Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy

ObjectivesBreast cancers show different regression patterns after neoadjuvant chemotherapy. Certain regression patterns are associated with more reliable margins in breast-conserving surgery. Our study aims to establish a nomogram based on radiomic features and clinicopathological factors to predict regression patterns in breast cancer patients.MethodsWe retrospectively reviewed 144 breast cancer patients who received neoadjuvant chemotherapy and underwent definitive surgery in our center from January 2016 to December 2019. Tumor regression patterns were categorized as type 1 (concentric regression + pCR) and type 2 (multifocal residues + SD + PD) based on pathological results. We extracted 1158 multidimensional features from 2 sequences of MRI images. After feature selection, machine learning was applied to construct a radiomic signature. Clinical characteristics were selected by backward stepwise selection. The combined prediction model was built based on both the radiomic signature and clinical factors. The predictive performance of the combined prediction model was evaluated.ResultsTwo radiomic features were selected for constructing the radiomic signature. Combined with two significant clinical characteristics, the combined prediction model showed excellent prediction performance, with an area under the receiver operating characteristic curve of 0.902 (95% confidence interval 0.8343–0.9701) in the primary cohort and 0.826 (95% confidence interval 0.6774–0.9753) in the validation cohort.ConclusionsOur study established a unique model combining a radiomic signature and clinicopathological factors to predict tumor regression patterns prior to the initiation of NAC. The early prediction of type 2 regression offers the opportunity to modify preoperative treatments or aids in determining surgical options.     

Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer

PurposeRadiomic models have been demonstrated to have acceptable discrimination capability for detecting lymph node metastasis (LNM). We aimed to develop a computed tomography–based radiomic model and validate its usefulness in the prediction of normal-sized LNM at node level in cervical cancer.MethodsA total of 273 LNs of 219 patients from 10 centers were evaluated in this study. We randomly divided the LNs from the 2 centers with the largest number of LNs into the training and internal validation cohorts, and the rest as the external validation cohort. Radiomic features were extracted from the arterial and venous phase images. We trained an artificial neural network (ANN) to develop two single-phase models. A radiomic model reflecting the features of two-phase images was also built for directly predicting LNM in cervical cancer. Moreover, four state-of-the-art methods were used for comparison. The performance of all models was assessed using the area under the receiver operating characteristic curve (AUC).ResultsAmong the models we built, the models combining the features of two phases surpassed the single-phase models, and the models generated by ANN had better performance than the others. We found that the radiomic model achieved the highest AUCs of 0.912 and 0.859 in the training and internal validation cohorts, respectively. In the external validation cohort, the AUC of the radiomic model was 0.800.ConclusionWe constructed a radiomic model that exhibited great ability in the prediction of LNM. The application of the model could optimize clinical staging and decision-making.     

Early risk-assessment of patients with nasopharyngeal carcinoma: the added prognostic value of MR-based radiomics

ObjectivesTo assess the additive prognostic value of MR-based radiomics in predicting progression-free survival (PFS) in patients with nasopharyngeal carcinoma (NPC)MethodsPatients newly diagnosed with non-metastatic NPC between June 2006 and October 2019 were retrospectively included and randomly grouped into training and test cohorts (7:3 ratio). Radiomic features (n=213) were extracted from T2-weighted and contrast-enhanced T1-weighted MRI. The patients were staged according to the 8^th edition of American Joint Committee on Cancer Staging Manual. The least absolute shrinkage and selection operator was used to select the relevant radiomic features. Univariate and multivariate Cox proportional hazards analyses were conducted for PFS, yielding three different survival models (clinical, stage, and radiomic). The integrated time-dependent area under the curve (iAUC) for PFS was calculated and compared among different combinations of survival models, and the analysis of variance was used to compare the survival models. The prognostic performance of all models was validated using a test set with integrated Brier scores.ResultsThis study included 81 patients (training cohort=57; test cohort=24), and the mean PFS was 57.5 ± 43.6 months. In the training cohort, the prognostic performances of survival models improved significantly with the addition of radiomics to the clinical (iAUC, 0.72–0.80; p=0.04), stage (iAUC, 0.70–0.79; p=0.001), and combined models (iAUC, 0.76–0.81; p<0.001). In the test cohort, the radiomics and combined survival models were robustly validated for their ability to predict PFS.ConclusionIntegration of MR-based radiomic features with clinical and stage variables improved the prediction PFS in patients diagnosed with NPC.     

Pretreatment CT and PET radiomics predicting rectal cancer patients in response to neoadjuvant chemoradiotherapy

BackgroundThe purpose of this study was to characterize pre-treatment non-contrast computed tomography (CT) and ¹⁸F-fluorodeoxyglucose positron emission tomography (PET) based radiomics signatures predictive of pathological response and clinical outcomes in rectal cancer patients treated with neoadjuvant chemoradiotherapy (NACR T).Materials and methodsAn exploratory analysis was performed using pre-treatment non-contrast CT and PET imaging dataset. The association of tumor regression grade (TRG) and neoadjuvant rectal (NAR) score with pre-treatment CT and PET features was assessed using machine learning algorithms. Three separate predictive models were built for composite features from CT + PET.ResultsThe patterns of pathological response were TRG 0 (n = 13; 19.7%), 1 (n = 34; 51.5%), 2 (n = 16; 24.2%), and 3 (n = 3; 4.5%). There were 20 (30.3%) patients with low, 22 (33.3%) with intermediate and 24 (36.4%) with high NAR scores. Three separate predictive models were built for composite features from CT + PET and analyzed separately for clinical endpoints. Composite features with α = 0.2 resulted in the best predictive power using logistic regression. For pathological response prediction, the signature resulted in 88.1% accuracy in predicting TRG 0 vs. TRG 1–3; 91% accuracy in predicting TRG 0–1 vs. TRG 2–3. For the surrogate of DFS and OS, it resulted in 67.7% accuracy in predicting low vs. intermediate vs. high NAR scores.ConclusionThe pre-treatment composite radiomics signatures were highly predictive of pathological response in rectal cancer treated with NACR T. A larger cohort is warranted for further validation.     

Diagnosis of Distant Metastasis of Lung Cancer: Based on Clinical and Radiomic Features

OBJECTIVES: To analyze the distant metastasis possibility based on computed tomography (CT) radiomic features in patients with lung cancer. METHODS: This was a retrospective analysis of 348 patients with lung cancer enrolled between 2014 and February 2015. A feature set containing clinical features and 485 radiomic features was extracted from the pretherapy CT images. Feature selection via concave minimization (FSV) was used to select effective features. A support vector machine (SVM) was used to evaluate the predictive ability of each feature. RESULTS: Four radiomic features and three clinical features were obtained by FSV feature selection. Classification accuracy by the proposed SVM with SGD method was 71.02%, and the area under the curve was 72.84% with only the radiomic features extracted from CT. After the addition of clinical features, 89.09% can be achieved. CONCLUSION: The radiomic features of the pretherapy CT images may be used as predictors of distant metastasis. And it also can be used in combination with the patient's gender and tumor T and N phase information to diagnose the possibility of distant metastasis in lung cancer.     

Prediction of effect of pegylated interferon alpha-2b plus ribavirin combination therapy in patients with chronic hepatitis C infection

Treatment with pegylated interferon alpha-2b (PEGIFN) plus ribavirin (RBV) is standard therapy for patients with chronic hepatitis C. Although the effectiveness, patients with high titres of group Ib hepatitis C virus (HCV) respond poorly compared to other genotypes. At present, we cannot predict the effect in an individual. Previous studies have used traditional statistical analysis by assuming a linear relationship between clinical features, but most phenomena in the clinical situation are not linearly related. The aim of this study is to predict the effect of PEG IFN plus RBV therapy on an individual patient level using an artificial neural network system (ANN). 156 patients with HCV group 1b from multiple centres were treated with PEGIFN (1.5 μg/kg) plus RBV (400-1000 mg) for 48 weeks. Data on the patients' demographics, laboratory tests, PEGIFN, and RBV doses, early viral responses (EVR), and sustained viral responses were collected. Clinical data were randomly divided into training data set and validation data set and analyzed using multiple logistic regression analysis (MLRs) and ANN to predict individual outcomes. The sensitivities of predictive expression were 0.45 for the MLRs models and 0.82 for the ANNs and specificities were 0.55 for the MLR and 0.88 for the ANN. Non-linear relation analysis showed that EVR, serum creatinine, initial dose of Ribavirin, gender and age were important predictive factors, suggesting non-linearly related to outcome. In conclusion, ANN was more accurate than MLRs in predicting the outcome of PEGIFN plus RBV therapy in patients with group 1b HCV.     

Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer

OBJECTIVES: To predict epidermal growth factor receptor (EGFR) mutation status using quantitative radiomic biomarkers and representative clinical variables. METHODS: The study included 180 patients diagnosed as of non-small cell lung cancer (NSCLC) with their pre-therapy computed tomography (CT) scans. Using a radiomic method, 485 features that reflect the heterogeneity and phenotype of tumors were extracted. Afterwards, these radiomic features were used for predicting epidermal growth factor receptor (EGFR) mutation status by a least absolute shrinkage and selection operator (LASSO) based on multivariable logistic regression. As a result, we found that radiomic features have prognostic ability in EGFR mutation status prediction. In addition, we used radiomic nomogram and calibration curve to test the performance of the model. RESULTS: Multivariate analysis revealed that the radiomic features had the potential to build a prediction model for EGFR mutation. The area under the receiver operating characteristic curve (AUC) for the training cohort was 0.8618, and the AUC for the validation cohort was 0.8725, which were superior to prediction model that used clinical variables alone. CONCLUSION: Radiomic features are better predictors of EGFR mutation status than conventional semantic CT image features or clinical variables to help doctors to decide who need EGFR tyrosine kinase inhibitor (TKI) treatment.     

Deep radiomic model based on the sphere–shell partition for predicting treatment response to chemotherapy in lung cancer

BackgroundThe prognosis of chemotherapy is important in clinical decision-making for non-small cell lung cancer (NSCLC) patients.ObjectivesTo develop a model for predicting treatment response to chemotherapy in NSCLC patients from pre-chemotherapy CT images.Materials and MethodsThis retrospective multicenter study enrolled 485 patients with NSCLC who received chemotherapy alone as a first-line treatment. Two integrated models were developed using radiomic and deep-learning-based features. First, we partitioned pre-chemotherapy CT images into spheres and shells with different radii around the tumor (0–3, 3–6, 6–9, 9–12, 12–15 mm) containing intratumoral and peritumoral regions. Second, we extracted radiomic and deep-learning-based features from each partition. Third, using radiomic features, five sphere–shell models, one feature fusion model, and one image fusion model were developed. Finally, the model with the best performance was validated in two cohorts.ResultsAmong the five partitions, the model of 9–12 mm achieved the highest area under the curve (AUC) of 0.87 (95% confidence interval: 0.77–0.94). The AUC was 0.94 (0.85–0.98) for the feature fusion model and 0.91 (0.82–0.97) for the image fusion model. For the model integrating radiomic and deep-learning-based features, the AUC was 0.96 (0.88–0.99) for the feature fusion method and 0.94 (0.85–0.98) for the image fusion method. The best-performing model had an AUC of 0.91 (0.81–0.97) and 0.89 (0.79–0.93) in two validation sets, respectively.ConclusionsThis integrated model can predict the response to chemotherapy in NSCLC patients and assist physicians in clinical decision-making.     

Investigating the impact of the CT Hounsfield unit range on radiomic feature stability using dual energy CT data

PurposeRadiomic texture calculation requires discretizing image intensities within the region-of-interest. FBN (fixed-bin-number), FBS (fixed-bin-size) and FBN and FBS with intensity equalization (FBNequal, FBSequal) are four discretization approaches. A crucial choice is the voxel intensity (Hounsfield units, or HU) binning range. We assessed the effect of this choice on radiomic features.MethodsThe dataset comprised 95 patients with head-and-neck squamous-cell-carcinoma. Dual energy CT data was reconstructed at 21 electron energies (40, 45,… 140 keV). Each of 94 texture features were calculated with 64 extraction parameters. All features were calculated five times: original choice, left shift (-10/-20 HU), right shift (+10/+20 HU). For each feature, Spearman correlation between nominal and four variants were calculated to determine feature stability. This was done for six texture feature types (GLCM, GLRLM, GLSZM, GLDZM, NGTDM, and NGLDM) separately. This analysis was repeated for the four binning algorithms. Effect of feature instability on predictive ability was studied for lymphadenopathy as endpoint.ResultsFBN and FBNequal algorithms showed good stability (correlation values consistently $>$0.9). For FBS and FBSequal algorithms, while median values exceeded 0.9, the 95% lower bound decreased as a function of energy, with poor performance over the entire spectrum. FBNequal was the most stable algorithm, and FBS the least.ConclusionsWe believe this is the first multi-energy systematic study of the impact of CT HU range used during intensity discretization for radiomic feature extraction. Future analyses should account for this source of uncertainty when evaluating the robustness of their radiomic signature.     

Impact of deformable registration methods for prediction of recurrence free survival response to neoadjuvant chemotherapy in breast cancer: Results from the ISPY 1/ACRIN 6657 trial

PurposeImage registration plays a vital role in spatially aligning multiple MRI scans for better longitudinal assessment of tumor morphological features. The objective was to evaluate the effect of registration accuracy of six established deformable registration methods(ANTs, DRAMMS, ART, NiftyReg, SSD-FFD, and NMI-FFD) on the predictive value of extracted radiomic features when modeling recurrence-free-survival(RFS) for women after neoadjuvant chemotherapy(NAC) for locally advanced breast cancer.Methods130 women had DCE-MRI scans available from the first two visits in the ISPY1/ACRIN-6657 cohort. We calculated the transformation field from each of the different deformable registration methods, and used it to compute voxel-wise parametric-response-maps(PRM) for established four kinetic features.104-radiomic features were computed from each PRM map to characterize intra-tumor heterogeneity. We evaluated performance for RFS using Cox-regression, C-statistic, and Kaplan-Meier(KM) plots.ResultsA baseline model(F1:Age, Race, and Hormone-receptor-status) had a 0.54 C-statistic, and model F2(baseline + functional-tumor-volume at early treatment visit(FTV₂)) had 0.63. The F2+ANTs had the highest C-statistic(0.72) with the smallest landmark differences(5.40±4.40mm) as compared to other models. The KM curve for model F2 gave p=0.004 for separation between women above and below the median hazard compared to the model F1(p=0.31). A models augmented with radiomic features, also achieved significant KM curve separation(p<0.001) except the F2+ART model.ConclusionIncorporating image registration in quantifying changes in tumor heterogeneity during NAC can improve prediction of RFS. Radiomic features of PRM maps derived from warping the DCE-MRI kinetic maps using ANTs registration method further improved the early prediction of RFS as compared to other methods.     

Quantifying the robustness of [18F]FDG-PET/CT radiomic features with respect to tumor delineation in head and neck and pancreatic cancer patients

PurposeTo investigate the robustness of PET radiomic features (RF) against tumour delineation uncertainty in two clinically relevant situations.MethodsTwenty-five head-and-neck (HN) and 25 pancreatic cancer patients previously treated with ¹⁸F-Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT)-based planning optimization were considered. Seven FDG-based contours were delineated for tumour (T) and positive lymph nodes (N, for HN patients only) following manual (2 observers), semi-automatic (based on SUV maximum gradient: PET_Edge) and automatic (40%, 50%, 60%, 70% SUV_max thresholds) methods. Seventy-three RF (14 of first order and 59 of higher order) were extracted using the CGITA software (v.1.4). The impact of delineation on volume agreement and RF was assessed by DICE and Intra-class Correlation Coefficients (ICC).ResultsA large disagreement between manual and SUV_max method was found for thresholds ≥50%. Inter-observer variability showed median DICE values between 0.81 (HN-T) and 0.73 (pancreas). Volumes defined by PET_Edge were better consistent with the manual ones compared to SUV40%. Regarding RF, 19%/19%/47% of the features showed ICC < 0.80 between observers for HN-N/HN-T/pancreas, mostly in the Voxel-alignment matrix and in the intensity-size zone matrix families. RFs with ICC < 0.80 against manual delineation (taking the worst value) increased to 44%/36%/61% for PET_Edge and to 69%/53%/75% for SUV40%.ConclusionsAbout 80%/50% of 72 RF were consistent between observers for HN/pancreas patients. PET_edge was sufficiently robust against manual delineation while SUV40% showed a worse performance. This result suggests the possibility to replace manual with semi-automatic delineation of HN and pancreas tumours in studies including PET radiomic analyses.     

Comparison of Regression Methods for Modeling Intensive Care Length of Stay

Intensive care units (ICUs) are increasingly interested in assessing and improving their performance. ICU Length of Stay (LoS) could be seen as an indicator for efficiency of care. However, little consensus exists on which prognostic method should be used to adjust ICU LoS for case-mix factors. This study compared the performance of different regression models when predicting ICU LoS. We included data from 32,667 unplanned ICU admissions to ICUs participating in the Dutch National Intensive Care Evaluation (NICE) in the year 2011. We predicted ICU LoS using eight regression models: ordinary least squares regression on untransformed ICU LoS,LoS truncated at 30 days and log-transformed LoS; a generalized linear model with a Gaussian distribution and a logarithmic link function; Poisson regression; negative binomial regression; Gamma regression with a logarithmic link function; and the original and recalibrated APACHE IV model, for all patients together and for survivors and non-survivors separately. We assessed the predictive performance of the models using bootstrapping and the squared Pearson correlation coefficient (R²), root mean squared prediction error (RMSPE), mean absolute prediction error (MAPE) and bias. The distribution of ICU LoS was skewed to the right with a median of 1.7 days (interquartile range 0.8 to 4.0) and a mean of 4.2 days (standard deviation 7.9). The predictive performance of the models was between 0.09 and 0.20 for R², between 7.28 and 8.74 days for RMSPE, between 3.00 and 4.42 days for MAPE and between −2.99 and 1.64 days for bias. The predictive performance was slightly better for survivors than for non-survivors. We were disappointed in the predictive performance of the regression models and conclude that it is difficult to predict LoS of unplanned ICU admissions using patient characteristics at admission time only.     

TEP/TDM au fluorure (18F) de sodium pour la détection des métastases osseuses du cancer de la prostate. Description de l’étude Fluprostic de comparaison de la TEP/TDM au fluorure (18F) de sodium à l’IRM corps entier dans cette indication

Fluorodeoxyglucose (¹⁸F) or FDG, the radioactive glucose analogue which is the reference radiopharmaceutical in oncologic PET, is not well suited for the detection of prostate cancer metastases the glucose metabolism of which is usually only slightly enhanced. Fluoride (¹⁸F) accumulates into the cortical bone, rapidly and intensely in reaction to a bony metastasis. In 2008, it has been granted a marketing authorisation in France, including imaging bone metastasis of prostate cancer. We report original clinical cases to illustrate its diagnostic performance. Whole-body MRI is developing and can also detect bone metastases. Recently diffusion-weighted MRI (DWI) has been proposed to increase the detection rate of metastases of the axial skeleton, which are largely predominant in prostate cancer. Using either hybrid PET/CT or MRI requires mobilising equipments, which are less available and more expensive than the gamma-cameras for classical bone scintigraphy, in the aim to achieve superior diagnostic performance. A clinical study protocol (STIC) has just been accepted for public funding. It aims to assess the impact on patient management of the discovery of the first macroscopic bony metastasis and the efficacy of diagnostic strategies including those innovations, individually and in association. In case of prostate cancer with a high risk of metastasis, but without any proven bone metastasis and no typical pattern on bone scintigraphy, fluoride (¹⁸F) PET/CT will be performed as well as whole-body MRI. Histopathology and/or data of a 6-month follow-up will be the standard of truth to evaluate the adequacy of impact on patient management and the benefit / cost ratio of those examinations. With this prospective national study, we hope to demonstrate in the real world a clinical role for this radiopharmaceutical, which was proposed several decades ago, but benefits from a renewed interest thanks to the development of PET/CT imaging.     

Value of 18FDG PET for prediction of therapeutic response in HER2+ breast cancer: Interim analysis

¹⁸F-FDG PET is recommended for the initial staging of locally advanced breast cancer. Studies have shown the prognostic value of ¹⁸F-FDG PET for staging, but its ability to predict pathological complete response remains uncertain. Our objective was to determine the predictive value of early therapeutic evaluation by ¹⁸F-FDG PET for HER2-positive breast cancer patients. We studied a subpopulation of the prospective and multicentric French NeoTOP trial, at interim analysis. All patients were eligible for neoadjuvant chemotherapy. A ¹⁸F-FDG PET was performed at baseline and then after one cycle of neoadjuvant treatment. ¹⁸F-FDG PET were studied by three conventional methods (two visuals and one quantitative) and by textural analysis. Complete pathological response on surgical samples corresponded to grades 1/2 of Chevallier's classification and no responders corresponded to grades 3/4 of Chevallier. Pathological results were available for 21 patients. SUV_max decreased by 55% after one cycle of chemotherapy. No difference between groups was found with visuals, conventional quantitative and textural analysis of tumour uptake evaluations. Early therapeutic evaluation by ¹⁸F-FDG PET for HER2-positive breast cancer was not predictive of pathological therapeutic response after neoadjuvant treatment, with conventional study or textural analyses in this interim analysis.     

Investigating the contribution of pre- and per-treatment 18F-FDG PET-CT segmentation methodologies for post-treatment tumor recurrence prediction in cervical cancer

Cervical cancer is one of the most common cancers to affect women worldwide. Despite the efficiency of radiotherapy treatment, some patients present post-treatment tumor recurrence, which increases the risk of death. Several studies suggest that tumor characteristics visible with PET imaging before and during the treatment could be used to predict post-treatment recurrence. We evaluate the contribution of pre- and per-treatment ¹⁸F-FDG PET images by exploring the predictive value of features extracted through several segmentation methods. Forty-one patients with locally advanced cervix cancer treated by chemoradiotherapy were considered. For each patient, two coregistered PET/CT scan were acquired before and during the treatment. A non-rigid registration was used to match the two PET acquisitions and evaluate the tumor metabolism inside the same area. Maximum and peak standardized uptake value (SUVmax and SUVpeak), the metabolic tumor volume (MTV) and the total lesion glycolysis (TLG) were evaluated. The predictive value of the extracted features was assessed through the Harrel's C-index. Results suggest that accurate segmentation can compute early meaningful features that are related with tumor recurrence. TLG seems to be strongly informative in prediction of tumor recurrence in cervical cancer.     

Évaluation fonctionnelle par TEP 18F-FDopa des paragangliomes et phéochromocytomes non métastatiques : impact de la localisation lésionnelle et du statut génétique

ObjectiveParaganglioma (PGL) and pheochromocytoma (PCC) are neuroendocrine tumors most often benign associated with hereditary syndromes in about 30% of cases. This study aims to define the impact of tumor location and patient genotype on the clinical value of ¹⁸F-FDopa PET by assessing in detail the false negative occurrences.Patients and methodsA retrospective study was conducted on a cohort of 53 cases with non-metastatic sporadic or inherited PGL/PCC (SDHx or VHL related syndromes), investigated with ¹⁸F-FDopa PET.ResultsOverall detection sensitivity of ¹⁸F-FDopa PET was 88%. Seventy-three lesions were found using this technique, including 49 head-and-neck PGL (HNP), two thoracic PGL (1 sympathetic and 1 parasympathetic), eight extra-adrenal retroperitoneal PGL and 15 PCC. The 10 missed lesions were seven extra-adrenal abdominal PGL (2 SDHB, 2 SDHD), two HNP (1 sporadic, 1 SDHD) and one PCC (1 SDHD).Conclusion¹⁸F-FDopa PET is a sensitive technique for the evaluation of non-metastatic head and neck and adrenal PGLs. Exploration of extra-adrenal retroperitoneal PGL associated with SDHB or SDHD syndrome is the main limitation of this technique, encouraging the use of alternative functional imaging modalities like FDG-PET. Negativity of ¹⁸F-FDopa PET in the initial assessment of a PGL should prompt to search for a SDHx mutation.     

Comparing Artificial Neural Networks,General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs

Background

Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.

Principal Findings

Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs), General Linear Models (GLMs) and Support Vector Machines (SVMs). Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3×5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation.

Conclusions

The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are not consistent across learning techniques, suggesting care should be taken in the interpretation of feature relevance. In the models developed here, there are statistically differentiable combinations of learning techniques and feature mapping methods where the SVM technique under a specific combination of features significantly outperforms all the best combinations of features within the ANN and GLM techniques.     

Testing the generalizability of cfDNA fragmentomic features across different studies for cancer early detection

cfDNA fragmentomic features have been used in cancer detection models; however, the generalizability of the models needs to be tested. We proposed a type of cfDNA fragmentomic feature named chromosomal arm-level fragment size distribution (ARM-FSD), evaluated and compared its performance and generalizability for lung cancer and pan-cancer detection with existing cfDNA fragmentomic features (as reference) by using cohorts from different institutions. The ARM-FSD lung cancer model outperformed the reference model by ∼10% when being tested by two external cohorts (AUC: 0.97 vs. 0.86; 0.87 vs. 0.76). For pan-cancer detection, the performance of the ARM-FSD based model is consistently higher than the reference (AUC: 0.88 vs. 0.75, 0.98 vs. 0.63) in a pan-cancer and a lung cancer external validation cohort, indicating that ARM-FSD model produces stable performance across multiple cohorts. Our study reveals ARM-FSD based models have a higher generalizability, and highlights the necessity of cross-study validation for predictive model development.