Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database

Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images. Such abnormalities, or lesions, have collected over years and stored in hospitals' picture archiving and communication systems. However, they are basically unsorted and lack semantic annotations like type and location. In this paper, we aim to organize and explore them by learning a deep feature representation for each lesion. A large-scale and comprehensive dataset, DeepLesion, is introduced for this task. DeepLesion contains bounding boxes and size measurements of over 32K lesions. To model their similarity relationship, we leverage multiple supervision information including types, self-supervised location coordinates and sizes. They require little manual annotation effort but describe useful attributes of the lesions. Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure. Experiments show promising qualitative and quantitative results on lesion retrieval, clustering, and classification. The learned embeddings can be further employed to build a lesion graph for various clinically useful applications. We propose algorithms for intra-patient lesion matching and missing annotation mining. Experimental results validate their effectiveness.Comment: Accepted by CVPR2018. DeepLesion url adde

Artificial intelligence in cancer imaging: Clinical challenges and applications

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care

Adrenocortical Carcinoma and CT Assessment of Therapy Response: The Value of Combining Multiple Criteria

We evaluated tumor response at Computed Tomography (CT) according to three radiologic criteria: RECIST 1.1, CHOI and tumor volume in 34 patients with metastatic adrenocortical carcinoma (ACC) submitted to standard chemotherapy. These three criteria agreed in defining partial response, stable or progressive disease in 24 patients (70.5%). Partial response (PR) was observed in 29.4%, 29.4% and 41.2% of patients according to RECIST 1.1, CHOI and tumor volume, respectively. It was associated with a favorable prognosis, regardless of the criterion adopted. The concordance of all the 3 criteria in defining the disease response identified 8 patients (23.5%) which displayed a very good prognosis: median progression free survival (PFS) and overall survival (OS) 14.9 and 37.7 months, respectively. Seven patients (20.6%) with PR assessed by one or two criteria, however, still had a better prognosis than non-responding patients, both in terms of PFS: median 12.3 versus 9.9 months and OS: 21 versus 12.2, respectively. In conclusions, the CT assessment of disease response of ACC patients to chemotherapy with 3 different criteria is feasible and allows the identification of a patient subset with a more favorable outcome. PR with at least one criterion can be useful to early identify patients that deserve continuing the therapy

Assessment of Post-Treatment Imaging Changes Following Radiotherapy using Magnetic Susceptibility Techniques

Radiation therapy (RT) is a common treatment for brain neoplasms and is used alone or in combination with other therapies. The use of RT has been found to be successful in controlling tumors and extending the overall survival of patients; however, there are many unanswered questions regarding radiotherapy effects in the normal brain surrounding or infiltrated by tumor. Changes to the vascular and parenchyma have been documented, and more recently inflammatory mechanisms have been postulated to play a role in radiation injury. Traditional imaging techniques used within the clinic (CT and MRI) are often lacking in their ability to differentiate between recurrent tumor, transient treatment effects, or radiation necrosis. The primary goal of this thesis is to demonstrate an MRI acquisition method that has been shown to be sensitive to deoxygenated blood and iron content as a potential biomarker of radiation effect on the normal brain. Specifically, post-processing techniques are used to determine the applicability of qualitative images such as Susceptibility-Weighted Imaging (SWI) and quantitative methods such as Quantitative Susceptibility Mapping (QSM) and apparent traverse relaxation (R2*) using the same sequence. These methods are potential surrogate markers for vascular changes and neuroinflammatory components that could predict sub-acute and long-term radiation effects. Within this thesis, R2* is shown to be a promising marker for the prediction of radiation necrosis, whereas SWI and QSM are shown to be excellent modalities for detecting longterm effects such as microbleeds. Additionally, R2 * is shown to be a potentially useful technique in identifying post-imaging treatment changes (pseudoprogression) following chemoradiotherapy for malignant glioma. Finally, the use of this non-contrast method shows promise for integration within a clinical setting and the potential for expansion to multicenter clinical trials

Theoretical investigation of transgastric and intraductal approaches for ultrasound-based thermal therapy of the pancreas.

BackgroundThe goal of this study was to theoretically investigate the feasibility of intraductal and transgastric approaches to ultrasound-based thermal therapy of pancreatic tumors, and to evaluate possible treatment strategies.MethodsThis study considered ultrasound applicators with 1.2 mm outer diameter tubular transducers, which are inserted into the tissue to be treated by an endoscopic approach, either via insertion through the gastric wall (transgastric) or within the pancreatic duct lumen (intraductal). 8 patient-specific, 3D, transient, biothermal and acoustic finite element models were generated to model hyperthermia (n = 2) and ablation (n = 6), using sectored (210°-270°, n = 4) and 360° (n = 4) transducers for treatment of 3.3-17.0 cm3 tumors in the head (n = 5), body (n = 2), and tail (n = 1) of the pancreas. A parametric study was performed to determine appropriate treatment parameters as a function of tissue attenuation, blood perfusion rates, and distance to sensitive anatomy.ResultsParametric studies indicated that pancreatic tumors up to 2.5 or 2.7 cm diameter can be ablated within 10 min with the transgastric and intraductal approaches, respectively. Patient-specific simulations demonstrated that 67.1-83.3% of the volumes of four sample 3.3-11.4 cm3 tumors could be ablated within 3-10 min using transgastric or intraductal approaches. 55.3-60.0% of the volume of a large 17.0 cm3 tumor could be ablated using multiple applicator positions within 20-30 min with either transgastric or intraductal approaches. 89.9-94.7% of the volume of two 4.4-11.4 cm3 tumors could be treated with intraductal hyperthermia. Sectored applicators are effective in directing acoustic output away from and preserving sensitive structures. When acoustic energy is directed towards sensitive structures, applicators should be placed at least 13.9-14.8 mm from major vessels like the aorta, 9.4-12.0 mm from other vessels, depending on the vessel size and flow rate, and 14 mm from the duodenum.ConclusionsThis study demonstrated the feasibility of generating shaped or conformal ablative or hyperthermic temperature distributions within pancreatic tumors using transgastric or intraductal ultrasound

Identification of cancer hallmarks in patients with non-metastatic colon cancer after surgical resection

Colon cancer is one of the most common cancers in the world, and the therapeutic workflow is dependent on the TNM staging system and the presence of clinical risk factors. However, in the case of patients with non-metastatic disease, evaluating the benefit of adjuvant chemotherapy is a clinical challenge. Radiomics could be seen as a non-invasive novel imaging biomarker able to outline tumor phenotype and to predict patient prognosis by analyzing preoperative medical images. Radiomics might provide decisional support for oncologists with the goal to reduce the number of arbitrary decisions in the emerging era of personalized medicine. To date, much evidence highlights the strengths of radiomics in cancer workup, but several aspects limit the use of radiomics methods as routine. The study aimed to develop a radiomic model able to identify high-risk colon cancer by analyzing pre-operative CT scans. The study population comprised 148 patients: 108 with non-metastatic colon cancer were retrospectively enrolled from January 2015 to June 2020, and 40 patients were used as the external validation cohort. The population was divided into two groups—High-risk and No-risk—following the presence of at least one high-risk clinical factor. All patients had baseline CT scans, and 3D cancer segmentation was performed on the portal phase by two expert radiologists using open-source software (3DSlicer v4.10.2). Among the 107 radiomic features extracted, stable features were selected to evaluate the inter-class correlation (ICC) (cut-off ICC > 0.8). Stable features were compared between the two groups (T-test or Mann–Whitney), and the significant features were selected for univariate and multivariate logistic regression to build a predictive radiomic model. The radiomic model was then validated with an external cohort. In total, 58/108 were classified as High-risk and 50/108 as No-risk. A total of 35 radiomic features were stable (0.81 ≤ ICC <  0.92). Among these, 28 features were significantly different between the two groups (p < 0.05), and only 9 features were selected to build the radiomic model. The radiomic model yielded an AUC of 0.73 in the internal cohort and 0.75 in the external cohort. In conclusion, the radiomic model could be seen as a performant, non-invasive imaging tool to properly stratify colon cancers with high-risk diseas

CT based radiomic approach on first line pembrolizumab in lung cancer

Clinical evaluation poorly predicts outcomes in lung cancer treated with immunotherapy. The aim of the study is to assess whether CT-derived texture parameters can predict overall survival (OS) and progression-free survival (PFS) in patients with advanced non-small-cell lung cancer (NSCLC) treated with first line Pembrolizumab. Twenty-one patients with NSLC were prospectively enrolled; they underwent contrast enhanced CT (CECT) at baseline and during Pembrolizumab treatment. Response to therapy was assessed both with clinical and iRECIST criteria. Two radiologists drew a volume of interest of the tumor at baseline CECT, extracting several texture parameters. ROC curves, a univariate Kaplan-Meyer analysis and Cox proportional analysis were performed to evaluate the prognostic value of texture analysis. Twelve (57%) patients showed partial response to therapy while nine (43%) had confirmed progressive disease. Among texture parameters, mean value of positive pixels (MPP) at fine and medium filters showed an AUC of 72% and 74% respectively (P < 0.001). Kaplan-Meyer analysis showed that MPP < 56.2 were significantly associated with lower OS and PFS (P < 0.0035). Cox proportional analysis showed a significant correlation between MPP4 and OS (P = 0.0038; HR = 0.89[CI 95%:0.83,0.96]). In conclusion, MPP could be used as predictive imaging biomarkers of OS and PFS in patients with NSLC with first line immune treatment