Apc-mutant cells act as supercompetitors in intestinal tumour initiation.

A delicate equilibrium of WNT agonists and antagonists in the intestinal stem cell (ISC) niche is critical to maintaining the ISC compartment, as it accommodates the rapid renewal of the gut lining. Disruption of this balance by mutations in the tumour suppressor gene APC, which are found in approximately 80% of all human colon cancers, leads to unrestrained activation of the WNT pathway1,2. It has previously been established that Apc-mutant cells have a competitive advantage over wild-type ISCs3. Consequently, Apc-mutant ISCs frequently outcompete all wild-type stem cells within a crypt, thereby reaching clonal fixation in the tissue and initiating cancer formation. However, whether the increased relative fitness of Apc-mutant ISCs involves only cell-intrinsic features or whether Apc mutants are actively involved in the elimination of their wild-type neighbours remains unresolved. Here we show that Apc-mutant ISCs function as bona fide supercompetitors by secreting WNT antagonists, thereby inducing differentiation of neighbouring wild-type ISCs. Lithium chloride prevented the expansion of Apc-mutant clones and the formation of adenomas by rendering wild-type ISCs insensitive to WNT antagonists through downstream activation of WNT by inhibition of GSK3ß. Our work suggests that boosting the fitness of healthy cells to limit the expansion of pre-malignant clones may be a powerful strategy to limit the formation of cancers in high-risk individuals.

Routine MRI Among Patients With a Suspected Scaphoid Fracture Risks Overdiagnosis.

BACKGROUND: In the setting of a suspected scaphoid fracture, MRI may result in overdiagnosis and potential overtreatment. This is in part because of the low prevalence of true fractures among suspected fractures, but also because of potentially misleading variations in signal that may be more common than fracture-related signal changes. To better understand the risk of overdiagnosis, we first need insight into the relative prevalence of useful and potentially distracting signal changes among patients with a suspected scaphoid fracture. QUESTION/PURPOSE: What is the proportion of signal changes representing definite and possible scaphoid fractures relative to other types of signal changes on MRI among patients with a suspected scaphoid fracture? METHODS: In a retrospective study in an orthopaedic trauma clinic associated with a Level I trauma center, we evaluated MR images of patients 16 years and older with a clinically suspected scaphoid fracture. At our institution, patients with symptoms and signs of a possible scaphoid fracture and negative radiographs undergo MRI scanning. Between January 1, 2012, and September 1, 2019, a total of 310 patients 16 years or older had an MRI to evaluate a suspected scaphoid fracture. Exclusion criteria included a scaphoid fracture that was visible on radiographs before MRI as reported by the radiologist (four patients), no available radiographs before MRI (two), MRI more than 3 weeks after injury (28), unknown date of injury (nine), and repeat or bilateral MRI scans (11), leaving 256 MR images for analysis. Sixty percent (153) of patients were women, and the median age was 34 years (IQR 21 to 50 years). The images were taken a median of 8 days (IQR 2 to 12 days) after injury. MR images were screened for the presence of scaphoid signal changes. We identified the following patterns of signal change with a reliability of kappa 0.62: definite scaphoid fracture, possible scaphoid fracture, signal in the waist area other than possible or definite fractures, and other signal changes. A definite scaphoid fracture was defined as a linear, focal, and bicortical signal abnormality, with adjacent edema and a relatively transverse orientation relative to the scaphoid long axis. The transverse linear signal was visible on more than one cut in multiple planes. A possible scaphoid fracture had a transverse linear signal on more than one cut on sagittal or coronal planes, with or without adjacent edema. RESULTS: Six percent (16 of 256) of MR images were categorized as revealing definite (2% [four of 256]) or possible (5% [12 of 256]) scaphoid fractures, whereas 29% (74 of 256) were categorized as revealing nonspecific signal changes at the waist (14% [35 of 256]) and other areas (15% [39 of 256]). Of the 51 patients with scaphoid waist signal changes, 69% (35) were categorized as having distracting and potentially misleading MRI findings. CONCLUSION: The high prevalence of signal changes that are distracting and potentially misleading, the low prevalence of signal changes that clearly represent a scaphoid fracture, and the low pretest odds of a true fracture among patients with a suspected scaphoid fracture illustrate that routine MRI of suspected scaphoid fractures carries a notable risk of overdiagnosis and potential overtreatment. Two alternative strategies are supported by preliminary evidence and merit additional attention: more-selective use of MRI in people deemed at higher risk according to a clinical prediction rule and strategies for involving the patient in decisions regarding how to manage the notably small risk of future symptomatic nonunion. LEVEL OF EVIDENCE: Level IV, diagnostic study.

CNN Attention Guidance for Improved Orthopedics Radiographic Fracture Classification.

Convolutional neural networks (CNNs) have gained significant popularity in orthopedic imaging in recent years due to their ability to solve fracture classification problems. A common criticism of CNNs is their opaque learning and reasoning process, making it difficult to trust machine diagnosis and the subsequent adoption of such algorithms in clinical setting. This is especially true when the CNN is trained with limited amount of medical data, which is a common issue as curating sufficiently large amount of annotated medical imaging data is a long and costly process. While interest has been devoted to explaining CNN learnt knowledge by visualizing network attention, the utilization of the visualized attention to improve network learning has been rarely investigated. This paper explores the effectiveness of regularizing CNN network with human-provided attention guidance on where in the image the network should look for answering clues. On two orthopedics radiographic fracture classification datasets, through extensive experiments we demonstrate that explicit human-guided attention indeed can direct correct network attention and consequently significantly improve classification performance. The development code for the proposed attention guidance is publicly available on https://github.com/zhibinliao89/fracture_attention_guidance.