Comparison of lung cancer occurring in fibrotic versus non-fibrotic lung on chest CT.

PURPOSE: Evaluate the behavior of lung nodules occurring in areas of pulmonary fibrosis and compare them to pulmonary nodules occurring in the non-fibrotic lung parenchyma. METHODS: This retrospective review of chest CT scans and electronic medical records received expedited IRB approval and a waiver of informed consent. 4500 consecutive patients with a chest CT scan report containing the word fibrosis or a specific type of fibrosis were identified using the system M*Model Catalyst (Maplewood, Minnesota, U.S.). The largest nodule was measured in the longest dimension and re-evaluated, in the same way, on the follow-up exam if multiple time points were available. The nodule doubling time was calculated. If the patient developed cancer, the histologic diagnosis was documented. RESULTS: Six hundred and nine patients were found to have at least one pulmonary nodule on either the first or the second CT scan. 274 of the largest pulmonary nodules were in the fibrotic tissue and 335 were in the non-fibrotic lung parenchyma. Pathology proven cancer was more common in nodules occurring in areas of pulmonary fibrosis compared to nodules occurring in areas of non-fibrotic lung (34% vs 15%, p < 0.01). Adenocarcinoma was the most common cell type in both groups but more frequent in cancers occurring in non-fibrotic tissue. In the non-fibrotic lung, 1 of 126 (0.8%) of nodules measuring 1 to 6 mm were cancer. In contrast, 5 of 49 (10.2%) of nodules in fibrosis measuring 1 to 6 mm represented biopsy-proven cancer (p < 0.01). The doubling time for squamous cell cancer was shorter in the fibrotic lung compared to non-fibrotic lung, however, the difference was not statistically significant (p = 0.24). 15 incident lung nodules on second CT obtained ≤ 18 months after first CT scan was found in fibrotic lung and eight (53%) were diagnosed as cancer. CONCLUSIONS: Nodules occurring in fibrotic lung tissue are more likely to be cancer than nodules in the nonfibrotic lung. Incident pulmonary nodules in pulmonary fibrosis have a high likelihood of being cancer.

Lung nodule malignancy classification with associated pulmonary fibrosis using 3D attention-gated convolutional network with CT scans.

BACKGROUND: Chest Computed tomography (CT) scans detect lung nodules and assess pulmonary fibrosis. While pulmonary fibrosis indicates increased lung cancer risk, current clinical practice characterizes nodule risk of malignancy based on nodule size and smoking history; little consideration is given to the fibrotic microenvironment. PURPOSE: To evaluate the effect of incorporating fibrotic microenvironment into classifying malignancy of lung nodules in chest CT images using deep learning techniques. MATERIALS AND METHODS: We developed a visualizable 3D classification model trained with in-house CT dataset for the nodule malignancy classification task. Three slightly-modified datasets were created: (1) nodule alone (microenvironment removed); (2) nodule with surrounding lung microenvironment; and (3) nodule in microenvironment with semantic fibrosis metadata. For each of the models, tenfold cross-validation was performed. Results were evaluated using quantitative measures, such as accuracy, sensitivity, specificity, and area-under-curve (AUC), as well as qualitative assessments, such as attention maps and class activation maps (CAM). RESULTS: The classification model trained with nodule alone achieved 75.61% accuracy, 50.00% sensitivity, 88.46% specificity, and 0.78 AUC; the model trained with nodule and microenvironment achieved 79.03% accuracy, 65.46% sensitivity, 85.86% specificity, and 0.84 AUC. The model trained with additional semantic fibrosis metadata achieved 80.84% accuracy, 74.67% sensitivity, 84.95% specificity, and 0.89 AUC. Our visual evaluation of attention maps and CAM suggested that both the nodules and the microenvironment contributed to the task. CONCLUSION: The nodule malignancy classification performance was found to be improving with microenvironment data. Further improvement was found when incorporating semantic fibrosis information.

Diagnostic Utility of Restriction Spectrum Imaging in Head and Neck Tumors: A Pilot Study.

OBJECTIVE: Imaging is crucial in the assessment of head and neck cancers for site, extension, and enlarged lymph nodes. Restriction spectrum imaging (RSI) is a new diffusion-weighted magnetic resonance imaging (MRI) technique that enhances the ability to differentiate aggressive cancer from low-grade or benign tumors and helps guide treatment and biopsy. Its contribution to imaging of brain and prostate tumors has been previously published. However, there are no prior studies using RSI sequence in head and neck tumors. The purpose of this study was to evaluate the feasibility of performing RSI in head and neck cancer. METHODS: An additional RSI sequence was added in the routine MRI neck protocol for 13 patients diagnosed with head and neck cancer between November 2018 and April 2019. Restriction spectrum imaging sequence was performed with b values of 0, 500, 1500, and 3000 s/mm 2 and 29 directions on 1.5T magnetic resonance scanners.Diffusion-weighted imaging (DWI) images and RSI images were compared according to their ability to detect the primary malignancy and possible metastatic lymph nodes. RESULTS: In 71% of the patients, RSI outperformed DWI in detecting the primary malignancy and possible metastatic lymph nodes, whereas in the remaining cases, the 2 were comparable. In 66% of the patients, RSI detected malignant lymph nodes that DWI/apparent diffusion coefficient failed to detect. CONCLUSIONS: This is the first study of RSI in head and neck imaging and showed its superiority over the conventional DWI sequence. Because of its ability to differentiate benign and malignant lymph nodes in some cases, the addition of RSI to routine head and neck MRI should be considered.

Prediction of locally advanced rectal cancer response to neoadjuvant chemoradiation therapy using volumetric multiparametric MRI-based radiomics.

PURPOSE: To assess the role of pretreatment multiparametric (mp)MRI-based radiomic features in predicting pathologic complete response (pCR) of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiation therapy (nCRT). METHODS: This was a retrospective dual-center study including 98 patients (M/F 77/21, mean age 60 years) with LARC who underwent pretreatment mpMRI followed by nCRT and total mesorectal excision or watch and wait. Fifty-eight patients from institution 1 constituted the training set and 40 from institution 2 the validation set. Manual segmentation using volumes of interest was performed on T1WI pre-/post-contrast, T2WI and diffusion-weighted imaging (DWI) sequences. Demographic information and serum carcinoembryonic antigen (CEA) levels were collected. Shape, 1st and 2nd order radiomic features were extracted and entered in models based on principal component analysis used to predict pCR. The best model was obtained using a k-fold cross-validation method on the training set, and AUC, sensitivity and specificity for prediction of pCR were calculated on the validation set. RESULTS: Stage distribution was T3 (n = 79) or T4 (n = 19). Overall, 16 (16.3%) patients achieved pCR. Demographics, MRI TNM stage, and CEA were not predictive of pCR (p range 0.59-0.96), while several radiomic models achieved high diagnostic performance for prediction of pCR (in the validation set), with AUCs ranging from 0.7 to 0.9, with the best model based on high b-value DWI demonstrating AUC of 0.9 [95% confidence intervals: 0.67, 1], sensitivity of 100% [100%, 100%], and specificity of 81% [66%, 96%]. CONCLUSION: Radiomic models obtained from pre-treatment MRI show good to excellent performance for the prediction of pCR in patients with LARC, superior to clinical parameters and CEA. A larger study is needed for confirmation of these results.

Diffusion tensor imaging of the physis: the ABC's.

The physis, or growth plate, is the primary structure responsible for longitudinal growth of the long bones. Diffusion tensor imaging (DTI) is a technique that depicts the anisotropic motion of water molecules, or diffusion. When diffusion is limited by cellular membranes, information on tissue microstructure can be acquired. Tractography, the visual display of the direction and magnitude of water diffusion, provides qualitative visualization of complex cellular architecture as well as quantitative diffusion metrics that appear to indirectly reflect physeal activity. In the growing bones, DTI depicts the columns of cartilage and new bone in the physeal-metaphyseal complex. In this "How I do It", we will highlight the value of DTI as a clinical tool by presenting DTI tractography of the physeal-metaphyseal complex of children and adolescents during normal growth, illustrating variation in qualitative and quantitative tractography metrics with age and skeletal location. In addition, we will present tractography from patients with physeal dysfunction caused by growth hormone deficiency and physeal injury due to trauma, chemotherapy, and radiation therapy. Furthermore, we will delineate our process, or "DTI pipeline," from image acquisition to data interpretation.

Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review.

Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assessment models use risk factors such as demographics and patient history to guide policy and assess risk. Applications of artificial intelligence methods (AI) such as deep learning (DL) and convolutional neural networks (CNNs) to evaluate individual patient information and imaging showed promise as personalized risk models. We reviewed the current literature for studies related to deep learning and convolutional neural networks with digital mammography for assessing breast cancer risk. We discussed the literature and examined the ongoing and future applications of deep learning techniques in breast cancer risk modeling.

Chronic convection-enhanced delivery of topotecan for patients with recurrent glioblastoma: a first-in-patient, single-centre, single-arm, phase 1b trial.

BACKGROUND: Topotecan is cytotoxic to glioma cells but is clinically ineffective because of drug delivery limitations. Systemic delivery is limited by toxicity and insufficient brain penetrance, and, to date, convection-enhanced delivery (CED) has been restricted to a single treatment of restricted duration. To address this problem, we engineered a subcutaneously implanted catheter-pump system capable of repeated, chronic (prolonged, pulsatile) CED of topotecan into the brain and tested its safety and biological effects in patients with recurrent glioblastoma. METHODS: We did a single-centre, open-label, single-arm, phase 1b clinical trial at Columbia University Irving Medical Center (New York, NY, USA). Eligible patients were at least 18 years of age with solitary, histologically confirmed recurrent glioblastoma showing radiographic progression after surgery, radiotherapy, and chemotherapy, and a Karnofsky Performance Status of at least 70. Five patients had catheters stereotactically implanted into the glioma-infiltrated peritumoural brain and connected to subcutaneously implanted pumps that infused 146 µM topotecan 200 µL/h for 48 h, followed by a 5-7-day washout period before the next infusion, with four total infusions. After the fourth infusion, the pump was removed and the tumour was resected. The primary endpoint of the study was safety of the treatment regimen as defined by presence of serious adverse events. Analyses were done in all treated patients. The trial is closed, and is registered with ClinicalTrials.gov, NCT03154996. FINDINGS: Between Jan 22, 2018, and July 8, 2019, chronic CED of topotecan was successfully completed safely in all five patients, and was well tolerated without substantial complications. The only grade 3 adverse event related to treatment was intraoperative supplemental motor area syndrome (one [20%] of five patients in the treatment group), and there were no grade 4 adverse events. Other serious adverse events were related to surgical resection and not the study treatment. Median follow-up was 12 months (IQR 10-17) from pump explant. Post-treatment tissue analysis showed that topotecan significantly reduced proliferating tumour cells in all five patients. INTERPRETATION: In this small patient cohort, we showed that chronic CED of topotecan is a potentially safe and active therapy for recurrent glioblastoma. Our analysis provided a unique tissue-based assessment of treatment response without the need for large patient numbers. This novel delivery of topotecan overcomes limitations in delivery and treatment response assessment for patients with glioblastoma and could be applicable for other anti-glioma drugs or other CNS diseases. Further studies are warranted to determine the effect of this drug delivery approach on clinical outcomes. FUNDING: US National Institutes of Health, The William Rhodes and Louise Tilzer Rhodes Center for Glioblastoma, the Michael Weiner Glioblastoma Research Into Treatment Fund, the Gary and Yael Fegel Foundation, and The Khatib Foundation.

Dosimetric assessment of patient dose calculation on a deep learning-based synthesized computed tomography image for adaptive radiotherapy.

PURPOSE: Dose computation using cone beam computed tomography (CBCT) images is inaccurate for the purpose of adaptive treatment planning. The main goal of this study is to assess the dosimetric accuracy of synthetic computed tomography (CT)-based calculation for adaptive planning in the upper abdominal region. We hypothesized that deep learning-based synthetically generated CT images will produce comparable results to a deformed CT (CTdef) in terms of dose calculation, while displaying a more accurate representation of the daily anatomy and therefore superior dosimetric accuracy. METHODS: We have implemented a cycle-consistent generative adversarial networks (CycleGANs) architecture to synthesize CT images from the daily acquired CBCT image with minimal error. CBCT and CT images from 17 liver stereotactic body radiation therapy (SBRT) patients were used to train, test, and validate the algorithm. RESULTS: The synthetically generated images showed increased signal-to-noise ratio, contrast resolution, and reduced root mean square error, mean absolute error, noise, and artifact severity. Superior edge matching, sharpness, and preservation of anatomical structures from the CBCT images were observed for the synthetic images when compared to the CTdef registration method. Three verification plans (CBCT, CTdef, and synthetic) were created from the original treatment plan and dose volume histogram (DVH) statistics were calculated. The synthetic-based calculation shows comparatively similar results to the CTdef-based calculation with a maximum mean deviation of 1.5%. CONCLUSIONS: Our findings show that CycleGANs can produce reliable synthetic images for the adaptive delivery framework. Dose calculations can be performed on synthetic images with minimal error. Additionally, enhanced image quality should translate into better daily alignment, increasing treatment delivery accuracy.

Deep learning prediction of axillary lymph node status using ultrasound images.

OBJECTIVE: To investigate the ability of our convolutional neural network (CNN) to predict axillary lymph node metastasis using primary breast cancer ultrasound (US) images. METHODS: In this IRB-approved study, 338 US images (two orthogonal images) from 169 patients from 1/2014-12/2016 were used. Suspicious lymph nodes were seen on US and patients subsequently underwent core-biopsy. 64 patients had metastatic lymph nodes. A custom CNN was utilized on 248 US images from 124 patients in the training dataset and tested on 90 US images from 45 patients. The CNN was implemented entirely of 3 × 3 convolutional kernels and linear layers. The 9 convolutional kernels consisted of 6 residual layers, totaling 12 convolutional layers. Feature maps were down-sampled using strided convolutions. Dropout with a 0.5 keep probability and L2 normalization was utilized. Training was implemented by using the Adam optimizer and a final SoftMax score threshold of 0.5 from the average of raw logits from each pixel was used for two class classification (metastasis or not). RESULTS: Our CNN achieved an AUC of 0.72 (SD ± 0.08) in predicting axillary lymph node metastasis from US images in the testing dataset. The model had an accuracy of 72.6% (SD ± 8.4) with a sensitivity and specificity of 65.5% (SD ± 28.6) and 78.9% (SD ± 15.1) respectively. Our algorithm is available to be shared for research use. (https://github.com/stmutasa/MetUS). CONCLUSION: It's feasible to predict axillary lymph node metastasis from US images using a deep learning technique. This can potentially aid nodal staging in patients with breast cancer.

Weakly Supervised Deep Learning Approach to Breast MRI Assessment.

RATIONALE AND OBJECTIVES: To evaluate a weakly supervised deep learning approach to breast Magnetic Resonance Imaging (MRI) assessment without pixel level segmentation in order to improve the specificity of breast MRI lesion classification. MATERIALS AND METHODS: In this IRB approved study, the dataset consisted of 278,685 image slices from 438 patients. The weakly supervised network was based on the Resnet-101 architecture. Training was implemented using the Adam optimizer and a final SoftMax score threshold of 0.5 was used for two class classification (malignant or benign). 278,685 image slices were combined into 92,895 3-channel images. 79,871 (85%) images were used for training and validation while 13,024 (15%) images were separated for testing. Of the testing dataset, 11,498 (88%) were benign and 1531 (12%) were malignant. Model performance was assessed. RESULTS: The weakly supervised network achieved an AUC of 0.92 (SD ± 0.03) in distinguishing malignant from benign images. The model had an accuracy of 94.2% (SD ± 3.4) with a sensitivity and specificity of 74.4% (SD ± 8.5) and 95.3% (SD ± 3.3) respectively. CONCLUSION: It is feasible to use a weakly supervised deep learning approach to assess breast MRI images without the need for pixel-by-pixel segmentation yielding a high degree of specificity in lesion classification.

Integrating Eye Tracking and Speech Recognition Accurately Annotates MR Brain Images for Deep Learning: Proof of Principle.

PURPOSE: To generate and assess an algorithm combining eye tracking and speech recognition to extract brain lesion location labels automatically for deep learning (DL). MATERIALS AND METHODS: In this retrospective study, 700 two-dimensional brain tumor MRI scans from the Brain Tumor Segmentation database were clinically interpreted. For each image, a single radiologist dictated a standard phrase describing the lesion into a microphone, simulating clinical interpretation. Eye-tracking data were recorded simultaneously. Using speech recognition, gaze points corresponding to each lesion were obtained. Lesion locations were used to train a keypoint detection convolutional neural network to find new lesions. A network was trained to localize lesions for an independent test set of 85 images. The statistical measure to evaluate our method was percent accuracy. RESULTS: Eye tracking with speech recognition was 92% accurate in labeling lesion locations from the training dataset, thereby demonstrating that fully simulated interpretation can yield reliable tumor location labels. These labels became those that were used to train the DL network. The detection network trained on these labels predicted lesion location of a separate testing set with 85% accuracy. CONCLUSION: The DL network was able to locate brain tumors on the basis of training data that were labeled automatically from simulated clinical image interpretation.© RSNA, 2020.

Deep Learning of Computed Tomography Virtual Wedge Resection for Prediction of Histologic Usual Interstitial Pneumonitis.

Rationale: The computed tomography (CT) pattern of definite or probable usual interstitial pneumonia (UIP) can be diagnostic of idiopathic pulmonary fibrosis and may obviate the need for invasive surgical biopsy. Few machine-learning studies have investigated the classification of interstitial lung disease (ILD) on CT imaging, but none have used histopathology as a reference standard.Objectives: To predict histopathologic UIP using deep learning of high-resolution computed tomography (HRCT).Methods: Institutional databases were retrospectively searched for consecutive patients with ILD, HRCT, and diagnostic histopathology from 2011 to 2014 (training cohort) and from 2016 to 2017 (testing cohort). A blinded expert radiologist and pulmonologist reviewed all training HRCT scans in consensus and classified HRCT scans based on the 2018 American Thoracic Society/European Respriatory Society/Japanese Respiratory Society/Latin American Thoracic Association diagnostic criteria for idiopathic pulmonary fibrosis. A convolutional neural network (CNN) was built accepting 4 × 4 × 2 cm virtual wedges of peripheral lung on HRCT as input and outputting the UIP histopathologic pattern. The CNN was trained and evaluated on the training cohort using fivefold cross validation and was then tested on the hold-out testing cohort. CNN and human performance were compared in the training cohort. Logistic regression and survival analyses were performed.Results: The CNN was trained on 221 patients (median age 60 yr; interquartile range [IQR], 53-66), including 71 patients (32%) with UIP or probable UIP histopathologic patterns. The CNN was tested on a separate hold-out cohort of 80 patients (median age 66 yr; IQR, 58-69), including 22 patients (27%) with UIP or probable UIP histopathologic patterns. An average of 516 wedges were generated per patient. The percentage of wedges with CNN-predicted UIP yielded a cross validation area under the curve of 74% for histopathological UIP pattern per patient. The optimal cutoff point for classifying patients on the training cohort was 16.5% of virtual lung wedges with CNN-predicted UIP and resulted in sensitivity and specificity of 74% and 58%, respectively, in the testing cohort. CNN-predicted UIP was associated with an increased risk of death or lung transplantation during cross validation (hazard ratio, 1.5; 95% confidence interval, 1.1-2.2; P = 0.03).Conclusions: Virtual lung wedge resection in patients with ILD can be used as an input to a CNN for predicting the histopathologic UIP pattern and transplant-free survival.

A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI database.

PURPOSE: To apply our convolutional neural network (CNN) algorithm to predict neoadjuvant chemotherapy (NAC) response using the I-SPY TRIAL breast MRI dataset. METHODS: From the I-SPY TRIAL breast MRI database, 131 patients from 9 institutions were successfully downloaded for analysis. First post-contrast MRI images were used for 3D segmentation using 3D slicer. Our CNN was implemented entirely of 3 × 3 convolutional kernels and linear layers. The convolutional kernels consisted of 6 residual layers, totaling 12 convolutional layers. Dropout with a 0.5 keep probability and L2 normalization was utilized. Training was implemented by using the Adam optimizer. A 5-fold cross validation was used for performance evaluation. Software code was written in Python using the TensorFlow module on a Linux workstation with one NVidia Titan X GPU. RESULTS: Of 131 patients, 40 patients achieved pCR following NAC (group 1) and 91 patients did not achieve pCR following NAC (group 2). Diagnostic accuracy of our CNN two classification model distinguishing patients with pCR vs non-pCR was 72.5 (SD ± 8.4), with sensitivity 65.5% (SD ± 28.1) and specificity of 78.9% (SD ± 15.2). The area under a ROC Curve (AUC) was 0.72 (SD ± 0.08). CONCLUSION: It is feasible to use our CNN algorithm to predict NAC response in patients using a multi-institution dataset.

Channel width optimized neural networks for liver and vessel segmentation in liver iron quantification.

INTRODUCTION: MRI T2* relaxometry protocols are often used for Liver Iron Quantification in patients with hemochromatosis. Several methods exist to semi-automatically segment parenchyma and exclude vessels for this calculation. PURPOSE: To determine if inclusion of multiple echoes inputs to Convolutional Neural Networks (CNN) improves automated liver and vessel segmentation in MRI T2* relaxometry protocols and to determine if the resultant segmentations agree with manual segmentations for liver iron quantification analysis. METHODS: Multi echo Gradient Recalled Echo (GRE) MRI sequence for T2* relaxometry was performed for 79 exams on 31 patients with hemochromatosis for iron quantification analysis. 275 axial liver slices were manually segmented as ground truth masks. A batch normalized U-Net with variable input width to incorporate multiple echoes is used for segmentation, using DICE as the accuracy metric. ANOVA is used to evaluate significance of channel width changes in segmentation accuracy. Linear regression is used to model the relationship of channel width on segmentation accuracy. Liver segmentations are applied to relaxometry data to calculate liver T2* yielding liver iron concentration(LIC) derived from literature based calibration curves. Manual and CNN based LIC values are compared with Pearson correlation. Bland altman plots are used to visualize differences between manual and CNN based LIC values. RESULTS: Performance metrics are tested on 55 hold out slices. Linear regression indicates that there is a monotonic increase of DICE with increasing channel depth (p = 0.001) with a slope of 3.61e-3. ANOVA indicates a significant increase segmentation accuracy over single channel starting at 3 channels. Incorporation of all channels results in an average DICE of 0.86, an average increase of 0.07 over single channel. The calculated LIC from CNN segmented livers agrees well with manual segmentation (R = 0.998, slope = 0.914, p«0.001), with an average absolute difference 0.27 ± 0.99 mg Fe/g or 1.34 ± 4.3%. CONCLUSION: More input echoes yields higher model accuracy until the noise floor. Echos beyond the first three echo times in GRE based T2* relaxometry do not contribute significant information for segmentation of liver for LIC calculation. Deep learning models with three channel width allow for generalization of model to protocols of more than three echoes, effectively a universal requirement for relaxometry. Deep learning segmentations achieve a good accuracy compared with manual segmentations with minimal preprocessing. Liver iron values calculated from hand segmented liver and Neural network segmented liver were not statistically different from each other.

Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study.

OBJECTIVE: To investigate whether pretreatment MRI-based radiomics of locally advanced rectal cancer (LARC) and/or the surrounding mesorectal compartment (MC) can predict pathologic complete response (pCR), neoadjuvant rectal (NAR) score, and tumor regression grade (TRG). METHODS: One hundred thirty-two consecutive patients with LARC who underwent neoadjuvant chemoradiation and total mesorectal excision (TME) were retrospectively collected from 2 centers in the USA and Italy. The primary tumor and surrounding MC were segmented on the best available T2-weighted sequence (axial, coronal, or sagittal). Three thousand one hundred ninety radiomic features were extracted using a python package. The most salient radiomic features as well as MRI parameter and clinical-based features were selected using recursive feature elimination. A logistic regression classifier was built to distinguish between any 2 binned categories in the considered endpoints: pCR, NAR, and TRG. Repeated k-fold validation was performed and AUCs calculated. RESULTS: There were 24, 87, and 21 T4, T3, and T2 LARCs, respectively (median age 63 years, 32 to 86). For NAR and TRG, the best classification performance was obtained using both the tumor and MC segmentations. The AUCs for classifying NAR 0 versus 2, pCR, and TRG 0/1 versus 2/3 were 0.66 (95% CI, 0.60-0.71), 0.80 (95% CI, 0.74-0.85), and 0.80 (95% CI, 0.77-0.82), respectively. CONCLUSION: Radiomics of pretreatment MRIs can predict pCR, TRG, and NAR score in patients with LARC undergoing neoadjuvant treatment and TME with moderate accuracy despite extremely heterogenous image data. Both the tumor and MC contain important prognostic information. KEY POINTS: • Machine learning of rectal cancer on images from the pretreatment MRI can predict important patient outcomes with moderate accuracy. • The tumor and the tissue around it both contain important prognostic information.

Convolutional Neural Network Detection of Axillary Lymph Node Metastasis Using Standard Clinical Breast MRI.

BACKGROUND: Axillary lymph node status is important for breast cancer staging and treatment planning as the majority of breast cancer metastasis spreads through the axillary lymph nodes. There is currently no reliable noninvasive imaging method to detect nodal metastasis associated with breast cancer. MATERIALS AND METHODS: Magnetic resonance imaging (MRI) data were those from the peak contrast dynamic image from 1.5 Tesla MRI scanners at the pre-neoadjuvant chemotherapy stage. Data consisted of 66 abnormal nodes from 38 patients and 193 normal nodes from 61 patients. Abnormal nodes were those determined by expert radiologist based on 18Fluorodeoxyglucose positron emission tomography images. Normal nodes were those with negative diagnosis of breast cancer. The convolutional neural network consisted of 5 convolutional layers with filters from 16 to 128. Receiver operating characteristic analysis was performed to evaluate prediction performance. For comparison, an expert radiologist also scored the same nodes as normal or abnormal. RESULTS: The convolutional neural network model yielded a specificity of 79.3% ± 5.1%, sensitivity of 92.1% ± 2.9%, positive predictive value of 76.9% ± 4.0%, negative predictive value of 93.3% ± 1.9%, accuracy of 84.8% ± 2.4%, and receiver operating characteristic area under the curve of 0.91 ± 0.02 for the validation data set. These results compared favorably with scoring by radiologists (accuracy of 78%). CONCLUSION: The results are encouraging and suggest that this approach may prove useful for classifying lymph node status on MRI in clinical settings in patients with breast cancer, although additional studies are needed before routine clinical use can be realized. This approach has the potential to ultimately be a noninvasive alternative to lymph node biopsy.

A threshold-based method to predict thyroid nodules on scintigraphy scans.

Nuclear Medicine imaging is an important modality to follow up abnormalities of thyroid function tests and to uncover and characterize thyroid nodules either de novo or as previously seen on other imaging modalities, namely ultrasound. In general, the hypofunctioning 'cold' nodules pose a higher malignancy potential than hyperfunctioning 'hot' nodules, for which the risk is <1%. Hot nodules are detected by the radiologist as a region of focal increased radiotracer uptake, which appears as a density of pixels that is higher than surrounding normal thyroid parenchyma. Similarly, cold nodules show decreased density of pixels, corresponding to their decreased uptake of radiotracer, and are photopenic. Partly because Nuclear Medicine images have poor resolution, these density variations can sometimes be subtle, and a second reader computer-aided detection (CAD) scheme that can highlight hot/cold nodules has the potential to reduce false negatives by bringing the radiologists' attention to the occasional overlooked nodules. Our approach subdivides thyroid images into small regions and employs a set of pixel density cutoffs, marking regions that fulfill density criteria. Thresholding is a fundamental tool in image processing. In nuclear medicine, scroll bars to adjust standardized uptake value cutoffs are already in wide commercial use in PET/CT display systems. A similar system could be used for planar thyroid images, whereby the user varies threshold and highlights suspect regions after an initial reader survey of the images. We hypothesized that a thresholding approach would accurately detect both hot and cold thyroid nodules relative to expert readers. Analyzing 22 nodules, half of them hot and the other half cold, we found good agreement between highlighted candidate nodules and the consensus selections of two expert readers, with nonzero overlap between expert and CAD selections in all cases.

Potential Role of Convolutional Neural Network Based Algorithm in Patient Selection for DCIS Observation Trials Using a Mammogram Dataset.

RATIONALE AND OBJECTIVES: We investigated the feasibility of utilizing convolutional neural network (CNN) for predicting patients with pure Ductal Carcinoma In Situ (DCIS) versus DCIS with invasion using mammographic images. MATERIALS AND METHODS: An IRB-approved retrospective study was performed. 246 unique images from 123 patients were used for our CNN algorithm. In total, 164 images in 82 patients diagnosed with DCIS by stereotactic-guided biopsy of calcifications without any upgrade at the time of surgical excision (pure DCIS group). A total of 82 images in 41 patients with mammographic calcifications yielding occult invasive carcinoma as the final upgraded diagnosis on surgery (occult invasive group). Two standard mammographic magnification views (CC and ML/LM) of the calcifications were used for analysis. Calcifications were segmented using an open source software platform 3D Slicer and resized to fit a 128 × 128 pixel bounding box. A 15 hidden layer topology was used to implement the neural network. The network architecture contained five residual layers and dropout of 0.25 after each convolution. Five-fold cross validation was performed using training set (80%) and validation set (20%). Code was implemented in open source software Keras with TensorFlow on a Linux workstation with NVIDIA GTX 1070 Pascal GPU. RESULTS: Our CNN algorithm for predicting patients with pure DCIS achieved an overall diagnostic accuracy of 74.6% (95% CI, ±5) with area under the ROC curve of 0.71 (95% CI, ±0.04), specificity of 91.6% (95% CI, ±5%) and sensitivity of 49.4% (95% CI, ±6%). CONCLUSION: It's feasible to apply CNN to distinguish pure DCIS from DCIS with invasion with high specificity using mammographic images.

Repeatability of Quantitative Diffusion-Weighted Imaging Metrics in Phantoms, Head-and-Neck and Thyroid Cancers: Preliminary Findings.

The aim of this study was to establish the repeatability measures of quantitative Gaussian and non-Gaussian diffusion metrics using diffusion-weighted imaging (DWI) data from phantoms and patients with head-and-neck and papillary thyroid cancers. The Quantitative Imaging Biomarker Alliance (QIBA) DWI phantom and a novel isotropic diffusion kurtosis imaging phantom were scanned at 3 different sites, on 1.5T and 3T magnetic resonance imaging systems, using standardized multiple b-value DWI acquisition protocol. In the clinical component of this study, a total of 60 multiple b-value DWI data sets were analyzed for test-retest, obtained from 14 patients (9 head-and-neck squamous cell carcinoma and 5 papillary thyroid cancers). Repeatability of quantitative DWI measurements was assessed by within-subject coefficient of variation (wCV%) and Bland-Altman analysis. In isotropic diffusion kurtosis imaging phantom vial with 2% ceteryl alcohol and behentrimonium chloride solution, the mean apparent diffusion (Dapp × 10-3 mm2/s) and kurtosis (Kapp, unitless) coefficient values were 1.02 and 1.68 respectively, capturing in vivo tumor cellularity and tissue microstructure. For the same vial, Dapp and Kapp mean wCVs (%) were ≤1.41% and ≤0.43% for 1.5T and 3T across 3 sites. For pretreatment head-and-neck squamous cell carcinoma, apparent diffusion coefficient, D, D*, K, and f mean wCVs (%) were 2.38%, 3.55%, 3.88%, 8.0%, and 9.92%, respectively; wCVs exhibited a higher trend for papillary thyroid cancers. Knowledge of technical precision and bias of quantitative imaging metrics enables investigators to properly design and power clinical trials and better discern between measurement variability versus biological change.

Accuracy of Distinguishing Atypical Ductal Hyperplasia From Ductal Carcinoma In Situ With Convolutional Neural Network-Based Machine Learning Approach Using Mammographic Image Data.

OBJECTIVE. The purpose of this study was to test the hypothesis that convolutional neural networks can be used to predict which patients with pure atypical ductal hyperplasia (ADH) may be safely monitored rather than undergo surgery. MATERIALS AND METHODS. A total of 298 unique images from 149 patients were used for our convolutional neural network algorithm. A total of 134 images from 67 patients with ADH that had been diagnosed by stereotactic-guided biopsy of calcifications but had not been upgraded to ductal carcinoma in situ or invasive cancer at the time of surgical excision. A total of 164 images from 82 patients with mammographic calcifications indicated that ductal carcinoma in situ was the final diagnosis. Two standard mammographic magnification views of the calcifications (a craniocaudal view and a mediolateral or lateromedial view) were used for analysis. Calcifications were segmented using an open-source software platform and images were resized to fit a bounding box of 128 × 128 pixels. A topology with 15 hidden layers was used to implement the convolutional neural network. The network architecture contained five residual layers and dropout of 0.25 after each convolution. Patients were randomly separated into a training-and-validation set (80% of patients) and a test set (20% of patients). Code was implemented using open-source software on a workstation with an open-source operating system and a graphics card. RESULTS. The AUC value was 0.86 (95% CI, ± 0.03) for the test set. Aggregate sensitivity and specificity were 84.6% (95% CI, ± 4.0%) and 88.2% (95% CI, ± 3.0%), respectively. Diagnostic accuracy was 86.7% (95% CI, ± 2.9). CONCLUSION. It is feasible to apply convolutional neural networks to distinguish pure atypical ductal hyperplasia from ductal carcinoma in situ with the use of mammographic images. A larger dataset will likely result in further improvement of our prediction model.