features. Fivefold cross validation was used to tune the number of features. Seven different feature selection methods and four classifiers were tested. The models with the selected features were trained using bootstrapping and tested in data from each scanner independently. Reproducibility of radiomics features, clinical data added value and effect of ComBat-based harmonisation were evaluated across scanners.
Results
After a median follow-up of 23 months, 29% of the patients recurred. No individual radiomic or clinical features were significantly associated with cancer recurrence. The best model was obtained using 10 TLR features combined with clinical information. The area under the curve (AUC),
F
1
-score, precision and recall were respectively 0.78 (0.67–0.88), 0.49 (0.25–0.67), 0.42 (0.25–0.60) and 0.63 (0.20–0.80). ComBat did not improve the predictive performance of the best models. Both the TLR and the native models performance varied across scanners used in the test set.
Conclusion
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FFDG PET radiomic features combined with ML add relevant information to the standard clinical parameters in terms of LACC patient’s outcome but remain subject to variability across PET/CT devices.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, VSZLJ, ZAGLJ
Radiogenomics in Colorectal Cancer Badic, Bogdan; Tixier, Florent; Cheze Le Rest, Catherine ...
Cancers,
02/2021, Volume:
13, Issue:
5
Journal Article
Peer reviewed
Open access
The steady improvement of high-throughput technologies greatly facilitates the implementation of personalized precision medicine. Characterization of tumor heterogeneity through image-derived ...features-radiomics and genetic profile modifications-genomics, is a rapidly evolving field known as radiogenomics. Various radiogenomics studies have been dedicated to colorectal cancer so far, highlighting the potential of these approaches to enhance clinical decision-making. In this review, a general outline of colorectal radiogenomics literature is provided, discussing the current limitations and suggested further developments.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
To investigate parameters based on fluorine 18 fluorodeoxyglucose (FDG) positron emission tomographic (PET) imaging that are best correlated with pathologic complete response (PCR) in human epidermal ...growth factor receptor type 2 (HER2)-positive cancer and triple-negative breast cancer (TNBC) and with partial or complete response in ER-positive/HER2-negative breast cancer.
This study was approved by institutional review board with waivers of informed written consent and included consecutive patients treated by neoadjuvant chemotherapy. Five PET examination-derived parameters were tested: standard uptake value (SUV) maximum (SUV(max)), peak (SUV(peak)), and mean (SUV(mean)), metabolically active tumor volume, and total lesion glycolysis (TLG). Absolute values at baseline PET, at PET imaging after two cycles of chemotherapy, and variation (ie, change) were measured. Correlations with pathologic response (Wilcoxon rank-sum test) and predictive power assessed (area under the curve AUC on the basis of receiver operating characteristic analysis) were examined.
Included were 169 consecutive patients (mean age, 50 years). PCR was more frequent in HER2-positive tumors (16 of 33 patients 48.5%) and TNBCs (20 of 54 patients 37%) than in ER positive/HER2-negative tumors (four of 82 4.9%) (P < .001). Among patients with ER-positive/HER2-negative cancers, 33 patients had partial response. In TNBC, best association with PCR was obtained with change in SUV(max) (AUC, 0.86) or change in TLG (AUC, 0.88). In HER2-positive phenotype, absolute SUV(max) (or SUV(peak)) values at PET imaging after two cycles of chemotherapy (AUC for each cycle, 0.93) were better correlated with PCR than change in SUV(max) (AUC, 0.78; P = .11) or change in TLG (AUC, 0.62; P = .005). Regarding ER-positive/HER2-negative cancers, change in SUV(max) or change in TLG (AUC, 0.75) were parameters best correlated with partial or complete response. Baseline SUV(max) was higher in lymph nodes than in primary tumor in 31 patients. Findings were similar considering the site with highest FDG uptake.
Quantitative indexes of tumor glucose use that are best correlated with pathologic response vary by phenotype: change in SUV(max) or TLG are most adequate for TNBCs and ER-positive/ HER2-negative cancers and absolute SUV(max) after two cycles of chemotherapy for HER2-positive breast cancers.
Purpose
In this work, we addressed fully automatic determination of tumor functional uptake from positron emission tomography (PET) images without relying on other image modalities or additional ...prior constraints, in the context of multicenter images with heterogeneous characteristics.
Methods
In cervical cancer, an additional challenge is the location of the tumor uptake near or even stuck to the bladder. PET datasets of 232 patients from five institutions were exploited. To avoid unreliable manual delineations, the ground truth was generated with a semi-automated approach: a volume containing the tumor and excluding the bladder was first manually determined, then a well-validated, semi-automated approach relying on the Fuzzy locally Adaptive Bayesian (FLAB) algorithm was applied to generate the ground truth. Our model built on the U-Net architecture incorporates residual blocks with concurrent spatial squeeze and excitation modules, as well as learnable non-linear downsampling and upsampling blocks. Experiments relied on cross-validation (four institutions for training and validation, and the fifth for testing).
Results
The model achieved good Dice similarity coefficient (DSC) with little variability across institutions (0.80 ± 0.03), with higher recall (0.90 ± 0.05) than precision (0.75 ± 0.05) and improved results over the standard U-Net (DSC 0.77 ± 0.05, recall 0.87 ± 0.02, precision 0.74 ± 0.08). Both vastly outperformed a fixed threshold at 40% of SUVmax (DSC 0.33 ± 0.15, recall 0.52 ± 0.17, precision 0.30 ± 0.16). In all cases, the model could determine the tumor uptake without including the bladder. Neither shape priors nor anatomical information was required to achieve efficient training.
Conclusion
The proposed method could facilitate the deployment of a fully automated radiomics pipeline in such a challenging multicenter context.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, VSZLJ, ZAGLJ
Prostatectomy is one of the main therapeutic options for prostate cancer (PCa). Studies proved the benefit of adjuvant radiotherapy (aRT) on clinical outcomes, with more toxicities when compared to ...salvage radiotherapy. A better assessment of the likelihood of biochemical recurrence (BCR) would rationalize performing aRT. Our goal was to assess the prognostic value of MRI-derived radiomics on BCR for PCa with high recurrence risk.
We retrospectively selected patients with a high recurrence risk (T3a/b or T4 and/or R1 and/or Gleason score>7) and excluded patients with a post-operative PSA > 0.04 ng/mL or a lymph-node involvement. We extracted IBSI-compliant radiomic features (shape and first order intensity metrics, as well as second and third order textural features) from tumors delineated in T2 and ADC sequences. After random division (training and testing sets) and machine learning based feature reduction, a univariate and multivariate Cox regression analysis was performed to identify independent factors. The correlation with BCR was assessed using AUC and prediction of biochemical relapse free survival (bRFS) with a Kaplan-Meier analysis.
One hundred seven patients were included. With a median follow-up of 52.0 months, 17 experienced BCR. In the training set, no clinical feature was correlated with BCR. One feature from ADC (SZE
) outperformed with an AUC of 0.79 and a HR 17.9 (
= 0.0001). Lower values of SZE
are associated with more heterogeneous tumors. In the testing set, this feature remained predictive of BCR and bRFS (AUC 0.76,
= 0.0236).
One radiomic feature was predictive of BCR and bRFS after prostatectomy helping to guide post-operative management.
The primary objective of the present study was to identify a subset of radiomic features extracted from primary tumor imaged by computed tomography of early-stage non-small cell lung cancer patients, ...which remain unaffected by variations in segmentation quality and in computed tomography image acquisition protocol. The robustness of these features to segmentation variations was assessed by analyzing the correlation of feature values extracted from lesion volumes delineated by two annotators. The robustness to variations in acquisition protocol was evaluated by examining the correlation of features extracted from high-dose and low-dose computed tomography scans, both of which were acquired for each patient as part of the stereotactic body radiotherapy planning process. Among 106 radiomic features considered, 21 were identified as robust. An analysis including univariate and multivariate assessments was subsequently conducted to estimate the predictive performance of these robust features on the outcome of early-stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. The univariate predictive analysis revealed that robust features demonstrated superior predictive potential compared to non-robust features. The multivariate analysis indicated that linear regression models built with robust features displayed greater generalization capabilities by outperforming other models in predicting the outcomes of an external validation dataset.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Adjuvant radiotherapy after prostatectomy was recently challenged by early salvage radiotherapy, which highlighted the need for biomarkers to improve risk stratification. Therefore, we developed an ...MRI ADC map-derived radiomics model to predict biochemical recurrence (BCR) and BCR-free survival (bRFS) after surgery. Our goal in this work was to externally validate this radiomics-based prediction model.
A total of 195 patients with a high recurrence risk of prostate cancer (pT3-4 and/or R1 and/or Gleason's score > 7) were retrospectively included in two institutions. Patients with postoperative PSA (Prostate Specific Antigen) > 0.04 ng/mL or lymph node involvement were excluded. Radiomics features were extracted from T2 and ADC delineated tumors. A total of 107 patients from Institution 1 were used to retrain the previously published model. The retrained model was then applied to 88 patients from Institution 2 for external validation. BCR predictions were evaluated using AUC (Area Under the Curve), accuracy, and bRFS using Kaplan-Meier curves.
With a median follow-up of 46.3 months, 52/195 patients experienced BCR. In the retraining cohort, the clinical prediction model (combining the number of risk factors and postoperative PSA) demonstrated moderate predictive power (accuracy of 63%). The radiomics model (ADC-based SZE
predicted BCR with an accuracy of 78% and allowed for significant stratification of patients for bRFS (
< 0.0001). In Institution 2, this radiomics model remained predictive of BCR (accuracy of 0.76%) contrary to the clinical model (accuracy of 0.56%).
The recently developed MRI ADC map-based radiomics model was validated in terms of its predictive accuracy of BCR and bRFS after prostatectomy in an external cohort.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Purpose
To develop machine learning models to predict para-aortic lymph node (PALN) involvement in patients with locally advanced cervical cancer (LACC) before chemoradiotherapy (CRT) using
18
F-FDG ...PET/CT and MRI radiomics combined with clinical parameters.
Methods
We retrospectively collected 178 patients (60% for training and 40% for testing) in 2 centers and 61 patients corresponding to 2 further external testing cohorts with LACC between 2010 to 2022 and who had undergone pretreatment analog or digital
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F-FDG PET/CT, pelvic MRI and surgical PALN staging. Only primary tumor volumes were delineated. Radiomics features were extracted using the Radiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Different prediction models were trained using a neural network approach with either clinical, radiomics or combined models. They were then evaluated on the testing and external validation sets and compared.
Results
In the training set (
n
= 102), the clinical model achieved a good prediction of the risk of PALN involvement with a C-statistic of 0.80 (95% CI 0.71, 0.87). However, it performed in the testing (
n
= 76) and external testing sets (
n
= 30 and
n
= 31) with C-statistics of only 0.57 to 0.67 (95% CI 0.36, 0.83). The ComBat-radiomic (GLDZM_HISDE_PET_FBN64 and Shape_maxDiameter2D3_PET_FBW0.25) and ComBat-combined (FIGO 2018 and same radiomics features) models achieved very high predictive ability in the training set and both models kept the same performance in the testing sets, with C-statistics from 0.88 to 0.96 (95% CI 0.76, 1.00) and 0.85 to 0.92 (95% CI 0.75, 0.99), respectively.
Conclusions
Radiomic features extracted from pre-CRT analog and digital
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F-FDG PET/CT outperform clinical parameters in the decision to perform a para-aortic node staging or an extended field irradiation to PALN. Prospective validation of our models should now be carried out.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, VSZLJ, ZAGLJ
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