Improved detection of bone metastases from lung cancer in the thoracic cage using 5- and 1-mm axial images versus a new CT software generating rib unfolding images: comparison with standard ¹⁸F-FDG-PET/CT

Value of Clinical Information on Radiology Reports in Oncological Imaging

Radiological reporting errors have a direct negative impact on patient treatment. The purpose of this study was to investigate the contribution of clinical information (CI) in radiological reporting of oncological imaging and the dependence on the radiologists’ experience level (EL). Sixty-four patients with several types of carcinomas and twenty patients without tumors were enrolled. Computed tomography datasets acquired in primary or follow-up staging were independently analyzed by three radiologists (R) with different EL (R1: 15 years; R2: 10 years, R3: 1 year). Reading was initially performed without and 3 months later with CI. Overall, diagnostic accuracy and sensitivity for primary tumor detection increased significantly when receiving CI from 77% to 87%; p = 0.01 and 73% to 83%; p = 0.01, respectively. All radiologists benefitted from CI; R1: 85% vs. 92%, p = 0.15; R2: 77% vs. 83%, p = 0.33; R3: 70% vs. 86%, p = 0.02. Overall, diagnostic accuracy and sensitivity for detecting lymphogenous metastases increased from 80% to 85% (p = 0.13) and 42% to 56% (p = 0.13), for detection of hematogenous metastases from 85% to 86% (p = 0.61) and 46% to 60% (p = 0.15). Specificity remained stable (>90%). Thus, CI in oncological imaging seems to be essential for correct radiological reporting, especially for residents, and should be available for the radiologist whenever possible.

A Neural Network and Optimization Based Lung Cancer Detection System in CT Images

One of the most common causes of death from cancer for both women and men is lung cancer. Lung nodules are critical for the screening of cancer and early recognition permits treatment and enhances the rate of rehabilitation in patients. Although a lot of work is being done in this area, an increase in accuracy is still required to swell patient persistence rate. However, traditional systems do not segment cancer cells of different forms accurately and no system attained greater reliability. An effective screening procedure is proposed in this work to not only identify lung cancer lesions rapidly but to increase accuracy. In this procedure, Otsu thresholding segmentation is utilized to accomplish perfect isolation of the selected area, and the cuckoo search algorithm is utilized to define the best characteristics for partitioning cancer nodules. By using a local binary pattern, the relevant features of the lesion are retrieved. The CNN classifier is designed to spot whether a lung lesion is malicious or non-malicious based on the retrieved features. The proposed framework achieves an accuracy of 96.97% percent. The recommended study reveals that accuracy is improved, and the results are compiled using Particle swarm optimization and genetic algorithms.

Establishment of a regression model of bone metabolism markers for the diagnosis of bone metastases in lung cancer

Background

The aim of this study was to establish a regression equation model of serum bone metabolism markers. We analyzed the diagnostic value of bone metastases in lung cancer and provided laboratory evidence for the early clinical treatment of bone metastases in lung cancer.

Methods

A total of 339 patients with non-metastatic lung cancer, patients with lung cancer with bone metastasis, and patients with benign lung disease who were treated in our hospital from July 2012 to October 2015 were included. A total of 103 patients with lung cancer in the non-metastatic group, 128 patients with lung cancer combined with bone metastasis group, and 108 patients with benign lung diseases who had nontumor and nonbone metabolism-related diseases were selected as the control group. Detection and analysis of type I collagen carboxyl terminal peptide β-special sequence (β-CTX), total type I procollagen amino terminal propeptide (TPINP), N-terminal-mid fragment of osteocalcin (N-MID), parathyroid hormone (PTH), vitamin D (VitD3), alkaline phosphatase (ALP), calcium (CA), phosphorus (P), cytokeratin 19 fragment (F211), and other indicators were performed. Four multiple regression models were established to determine the best diagnostic model for lung cancer with bone metastasis.

Results

Analysis of single indicators of bone metabolism markers in lung cancer was performed, among which F211, β-CTX, TPINP, and ALP were significantly different (P < 0.05). The ROC curve of each indicator was less than 0.712. Based on the multiple regression models, the fourth model was the best and was much better than a single indicator with an AUC of 0.856, a sensitivity of 70.0%, a specificity of 91.0%, a positive predictive value of 82.5%, and a negative predictive value of 72.0%.

Conclusion

Multiple regression models of bone metabolism markers were established. These models can be used to evaluate the progression of lung cancer and provide a basis for the early treatment of bone metastases.

Added Value of CT Pelvic Bone Unfolding Software to Radiologist Performance in Detecting Osteoblastic Pelvic Bone Lesions in Patients With Prostate Cancer

PURPOSE

To evaluate the contribution of CT Bone Unfolding software to the diagnostic accuracy and efficiency for the detection of osteoblastic pelvic bone lesions in patients with prostate cancer.

METHODS

A total of 102 consecutive (January 2016-September 2019) patients who underwent abdominopelvic CT with prostate cancer were retrospectively evaluated for osteoblastic pelvic bone lesions, using commercially available the post-processing-pelvic bone flattening-image software package "CT Bone Unfolding." Two radiologists with 3 and 15 years of experience in abdominal radiology evaluated CT image data sets independently in 2 separate reading sessions. At the first session, only MPR images and at the second session MPR images and additionally unfolded reconstructions were assessed. Reading time for each patient was noted. A radiologist with 25 years of experience, established the standard of reference.

RESULTS

In the evaluations performed with the MPR-Unfold method, the diagnostic accuracy were found to be 2.067 times higher compared to the MPRs method (P < 0.001). The location of the lesions or the reader variabilities did not show any influence on accuracy (P > 0.05) For all readers the reading time for MPR was significantly longer than for MPR-Unfold (P < 0.05). For both methods substantial to almost-perfect inter-reader agreement was found (0.686-0.936).

CONCLUSIONS

The use of unfolded pelvic bone reconstructions increases diagnostic accuracy while decreasing the reading times in the evaluation of pelvic bone lesions. Therefore, our findings suggest that utilizing unfolded reconstructions in addition to MPR images may be preferable in patients with prostate cancer for the screening of osteoblastic pelvic bone lesions.

Automatic rib unfolding in postmortem computed tomography: diagnostic evaluation of the OpenRib software compared with the autopsy in the detection of rib fractures

OBJECTIVES

The main objective of this study was to evaluate the diagnostic performance of the OpenRib software against the gold standard of autopsy in the detection of rib fractures. The secondary objective was to measure inter-rater agreement between each radiological reader.

MATERIALS AND METHODS

Thirty-six subjects who underwent postmortem CT and autopsy were included in this study. Rib fractures were first assessed during the autopsy by carefully dissecting and examining each rib. They were also independently evaluated by three readers using OpenRib software. This software produces from postmortem CT images a reformat of the rib cage and a display of all ribs in a single plane. Each reader was asked to determine if the rib was fractured and, if so, whether the fracture was single or multiple.

RESULTS

After exclusions, 649 ribs were included in the statistical analysis. The two readers with a similar level of experience showed a satisfactory inter-rater agreement and a sensitivity of 0.73 and 0.83 with a specificity of 0.95 and 0.91. However, the experienced reader diagnosed significantly more fractures than the autopsy and the other two readers (p < 0.001).

CONCLUSION

The use of automatic rib unfolding software in postmortem CT allows an efficient and accurate assessment of rib fractures and enables the diagnosis of fractures that cannot be detected during a standard autopsy. For now, this method seems to be the simplest that can be routinely performed; however, it requires training time in order to be sufficiently effective.

Automated "Bone Subtraction" Image Analysis Software Package for Improved and Faster CT Monitoring of Longitudinal Spine Involvement in Patients with Multiple Myeloma

RATIONALE AND OBJECTIVES

The study aimed to assess the diagnostic benefit of a novel computed tomography (CT) post-processing software generating subtraction maps of longitudinal non-enhanced CT examinations for monitoring the course of myeloma bone disease in the spine.

MATERIALS AND METHODS

The local institutional review board approved the retrospective data evaluation. Included were 82 consecutive myeloma patients (46 male; mean age, 65.08 ± 9.76) who underwent 188 repeated whole-body reduced-dose Multislice Detector Computed Tomography (MDCT) at our institution between December 2013 and January 2016. Lytic bone lesions were categorized as new or enlarging versus stable. Bone subtraction maps were read in combination with corresponding 1-mm source images comparing results to those of standard image reading of 5-mm axial and 2-mm multiplanar reformat reconstructions (MPR) scans and hematologic markers, and classified as either progressive disease (PD) or stable disease (SD or remission). The standard of reference was 1-mm axial CT image reading + hematologic response both confirmed at follow-up. For statistical purposes, we subgrouped the hematologic response categories similarly to those applied for CT imaging (progression vs stable/response).

RESULTS

According to the standard of reference, 16 patients experienced PD and 66 SD at follow-up. Th sensitivity, specificity, and accuracy for axial 5 mm + 2 mm MPR image versus bone subtraction maps in a "lesion-by-lesion" reading were 97.6%, 92.3%, and 97.2% versus 97.8%, 96.7%, and 97.7%, respectively. The use of bone subtraction maps resulted in a change of response classification in 9.7% of the patients (n = 8) versus 5 mm + 2 mm MPR image reading from SD to PD. Bone sclerosis lesions were detected in 52 out of 82 patients (63.4%). The reading time was significantly lower with the software bone subtraction compared to standard reading (P < 0.01) and 1-mm image reading (P < 0.001).

CONCLUSION

Accuracy of bone subtraction maps reading for monitoring multiple myeloma is slightly increased over that of conventional axial + MPR image reading and significantly speeds up the reading time.

Effect of Bone Reading CT software on radiologist performance in detecting bone metastases from breast cancer

OBJECTIVE

To evaluate the effect of CT software that generates rib unfolding images and automatically numbers ribs and thoracic spines on radiologist performance in detecting thoracic bone metastases from breast cancer.

METHODS

A total of 126 patients with breast cancer who underwent chest CT and fludeoxyglucose (FDG)-positron emission tomography (PET)/CT and/or bone scans were retrospectively reviewed. One board-certified radiologist (R1) and one radiology resident (R2) independently assessed the original chest CT and rib unfolding images using a commercially available post-processing software (Bone Reading) application to evaluate metastasis in the ribs and thoracic spines. Results were compared with reference standard based on CT, FDG-PET/CT and/or bone scan with follow-up.

RESULTS

Based on reference standard, 78 metastatic bone lesions in 26 patients were identified. On per-patient-based analysis, Bone Reading assessed by R1/R2 had a sensitivity of 84.6%/80.8% and a specificity of 94.0%/94.0% with an accuracy of 92.1%/91.3%. The original CT reading yielded a sensitivity of 73.1%/57.7% and a specificity of 95.0%/94.0% with an accuracy of 90.5%/86.5%. The sensitivity and accuracy of Bone Reading were significantly higher than those of CT reading, as assessed by R2 (both p = 0.031). On per-lesion-based analysis, Bone Reading assessed by R1/R2 yielded a sensitivity of 84.6%/82.1% and a specificity of 99.7%/99.6% with an accuracy of 99.4%/99.3%, while the original CT reading yielded a sensitivity of 71.8%/62.8% and a specificity of 99.6%/99.5% with an accuracy of 99.2%/98.9%. The sensitivity and accuracy with Bone Reading application were significantly higher than those with CT reading by both readers (R1, p = 0.006 and p = 0.036, respectively; R2, both p < 0.001). The mean reading time needed for Bone Reading application was significantly shorter than that for original chest CT reading (p < 0.001). Bone Reading application helped readers find small and sclerotic lesions missed in original CT reading.

CONCLUSION

In patients with breast cancer, the use of Bone Reading application improved radiologist performance in bone metastasis detection compared with original chest CT reading with reduced reading time. This software will be more helpful to inexperienced radiologists for improving the reading performance. Advances in knowledge: Small and sclerotic lesions can be easily missed in original CT reading. Using Bone Reading CT software can enhance the performance of radiologists in detecting bone metastasis in breast cancer. False-negative rates can be significantly reduced in both inexperienced and experienced readers.

Improved MDCT monitoring of pelvic myeloma bone disease through the use of a novel longitudinal bone subtraction post-processing algorithm

PURPOSE

To evaluate the diagnostic performance of a novel CT post-processing software that generates subtraction maps of baseline and follow-up CT examinations in the course of myeloma bone lesions.

MATERIALS AND METHODS

This study included 61 consecutive myeloma patients who underwent repeated whole-body reduced-dose MDCT at our institution between November 2013 and June 2015. CT subtraction maps classified a progressive disease (PD) vs. stable disease (SD)/remission. Bone subtraction maps (BSMs) only and in combination with 1-mm (BSM+) source images were compared with 5-mm axial/MPR scans.

RESULTS

Haematological response categories at follow-up were: complete remission (n = 9), very good partial remission (n = 2), partial remission (n = 17) and SDh (n = 19) vs. PDh (n = 14). Five-millimetre CT scan yielded PD (n = 14) and SD/remission (n = 47) whereas bone subtraction + 1-mm axial scans (BSM+) reading resulted in PD (n = 18) and SD/remission (n = 43). Sensitivity/ specificity/accuracy for 5-mm/1-mm/BSM(alone)/BSM + in "lesion-by-lesion" reading was 89.4 %/98.9 %/98.3 %/ 99.5 %; 69.1 %/96.9 %/72 %/92.1 % and 83.8 %/98.4 %/92.1 %/98.3 %, respectively. The use of BSM+ resulted in a change of response classification in 9.8 % patients (n = 6) from SD to PD.

CONCLUSION

BSM reading is more accurate for monitoring myeloma compared to axial scans whereas BSM+ yields similar results with 1-mm reading (gold standard) but by significantly reduced reading time.

KEY POINTS

• CT evaluation of myeloma bone disease using a longitudinal bone subtraction post-processing algorithm. • Bone subtraction post-processing algorithm is more accurate for assessment of therapy. • Bone subtraction allowed improved and more efficient detection of myeloma bone lesions. • Post-processing tool demonstrating a change in response classification in 9.8 % patients (all showing PD). • Reading time could be substantially shortened as compared to regular CT assessment.

Improvement of diagnostic confidence for detection of multiple myeloma involvement of the ribs by a new CT software generating rib unfolded images: Comparison with 5- and 1-mm axial images

OBJECTIVE

To investigate the performance of a new CT software generating rib unfolded images for improved detection of rib osteolyses in patients with multiple myeloma.

MATERIALS AND METHODS

One hundred sixteen patients who underwent whole-body reduced-dose multidetector computed tomography (WBRD-MDCT) for multiple myeloma diagnosis and during follow-up were retrospectively evaluated. Nonenhanced CT scans with 5- and 1-mm slice thickness were interpreted by two readers with focus on detection of rib involvement (location, number, fracture). Image analysis of "unfolded," 1-mm-based CT rib images was subsequently undertaken. We classified the number of lytic bone lesions into 0, 1, 2, <5, <10 and ≥10. For all three data sets the reading time was registered.

RESULTS

An approximated sum of 6,727 myeloma-related rib lesions was found. On a patient-based analysis, CT (5 mm), CT (1 mm) and CT (1 mm "unfolded rib") yielded a sensitivity, specificity and accuracy of 79.7/94.7/87.1, 88.1/93/90.5 and 98.3/96.5/97.4, respectively. In a lesion-based analysis, the sensitivity, specificity and accuracy of the three evaluations were 69.7/87.2/70.5, 79.8/55.9/78 and 96.5/89.7/96.1. Mean reading time for 5 mm/1 mm axial images and unfolded images was 178.7/215.1/90.8 s, respectively.

CONCLUSION

The generation of "unfolded rib" images improves detection of rib involvement in patients with multiple myeloma and significantly reduces reading time.