•Artificial intelligence is used for assessing response to therapy in rectal cancer.•Textural features extraction from high resolution 3 T MR images.•Artificial intelligence model helps personalize ...therapeutic strategy.•Decisions curves analysis confirm clinical utility.
To develop and validate an Artificial Intelligence (AI) model based on texture analysis of high-resolution T2 weighted MR images able 1) to predict pathologic Complete Response (CR) and 2) to identify non-responders (NR) among patients with locally-advanced rectal cancer (LARC) after receiving neoadjuvant chemoradiotherapy (CRT).
Fifty-five consecutive patients with LARC were retrospectively enrolled in this study. Patients underwent 3 T Magnetic Resonance Imaging (MRI) acquiring T2-weighted images before, during and after CRT. All patients underwent complete surgical resection and histopathology was the gold standard. Textural features were automatically extracted using an open-source software. A sub-set of statistically significant textural features was selected and two AI models were built by training a Random Forest (RF) classifier on 28 patients (training cohort). Model performances were estimated on 27 patients (validation cohort) using a ROC curve and a decision curve analysis.
Sixteen of 55 patients achieved CR. The AI model for CR classification showed good discrimination power with mean area under the receiver operating curve (AUC) of 0.86 (95% CI: 0.70, 0.94) in the validation cohort. The discriminatory power for the NR classification showed a mean AUC of 0.83 (95% CI: 0.71,0.92). Decision curve analysis confirmed higher net patient benefit when using AI models compared to standard-of-care.
AI models based on textural features of MR images of patients with LARC may help to identify patients who will show CR at the end of treatment and those who will not respond to therapy (NR) at an early stage of the treatment.
High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to ...predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC.
Purpose
To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods.
Methods
A deep ...learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established “no-new-Net” framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected from four different institutions were used for training (
n
= 276), evaluation (
n
= 104) and testing (
n
= 71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test.
Results
Our model outperformed no-new-Net for the pelvic/ovarian lesions in cross-validation, on the evaluation and test set by a significant margin (
p
values being 4 × 10
–7
, 3 × 10
–4
, 4 × 10
–2
, respectively), and for the omental lesions on the evaluation set (
p
= 1 × 10
–3
). Our model did not perform significantly differently in segmenting pelvic/ovarian lesions (
p
= 0.371) compared to a trainee radiologist. On an independent test set, the model achieved a DSC performance of 71 ± 20 (mean ± standard deviation) for pelvic/ovarian and 61 ± 24 for omental lesions.
Conclusion
Automated ovarian cancer segmentation on CT scans using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions.
Relevance statement
Automated segmentation of ovarian cancer may be used by clinicians for CT-based volumetric assessments and researchers for building complex analysis pipelines.
Key points
• The first automated approach for pelvic/ovarian and omental ovarian cancer lesion segmentation on CT images has been presented.
• Automated segmentation of ovarian cancer lesions can be comparable with manual segmentation of trainee radiologists.
• Careful hyperparameter tuning can provide models significantly outperforming strong state-of-the-art baselines.
Graphical Abstract
CT myocardial perfusion: state of the science Caruso, Damiano; DE Santis, Domenico; Schoepf, U Joseph ...
Minerva cardioangiologica,
06/2017, Letnik:
65, Številka:
3
Journal Article
Recenzirano
Non-invasive cardiac imaging has rapidly evolved during the last decade due to advancements in CT technologies. Coronary CT angiography (CCTA) has been shown to reliably assess the coronary anatomy ...and has established itself as the non-invasive imaging technique with the highest sensitivity and specificity in the evaluation of patients with suspected coronary artery disease (CAD). However, this technique has previously been limited to a pure anatomical assessment. CT myocardial perfusion imaging (CT-MPI) is an increasingly rediscovered CT technique able to provide functional assessment of the myocardium and, when combined with CTA, allows for a comprehensive assessment of the coronary arteries, all done within a single modality. This review will describe the current knowledge in CT-MPI, including the varying techniques as well as a summary of the current literature.
Taxonomy and phylogenetic inference Avanzati, A. M.; Bernini, F.; Petrucci, R. ...
Bollettino di zoologia,
19/1/1/, Letnik:
53, Številka:
sup001
Journal Article
Hydrobiology and Fish biology Alessio, G.; Alessio, G.; Baldaccini, G. N. ...
Bollettino di zoologia,
19/1/1/, Letnik:
53, Številka:
sup001
Journal Article
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