Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds—urging the quest for predictive biomarkers. We hypothesize that artificial intelligence (AI) algorithms can automatically quantify radiographic characteristics that are related to and may therefore act as noninvasive radiomic biomarkers for immunotherapy response. In this study, we analyzed 1055 primary and metastatic lesions from 203 patients with advanced melanoma and non-small-cell lung cancer (NSCLC) undergoing anti-PD1 therapy. We carried out an AI-based characterization of each lesion on the pretreatment contrast-enhanced CT imaging data to develop and validate a noninvasive machine learning biomarker capable of distinguishing between immunotherapy responding and nonresponding. To define the biological basis of the radiographic biomarker, we carried out gene set enrichment analysis in an independent dataset of 262 NSCLC patients. The biomarker reached significant performance on NSCLC lesions (up to 0.83 AUC, P < 0.001) and borderline significant for melanoma lymph nodes (0.64 AUC, P = 0.05). Combining these lesion-wide predictions on a patient level, immunotherapy response could be predicted with an AUC of up to 0.76 for both cancer types (P < 0.001), resulting in a 1-year survival difference of 24% (P = 0.02). We found highly significant associations with pathways involved in mitosis, indicating a relationship between increased proliferative potential and preferential response to immunotherapy. These results indicate that radiographic characteristics of lesions on standard-of-care imaging may function as noninvasive biomarkers for response to immunotherapy, and may show utility for improved patient stratification in both neoadjuvant and palliative settings.

BACKGROUND: Diffusion-weighted imaging is increasingly used in rectal cancer MRI to assess response after chemoradiotherapy. Certain pitfalls (eg, artefacts) may hamper diffusion–MRI assessment, leading to suboptimal diagnostic performance. Combining diffusion-weighted MRI with the underlying morphology on standard (T2-weighted) MRI may help overcome these pitfalls. OBJECTIVE: The purpose of this study was to evaluate the diagnostic performance of a pattern-based approach combining tumor morphology on T2-weighted MRI with distinct diffusion-weighted imaging signal patterns to assess response after chemoradiotherapy in rectal cancer. DESIGN: Response to chemoradiotherapy was scored according to 4 patterns: 1) cases with either a clear residual mass with corresponding high-diffusion signal (A+) or completely normalized wall without diffusion signal (A–); 2) cases with circular and/or irregular fibrosis with (B+) or without (B–) small foci of diffusion signal scattered throughout the fibrosis; 3) cases with semicircular fibrosis with (C+) or without (C–) high diffusion signal at the inner margin of the fibrosis; and 4) polypoid tumors showing regression of the polyp and fibrosis at the site of the stalk with (D+) or without (D–) focal high-diffusion signal in the stalk. A total of 75 cases were rescored by an independent second reader to study interobserver variations. Standard of reference was histopathology or long-term outcome. SETTINGS: The study was conducted at a single tertiary referral center. PATIENTS: A total of 222 patients with locally advanced rectal cancer undergoing chemoradiotherapy were included. MAIN OUTCOME MEASURES: Diagnostic performance to discriminate between a complete response and residual tumor was measured. RESULTS: The pattern-based approach resulted in a sensitivity of 94%, specificity of 77%, positive predictive value of 88%, negative predictive value of 87%, and overall accuracy of 88% to differentiate between tumor versus complete response. Accuracies per pattern were 100% (A), 74% (B), 86% (C), and 92% (D). Interobserver agreement was good (κ = 0.75). LIMITATIONS: The study included no comparison with routine (nonpattern) diffusion–MRI assessment. CONCLUSIONS: A pattern-based approach combining tumor morphology with distinct diffusion-weighted imaging patterns results in good diagnostic performance to assess response. See Video Abstract at http://links.lww.com/DCR/A433.

Objectives: (1) To investigate whether a contrast-free biparametric MRI (bp-MRI) including T2-weighted images (T2W) and diffusion-weighted images (DWI) can be considered an accurate alternative to the standard multiparametric MRI (mp-MRI), consisting of T2, DWI, and dynamic contrast-enhanced (DCE) imaging for the muscle-invasiveness assessment of bladder cancer (BC), and (2) to evaluate how the diagnostic performance of differently experienced readers is affected according to the type of MRI protocol. Methods: Thirty-eight patients who underwent a clinically indicated bladder mp-MRI on a 3-T scanner were prospectively enrolled. Trans-urethral resection of bladder was the gold standard. Two sets of images, set 1 (bp-MRI) and set 2 (mp-MRI), were independently reviewed by four readers. Descriptive statistics, including sensitivity and specificity, were calculated for each reader. Receiver operating characteristic (ROC) analysis was performed, and the areas under the curve (AUCs) were calculated for the bp-MRI and the standard mp-MRI. Pairwise comparison of the ROC curves was performed. Results: The AUCs for bp- and mp-MRI were respectively 0.91–0.92 (reader 1), 0.90 (reader 2), 0.95–0.90 (reader 3), and 0.90–0.87 (reader 4). Sensitivity was 100% for both protocols and specificity ranged between 79.31 and 89.66% and between 79.31 and 83.33% for bp-MRI and mp-MRI, respectively. No significant differences were shown between the two MRI protocols (p > 0.05). No significant differences were shown accordingly to the reader’s experience (p > 0.05). Conclusions: A bp-MRI protocol consisting of T2W and DWI has comparable diagnostic accuracy to the standard mp-MRI protocol for the detection of muscle-invasive bladder cancer. The experience of the reader does not significantly affect the diagnostic performance using VI-RADS. Key Points: • The contrast-free MRI protocol shows a comparable accuracy to the standard multiparametric MRI protocol in the bladder cancer muscle-invasiveness assessment. • VI-RADS classification helps non-expert radiologists to assess the muscle-invasiveness of bladder cancer. • DCE should be carefully interpreted by less experienced readers due to inflammatory changes representing a potential pitfall.

Ultrasound imaging is widely used to evaluate the neuromusculoskeletal system, and recently, a particular interest is mounting in assessing the bone tissue and fractures. Ultrasound can be considered a valuable diagnostic tool to perform a first-line evaluation of bone tissue, especially in particular settings without direct access to X-ray imaging and/or in emergency conditions. Moreover, different healing phases of bone fractures can be accurately assessed by combining the B-mode modality and (high-sensitive) color/power Doppler optimizing the management of patients—e.g., planning of progressive loads and rehabilitation procedures. In this review, we summarized the role of ultrasound imaging in the management of bone fractures and described the most common sonographic signs encountered in the daily practice by assessing different types of bone fractures and the progressive phases of the healing process.