A 60-Year-Old Man with a 34-Year History of Chronic Exertional Compartment Syndrome and 3 Previous Surgical Fasciotomies, Successfully Treated with Injection of Botulinum Toxin.

BACKGROUND Patients with post-fasciotomy CECS recurrence can experience significant mobility issues at baseline that limit independent living. For these patients, a repeat fasciotomy is not ideal because they are older and post-surgical scar tissue will make the fasciotomy technically challenging. Therefore, post-fasciotomy patients with CECS recurrence require new, non-surgical treatment options. Recent studies show botulinum toxin injections can be effective for the initial management of chronic exertional compartment syndrome (CECS) prior to surgery, especially in young patients primarily experiencing pain on exertion with minimal lower-extremity symptoms at rest. However, the ability to treat CECS recurrence status after fasciotomy with botulinum toxin injections of the legs has not been studied. CASE REPORT We present the first case where botulinum toxin was applied to this patient population. Our patient was a 60-year-old man with a 34-year history of CECS who, 8 years after his third bilateral fasciotomy, progressively developed rest pain in his calves bilaterally, paresthesias, and difficulties when walking or descending stairs, with multiple near-falls due to his toes catching on stair steps. OnabotulinumtoxinA (BTX-A) injections into the posterior and lateral compartments resolved baseline symptoms: within 2 weeks, he was able to walk, negotiate stairs symptom-free, and enjoy an overseas vacation without complications. CONCLUSIONS Symptoms related to recurrent CECS status after multiple fasciotomies can successfully be treated with BTX-A injections. Our patient's baseline mobility issues resolved within 2 weeks after the injection and remained that way for over 31 months. However, his exertional symptoms and rest pain recurred at 9 months, suggesting that BTX-A injections are not completely curative.

Erratum: Prostate cancer detection from multi-institution multiparametric MRIs using deep convolutional neural networks system (Erratum).

[This corrects the article DOI: 10.1117/1.JMI.5.4.044507.].

Machine learning to predict lung nodule biopsy method using CT image features: A pilot study.

Computed tomography (CT)-based screening on lung cancer mortality is poised to make lung nodule management a growing public health problem. Biopsy and pathologic analysis of suspicious nodules is necessary to ensure accurate diagnosis and appropriate intervention. Biopsy techniques vary as do the specialists that perform them and the ways lung nodule patients are referred and triaged. The largest dichotomy is between minimally invasive biopsy (MIB) and surgical biopsy (SB). Cases of unsuccessful MIB preceding a SB can result in considerable delay in definitive care with potentially an adverse impact on prognosis besides potentially avoidable healthcare expenditures. An automated method that predicts the optimal biopsy method for a given lung nodule could save time and healthcare costs by facilitating referral and triage patterns. To our knowledge, no such method has been published. Here, we used CT image features and radiologist-annotated semantic features to predict successful MIB in a way that has not been described before. Using data from the Lung Image Database Consortium image collection (LIDC-IDRI), we trained a logistic regression model to determine whether a MIB or SB procedure was used to diagnose lung cancer in a patient presenting with lung nodules. We found that in successful MIB cases, the nodules were significantly larger and more spiculated. Our model illustrates that using robust machine learning tools on easily accessible semantic and image data can predict whether a patient's nodule is best biopsied by MIB or SB. Pending further validation and optimization, clinicians could use our publicly accessible model to aid clinical decision-making.

Prostate cancer detection from multi-institution multiparametric MRIs using deep convolutional neural networks.

Multiparametric magnetic resonance imaging (mpMRI) of the prostate aids in early diagnosis of prostate cancer, but is difficult to interpret and subject to interreader variability. Our objective is to generate probability maps, overlaid on original mpMRI images to help radiologists identify where a cancer is suspected as a computer-aided diagnostic (CAD). We optimized the holistically nested edge detection (HED) deep convolutional neural network. Our dataset contains T2, apparent diffusion coefficient, and high b -value images from 186 patients across six institutions worldwide: 92 with an endorectal coil (ERC) and 94 without. Ground-truth was based on tumor segmentations manually drawn by expert radiologists based on histologic evidence of cancer. The training set consisted of 120 patients and the validation set and test set included 19 and 47, respectively. Slice-level probability maps are evaluated at the lesion level of analysis. The best model: HED using 5 × 5 convolutional kernels, batch normalization, and optimized using Adam. This CAD performed significantly better ( p < 0.001 ) in the peripheral zone ( AUC = 0.94 ± 0.01 ) than the transition zone. It outperforms a previous CAD from our group in a head-to-head comparison on the same ERC-only test cases ( AUC = 0.97 ± 0.01 ; p < 0.001 ). Our CAD establishes a state-of-the-art performance for predicting prostate cancer lesions on mpMRIs.