Non-invasive diagnosis of focal liver lesions: an individualized approach

Deep learning HASTE sequence compared with T2-weighted BLADE sequence for liver MRI at 3 Tesla: a qualitative and quantitative prospective study

OBJECTIVES

To qualitatively and quantitatively compare a single breath-hold fast half-Fourier single-shot turbo spin echo sequence with deep learning reconstruction (DL HASTE) with T2-weighted BLADE sequence for liver MRI at 3 T.

METHODS

From December 2020 to January 2021, patients with liver MRI were prospectively included. For qualitative analysis, sequence quality, presence of artifacts, conspicuity, and presumed nature of the smallest lesion were assessed using the chi-squared and McNemar tests. For quantitative analysis, number of liver lesions, size of the smallest lesion, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in both sequences were assessed using the paired Wilcoxon signed-rank test. Intraclass correlation coefficients (ICCs) and kappa coefficients were used to assess agreement between the two readers.

RESULTS

One hundred and twelve patients were evaluated. Overall image quality (p = .006), artifacts (p < .001), and conspicuity of the smallest lesion (p = .001) were significantly better for the DL HASTE sequence than for the T2-weighted BLADE sequence. Significantly more liver lesions were detected with the DL HASTE sequence (356 lesions) than with the T2-weighted BLADE sequence (320 lesions; p < .001). CNR was significantly higher for the DL HASTE sequence (p < .001). SNR was higher for the T2-weighted BLADE sequence (p < .001). Interreader agreement was moderate to excellent depending on the sequence. Of the 41 supernumerary lesions visible only on the DL HASTE sequence, 38 (93%) were true-positives.

CONCLUSION

The DL HASTE sequence can be used to improve image quality and contrast and reduces artifacts, allowing the detection of more liver lesions than with the T2-weighted BLADE sequence.

CLINICAL RELEVANCE STATEMENT

The DL HASTE sequence is superior to the T2-weighted BLADE sequence for the detection of focal liver lesions and can be used in daily practice as a standard sequence.

KEY POINTS

• The half-Fourier acquisition single-shot turbo spin echo sequence with deep learning reconstruction (DL HASTE sequence) has better overall image quality, reduced artifacts (particularly motion artifacts), and improved contrast, allowing the detection of more liver lesions than with the T2-weighted BLADE sequence. • The acquisition time of the DL HASTE sequence is at least eight times faster (21 s) than that of the T2-weighted BLADE sequence (3-5 min). • The DL HASTE sequence could replace the conventional T2-weighted BLADE sequence to meet the growing indication for hepatic MRI in clinical practice, given its diagnostic and time-saving performance.

Convolutional neural networks versus radiologists in characterization of small hypoattenuating hepatic nodules on CT: a critical diagnostic challenge in staging of colorectal carcinoma

Our objective was to compare the diagnostic performance and diagnostic confidence of convolutional neural networks (CNN) to radiologists in characterizing small hypoattenuating hepatic nodules (SHHN) in colorectal carcinoma (CRC) on CT scans. Retrospective review of CRC CT scans over 6-years yielded 199 patients (550 SHHN) defined as < 1 cm in diameter. The reference standard was established through 1-year stability/MRI for benign or nodule evolution for malignant nodules. Five CNNs underwent supervised training on 150 patients (412 SHHN). The remaining 49 patients (138 SHHN) were used as testing-set to compare performance of 3 radiologists to CNN, measured through ROC AUC analysis of confidence rating assigned to each nodule by the radiologists. Multivariable modeling was used to compensate for radiologist bias from visible findings other than SHHN. In characterizing SHHN as benign or malignant, the radiologists’ mean AUC ROC (0.96) was significantly higher than CNN (0.84, p = 0.0004) but equivalent to CNN adjusted through multivariable modeling for presence of synchronous ≥ 1 cm liver metastases (0.95, p = 0.9). The diagnostic confidence of radiologists and CNN were analyzed. There were significantly lower number of nodules rated with low confidence by CNN (19.6%) and CNN with liver metastatic status (18.1%) than two (38.4%, 44.2%, p < 0.0001) but not a third radiologist (11.1%, p = 0.09). We conclude that in CRC, CNN in combination with liver metastatic status equaled expert radiologists in characterizing SHHN but with better diagnostic confidence.

Quantitative evaluation of Gd-EOB-DTPA uptake in focal liver lesions by using T1 mapping: differences between hepatocellular carcinoma, hepatic focal nodular hyperplasia and cavernous hemangioma

Objectives

To investigate the difference of T1 relaxation time on Gd-EOB-DTPA-enhanced MRI in hepatocellular carcinoma (HCC), hepatic focal nodular hyperplasia (FNH) and cavernous hemangioma of liver (CHL), and to quantitatively evaluate the uptake of Gd-EOB-DTPA in these three focal liver lesions (FLLs).

Results

The T1_P of CHL was significantly higher than those of HCC and FNH (P < 0.05). Reduction of T1 relaxation time on hepatobiliary phase could be observed in all three types of lesions. There were significant differences of T1_P, T1_E, T1_D and T1_D% between FNH, CHL and HCC (P < 0.001). Spearman correlation analysis revealed that T1_D% was the best indicator for diagnostic differentiation, with a correlation coefficient of 0.702. Discriminant analysis using three variables (T1_P, T1_E, and T1_D%) showed that the classification accuracy was 88.2%.

Materials and Methods

74 patients diagnosed with focal liver lesions underwent Gd-EOB-DTPA-enhanced MRI including T1 mapping were enrolled, consisting of 51 HCCs, 10 FNHs, and 13 CHLs. T1 relaxation times of these lesions were measured on pre-contrast (T1_P) and on hepatobiliary phase images at 20 minute after contrast (T1_E). The reduction of T1 relaxation time on hepatobiliary (T1_D) and the percentage reduction (T1_D%) was calculated. The differences of T1_P, T1_E, T1_D and T1_D% in these FLLs were analyzed. The usefulness of these parameters for classification of FLLs was evaluated.

Conclusions

Uptake of Gd-EOB-DTPA is different between in HCC, FNH and CHL. These three lesions can be distinguished using T1 mapping.