Pickhardt, Perry J.

Abstract Background Experts' interpretations of medical images for lesion diagnosis may not always align with the underlying in vivo tissue pathology and, therefore, cannot be considered the definitive truth regarding malignancy or benignity. While current machine learning (ML) models in medical imaging can replicate expert interpretations, their results may also diverge from the actual ground truth. Purpose This study investigates various factors contributing to these discrepancies and proposes solutions. Methods The central idea of the proposed solution is to integrate prior knowledge into ML models to enhance the characterization of in vivo tissues. The incorporation of prior knowledge into decision‐making is task‐specific, tailored to the data acquired for that task. This central idea was tested on the diagnosis of lesions using low dose computed tomography (LdCT) for early cancer detection, particularly focusing on more challenging, ambiguous or indeterminate lesions (IDLs) as classified by experts. One key piece of prior knowledge involves CT x‐ray energy spectrum, where different energies interact with in vivo tissues within a lesion, producing variable but reproducible image contrasts that encapsulate biological information. Typically, CT imaging devices use only the high‐energy portion of this spectrum for data acquisition; however, this study considers the full spectrum for lesion diagnostics. Another critical aspect of prior knowledge includes the functional or dynamic properties of in vivo tissues, such as elasticity, which can indicate pathological conditions. Instead of relying solely on abstract image features as current ML models do, this study extracts these tissue pathological characteristics from the image contrast variations. Results The method was tested on LdCT images of four sets of IDLs, including pulmonary nodules and colorectal polyps, with pathological reports serving as the ground truth for malignancy or benignity. The method achieved an area under the receiver operating characteristic curve (AUC) of 0.98 ± 0.03, demonstrating a significant improvement over existing state‐of‐the‐art ML models, which typically have AUCs in the 0.70 range. Conclusion Leveraging prior knowledge in machine intelligence can enhance lesion diagnosis, resolve the ambiguity of IDLs interpreted by experts, and improve the effectiveness of LdCT screening for early‐stage cancers.

Intraoperative ultrasound (IOUS) is a valuable adjunctive tool that can provide real-time diagnostic information in surgery that has the potential to alter patient management and decrease complications. Lesion localization, characterization and staging can be performed, as well as surveying for additional lesions and metastatic disease. IOUS is commonly used in the liver for hepatic metastatic disease and hepatocellular carcinoma, in the pancreas for neuroendocrine tumors, and in the kidney for renal cell carcinoma. IOUS allows real-time evaluation of vascular patency and perfusion in organ transplantation and allows for early intervention for anastomotic complications. It can also be used to guide intraoperative procedures such as biopsy, fiducial placement, radiation, or ablation. A variety of adjuncts including microbubble contrast and elastography may provide additional information at IOUS. It is important for the radiologist to be familiar with the available equipment, common clinical indications, technique, relevant anatomy and intraoperative imaging appearance to optimize performance of this valuable imaging modality.

Fournier gangrene (FG) is a genitourinary necrotizing fasciitis that can be lethal if not promptly diagnosed and surgically debrided. The diagnosis is often made by physical examination paired with an appropriate clinical suspicion and supporting laboratory values. Imaging, particularly computed tomography (CT), plays a role in delineating involved fascial planes for operative debridement and occasionally in diagnosing FG. Less commonly, the imaging manifestations of FG may also be seen on ultrasound, radiographs, and magnetic resonance imaging. With the ubiquitous use and availability of CT, radiologists have a growing role in recognizing FG. This can be challenging in the absence of fascial gas, but a CT scoring system for necrotizing fasciitis can be helpful in making the diagnosis. Recent series suggest that this predominantly male disease has a rising incidence in women. Women with FG are more likely to be morbidly obese and have vulvar or labial involvement compared to men. Imaging mimics include ulcerative and necrotic tumors, traumatic or iatrogenic fascial gas, and vaginitis emphysematosa. The purpose of this pictorial review is to illustrate the imaging manifestations of FG and its mimics, with emphases on necrotizing fasciitis CT scoring systems and FG in women.