Non-secretory multiple myeloma expressed as multiple extramedullary plasmacytoma with an endobronchial lesion mimicking metastatic cancer: A case report.

BACKGROUND: Non-secretory multiple myeloma (MM) is a rare condition that accounts for only 3% of MM cases and is defined by normal serum and urine immunofixation and a normal serum free light chain ratio. Non-secretory MM with multiple extramedullary plasmacytomas derived from endobronchial lesions is extremely rare and can be misdiagnosed as metastasis of solid cancer. CASE SUMMARY: A 36-year-old man presented with progressive facial swelling and nasal congestion with cough. Various imaging studies revealed an endobronchial mass in the left bronchus and a large left maxillary mass with multiple destructive bone metastatic lesions. He initially presented with lung cancer and multiple metastases. However, pathologic reports showed multiple extramedullary plasmacytomas in the left maxilla and the left bronchus. There was no change in the serum and urine monoclonal protein levels, and no abnormalities were observed in laboratory examinations, including hemoglobin, calcium, and creatinine levels. The bone marrow was hypercellular, with 13.49% plasma cells. The patient was diagnosed with non-secretory MM expressed as multiple extramedullary plasmacytomas with endobronchial lesions in a rare location. Radiation therapy for symptomatic lesions with high-dose dexamethasone was started, and the size of the left maxillary sinus lesion dramatically decreased. In the future, chemotherapy will be administered to control lesions in other areas. CONCLUSION: We present a rare case of non-secretory MM with multiple extramedullary plasmacytoma with an endobronchial lesion.

Extraction of metastasis hotspots in a whole-body bone scintigram based on bilateral asymmetry.

PURPOSE: A hotspot of bone metastatic lesion in a whole-body bone scintigram is often observed as left-right asymmetry. The purpose of this study is to present a network to evaluate bilateral difference of a whole-body bone scintigram, and to subsequently integrate it with our previous network that extracts the hotspot from a pair of anterior and posterior images. METHODS: Input of the proposed network is a pair of scintigrams that are the original one and the flipped version with respect to body axis. The paired scintigrams are processed by a butterfly-type network (BtrflyNet). Subsequently, the output of the network is combined with the output of another BtrflyNet for a pair of anterior and posterior scintigrams by employing a convolutional layer optimized using training images. RESULTS: We evaluated the performance of the combined networks, which comprised two BtrflyNets followed by a convolutional layer for integration, in terms of accuracy of hotspot extraction using 1330 bone scintigrams of 665 patients with prostate cancer. A threefold cross-validation experiment showed that the number of false positive regions was reduced from 4.30 to 2.13 for anterior and 4.71 to 2.62 for posterior scintigrams on average compared with our previous model. CONCLUSIONS: This study presented a network for hotspot extraction of bone metastatic lesion that evaluates bilateral difference of a whole-body bone scintigram. When combining the network with the previous network that extracts the hotspot from a pair of anterior and posterior scintigrams, the false positives were reduced by nearly half compared to our previous model.

Automated measurement of bone scan index from a whole-body bone scintigram.

PURPOSE: We propose a deep learning-based image interpretation system for skeleton segmentation and extraction of hot spots of bone metastatic lesion from a whole-body bone scintigram followed by automated measurement of a bone scan index (BSI), which will be clinically useful. METHODS: The proposed system employs butterfly-type networks (BtrflyNets) for skeleton segmentation and extraction of hot spots of bone metastatic lesions, in which a pair of anterior and posterior images are processed simultaneously. BSI is then measured using the segmented bones and extracted hot spots. To further improve the networks, deep supervision (DSV) and residual learning technologies were introduced. RESULTS: We evaluated the performance of the proposed system using 246 bone scintigrams of prostate cancer in terms of accuracy of skeleton segmentation, hot spot extraction, and BSI measurement, as well as computational cost. In a threefold cross-validation experiment, the best performance was achieved by BtrflyNet with DSV for skeleton segmentation and BtrflyNet with residual blocks. The cross-correlation between the measured and true BSI was 0.9337, and the computational time for a case was 112.0 s. CONCLUSION: We proposed a deep learning-based BSI measurement system for a whole-body bone scintigram and proved its effectiveness by threefold cross-validation study using 246 whole-body bone scintigrams. The automatically measured BSI and computational time for a case are deemed clinically acceptable and reliable.